Video_Popularity.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Video_Popularity.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "W9J1gQ5yT7sM"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "viEPRzacZlIS",
        "outputId": "f57b4d09-9b0c-47f6-fb39-6e64c084ba5d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "cq5_uPaeZ0Yt",
        "outputId": "dfde5bd4-4660-4d6b-a325-25145844cd79"
      },
      "source": [
        "i = pd.read_csv('/content/gdrive/MyDrive/Video Popularity Prediction Challenge Dataset/Dataset/Image Data/train_image_df.csv')\n",
        "i"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comp_id</th>\n",
              "      <th>image_1</th>\n",
              "      <th>image_2</th>\n",
              "      <th>image_3</th>\n",
              "      <th>image_4</th>\n",
              "      <th>image_5</th>\n",
              "      <th>image_6</th>\n",
              "      <th>image_7</th>\n",
              "      <th>image_8</th>\n",
              "      <th>image_9</th>\n",
              "      <th>image_10</th>\n",
              "      <th>image_11</th>\n",
              "      <th>image_12</th>\n",
              "      <th>image_13</th>\n",
              "      <th>image_14</th>\n",
              "      <th>image_15</th>\n",
              "      <th>image_16</th>\n",
              "      <th>image_17</th>\n",
              "      <th>image_18</th>\n",
              "      <th>image_19</th>\n",
              "      <th>image_20</th>\n",
              "      <th>image_21</th>\n",
              "      <th>image_22</th>\n",
              "      <th>image_23</th>\n",
              "      <th>image_24</th>\n",
              "      <th>image_25</th>\n",
              "      <th>image_26</th>\n",
              "      <th>image_27</th>\n",
              "      <th>image_28</th>\n",
              "      <th>image_29</th>\n",
              "      <th>image_30</th>\n",
              "      <th>image_31</th>\n",
              "      <th>image_32</th>\n",
              "      <th>image_33</th>\n",
              "      <th>image_34</th>\n",
              "      <th>image_35</th>\n",
              "      <th>image_36</th>\n",
              "      <th>image_37</th>\n",
              "      <th>image_38</th>\n",
              "      <th>image_39</th>\n",
              "      <th>...</th>\n",
              "      <th>image_3961</th>\n",
              "      <th>image_3962</th>\n",
              "      <th>image_3963</th>\n",
              "      <th>image_3964</th>\n",
              "      <th>image_3965</th>\n",
              "      <th>image_3966</th>\n",
              "      <th>image_3967</th>\n",
              "      <th>image_3968</th>\n",
              "      <th>image_3969</th>\n",
              "      <th>image_3970</th>\n",
              "      <th>image_3971</th>\n",
              "      <th>image_3972</th>\n",
              "      <th>image_3973</th>\n",
              "      <th>image_3974</th>\n",
              "      <th>image_3975</th>\n",
              "      <th>image_3976</th>\n",
              "      <th>image_3977</th>\n",
              "      <th>image_3978</th>\n",
              "      <th>image_3979</th>\n",
              "      <th>image_3980</th>\n",
              "      <th>image_3981</th>\n",
              "      <th>image_3982</th>\n",
              "      <th>image_3983</th>\n",
              "      <th>image_3984</th>\n",
              "      <th>image_3985</th>\n",
              "      <th>image_3986</th>\n",
              "      <th>image_3987</th>\n",
              "      <th>image_3988</th>\n",
              "      <th>image_3989</th>\n",
              "      <th>image_3990</th>\n",
              "      <th>image_3991</th>\n",
              "      <th>image_3992</th>\n",
              "      <th>image_3993</th>\n",
              "      <th>image_3994</th>\n",
              "      <th>image_3995</th>\n",
              "      <th>image_3996</th>\n",
              "      <th>image_3997</th>\n",
              "      <th>image_3998</th>\n",
              "      <th>image_3999</th>\n",
              "      <th>image_4000</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.484456</td>\n",
              "      <td>0.036087</td>\n",
              "      <td>-0.149328</td>\n",
              "      <td>0.144475</td>\n",
              "      <td>-0.617386</td>\n",
              "      <td>0.035018</td>\n",
              "      <td>0.169174</td>\n",
              "      <td>-0.005733</td>\n",
              "      <td>-0.104908</td>\n",
              "      <td>-0.041200</td>\n",
              "      <td>0.780332</td>\n",
              "      <td>-0.088374</td>\n",
              "      <td>0.321899</td>\n",
              "      <td>-0.076341</td>\n",
              "      <td>-1.193367</td>\n",
              "      <td>0.076671</td>\n",
              "      <td>0.359643</td>\n",
              "      <td>-0.087548</td>\n",
              "      <td>-0.015789</td>\n",
              "      <td>0.201758</td>\n",
              "      <td>0.064355</td>\n",
              "      <td>-0.616845</td>\n",
              "      <td>0.046767</td>\n",
              "      <td>0.392215</td>\n",
              "      <td>-0.009920</td>\n",
              "      <td>0.049990</td>\n",
              "      <td>0.018078</td>\n",
              "      <td>0.198612</td>\n",
              "      <td>1.561772</td>\n",
              "      <td>0.004040</td>\n",
              "      <td>1.115828e-14</td>\n",
              "      <td>0.245002</td>\n",
              "      <td>1.398923</td>\n",
              "      <td>0.036252</td>\n",
              "      <td>-0.094207</td>\n",
              "      <td>-0.289255</td>\n",
              "      <td>-0.061230</td>\n",
              "      <td>-0.469053</td>\n",
              "      <td>0.016973</td>\n",
              "      <td>...</td>\n",
              "      <td>0.005193</td>\n",
              "      <td>-1.364837</td>\n",
              "      <td>0.006476</td>\n",
              "      <td>0.002824</td>\n",
              "      <td>-0.147828</td>\n",
              "      <td>-0.781976</td>\n",
              "      <td>0.396359</td>\n",
              "      <td>-3.690247</td>\n",
              "      <td>0.013893</td>\n",
              "      <td>-0.195668</td>\n",
              "      <td>0.206938</td>\n",
              "      <td>0.028772</td>\n",
              "      <td>1.522198</td>\n",
              "      <td>-0.606094</td>\n",
              "      <td>0.358221</td>\n",
              "      <td>0.020232</td>\n",
              "      <td>0.658431</td>\n",
              "      <td>-0.031049</td>\n",
              "      <td>0.026947</td>\n",
              "      <td>0.179981</td>\n",
              "      <td>-0.001695</td>\n",
              "      <td>-0.269223</td>\n",
              "      <td>0.070663</td>\n",
              "      <td>-0.604360</td>\n",
              "      <td>0.046013</td>\n",
              "      <td>-0.000350</td>\n",
              "      <td>0.171488</td>\n",
              "      <td>-0.067376</td>\n",
              "      <td>-0.052903</td>\n",
              "      <td>0.111028</td>\n",
              "      <td>-0.090408</td>\n",
              "      <td>-0.032915</td>\n",
              "      <td>-0.010857</td>\n",
              "      <td>-0.015954</td>\n",
              "      <td>0.123416</td>\n",
              "      <td>0.195833</td>\n",
              "      <td>-0.066449</td>\n",
              "      <td>0.115818</td>\n",
              "      <td>-0.005140</td>\n",
              "      <td>0.017278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.074533</td>\n",
              "      <td>-0.015655</td>\n",
              "      <td>-0.016286</td>\n",
              "      <td>-0.480964</td>\n",
              "      <td>0.687917</td>\n",
              "      <td>0.037131</td>\n",
              "      <td>-0.149725</td>\n",
              "      <td>-0.002098</td>\n",
              "      <td>0.099383</td>\n",
              "      <td>-0.021134</td>\n",
              "      <td>0.410953</td>\n",
              "      <td>0.029568</td>\n",
              "      <td>0.555797</td>\n",
              "      <td>0.210486</td>\n",
              "      <td>0.797866</td>\n",
              "      <td>-0.079695</td>\n",
              "      <td>-0.103046</td>\n",
              "      <td>0.008360</td>\n",
              "      <td>-0.006162</td>\n",
              "      <td>-0.820352</td>\n",
              "      <td>0.093285</td>\n",
              "      <td>-0.257368</td>\n",
              "      <td>-0.003837</td>\n",
              "      <td>-0.237525</td>\n",
              "      <td>-0.010784</td>\n",
              "      <td>-0.127445</td>\n",
              "      <td>0.003142</td>\n",
              "      <td>0.042099</td>\n",
              "      <td>0.092049</td>\n",
              "      <td>-0.015305</td>\n",
              "      <td>1.275889e-15</td>\n",
              "      <td>-0.293696</td>\n",
              "      <td>-0.173552</td>\n",
              "      <td>0.039536</td>\n",
              "      <td>-1.181006</td>\n",
              "      <td>-0.254040</td>\n",
              "      <td>0.065191</td>\n",
              "      <td>-0.769628</td>\n",
              "      <td>0.006467</td>\n",
              "      <td>...</td>\n",
              "      <td>0.034338</td>\n",
              "      <td>0.406546</td>\n",
              "      <td>-0.045887</td>\n",
              "      <td>0.008474</td>\n",
              "      <td>0.474869</td>\n",
              "      <td>-0.552040</td>\n",
              "      <td>-0.634184</td>\n",
              "      <td>0.225877</td>\n",
              "      <td>0.023105</td>\n",
              "      <td>-0.093336</td>\n",
              "      <td>0.163543</td>\n",
              "      <td>-0.101472</td>\n",
              "      <td>1.708743</td>\n",
              "      <td>-0.204869</td>\n",
              "      <td>0.513708</td>\n",
              "      <td>0.034999</td>\n",
              "      <td>-0.510086</td>\n",
              "      <td>0.032423</td>\n",
              "      <td>-0.018788</td>\n",
              "      <td>0.252818</td>\n",
              "      <td>-0.000703</td>\n",
              "      <td>-0.000187</td>\n",
              "      <td>0.083051</td>\n",
              "      <td>0.410483</td>\n",
              "      <td>-0.020481</td>\n",
              "      <td>0.020026</td>\n",
              "      <td>0.298024</td>\n",
              "      <td>0.554534</td>\n",
              "      <td>-0.054143</td>\n",
              "      <td>-0.102448</td>\n",
              "      <td>-0.625816</td>\n",
              "      <td>-0.009616</td>\n",
              "      <td>0.004558</td>\n",
              "      <td>0.008310</td>\n",
              "      <td>-0.173496</td>\n",
              "      <td>0.133518</td>\n",
              "      <td>-0.488898</td>\n",
              "      <td>0.084832</td>\n",
              "      <td>0.146566</td>\n",
              "      <td>0.007997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>-0.396809</td>\n",
              "      <td>0.021490</td>\n",
              "      <td>-1.723037</td>\n",
              "      <td>0.666147</td>\n",
              "      <td>-0.631924</td>\n",
              "      <td>0.047724</td>\n",
              "      <td>0.336041</td>\n",
              "      <td>-0.003904</td>\n",
              "      <td>0.039683</td>\n",
              "      <td>0.002628</td>\n",
              "      <td>-0.006978</td>\n",
              "      <td>-0.116380</td>\n",
              "      <td>0.048934</td>\n",
              "      <td>-0.235391</td>\n",
              "      <td>-1.849174</td>\n",
              "      <td>0.130089</td>\n",
              "      <td>-0.451063</td>\n",
              "      <td>0.065677</td>\n",
              "      <td>-0.055431</td>\n",
              "      <td>-0.669081</td>\n",
              "      <td>0.043518</td>\n",
              "      <td>-0.561830</td>\n",
              "      <td>-0.001670</td>\n",
              "      <td>0.824120</td>\n",
              "      <td>-0.022290</td>\n",
              "      <td>0.288842</td>\n",
              "      <td>0.005356</td>\n",
              "      <td>0.034002</td>\n",
              "      <td>-1.261856</td>\n",
              "      <td>-0.010268</td>\n",
              "      <td>1.328018e-14</td>\n",
              "      <td>0.486931</td>\n",
              "      <td>-0.056531</td>\n",
              "      <td>0.020430</td>\n",
              "      <td>0.187717</td>\n",
              "      <td>-0.384779</td>\n",
              "      <td>0.012771</td>\n",
              "      <td>-0.634231</td>\n",
              "      <td>-0.038821</td>\n",
              "      <td>...</td>\n",
              "      <td>0.014662</td>\n",
              "      <td>1.867043</td>\n",
              "      <td>-0.016193</td>\n",
              "      <td>-0.002900</td>\n",
              "      <td>-0.040511</td>\n",
              "      <td>1.650881</td>\n",
              "      <td>0.843204</td>\n",
              "      <td>2.086596</td>\n",
              "      <td>-0.005560</td>\n",
              "      <td>0.032179</td>\n",
              "      <td>-0.405258</td>\n",
              "      <td>0.036758</td>\n",
              "      <td>-1.253300</td>\n",
              "      <td>-0.229919</td>\n",
              "      <td>0.206761</td>\n",
              "      <td>0.071793</td>\n",
              "      <td>-0.377193</td>\n",
              "      <td>-0.047854</td>\n",
              "      <td>0.003419</td>\n",
              "      <td>-0.539637</td>\n",
              "      <td>-0.002529</td>\n",
              "      <td>0.007692</td>\n",
              "      <td>-0.077466</td>\n",
              "      <td>0.605521</td>\n",
              "      <td>0.213865</td>\n",
              "      <td>-0.010082</td>\n",
              "      <td>-0.050623</td>\n",
              "      <td>0.105038</td>\n",
              "      <td>0.144554</td>\n",
              "      <td>-0.384109</td>\n",
              "      <td>-0.078059</td>\n",
              "      <td>0.086320</td>\n",
              "      <td>-0.005606</td>\n",
              "      <td>0.002414</td>\n",
              "      <td>-0.164493</td>\n",
              "      <td>0.218473</td>\n",
              "      <td>0.151292</td>\n",
              "      <td>-0.076860</td>\n",
              "      <td>0.008321</td>\n",
              "      <td>0.012555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.995316</td>\n",
              "      <td>0.012766</td>\n",
              "      <td>0.387472</td>\n",
              "      <td>-0.684791</td>\n",
              "      <td>-0.209261</td>\n",
              "      <td>0.013654</td>\n",
              "      <td>0.138517</td>\n",
              "      <td>0.005586</td>\n",
              "      <td>0.079277</td>\n",
              "      <td>-0.004665</td>\n",
              "      <td>-0.356708</td>\n",
              "      <td>0.194370</td>\n",
              "      <td>0.147365</td>\n",
              "      <td>0.335734</td>\n",
              "      <td>0.807046</td>\n",
              "      <td>0.202777</td>\n",
              "      <td>0.111972</td>\n",
              "      <td>-0.002998</td>\n",
              "      <td>-0.047739</td>\n",
              "      <td>0.294054</td>\n",
              "      <td>-0.000040</td>\n",
              "      <td>0.169779</td>\n",
              "      <td>-0.043684</td>\n",
              "      <td>5.531464</td>\n",
              "      <td>-0.021486</td>\n",
              "      <td>0.550411</td>\n",
              "      <td>-0.002595</td>\n",
              "      <td>-0.151196</td>\n",
              "      <td>-0.647365</td>\n",
              "      <td>-0.016672</td>\n",
              "      <td>3.251479e-14</td>\n",
              "      <td>0.082928</td>\n",
              "      <td>-0.379089</td>\n",
              "      <td>-0.084236</td>\n",
              "      <td>-0.895890</td>\n",
              "      <td>0.385392</td>\n",
              "      <td>-0.028787</td>\n",
              "      <td>0.101765</td>\n",
              "      <td>0.021869</td>\n",
              "      <td>...</td>\n",
              "      <td>0.005221</td>\n",
              "      <td>-1.364425</td>\n",
              "      <td>0.007607</td>\n",
              "      <td>0.003765</td>\n",
              "      <td>0.072319</td>\n",
              "      <td>-0.414214</td>\n",
              "      <td>0.035882</td>\n",
              "      <td>-2.780271</td>\n",
              "      <td>-0.018474</td>\n",
              "      <td>0.099804</td>\n",
              "      <td>-0.217108</td>\n",
              "      <td>0.090902</td>\n",
              "      <td>-0.199446</td>\n",
              "      <td>-0.387944</td>\n",
              "      <td>0.142761</td>\n",
              "      <td>0.043976</td>\n",
              "      <td>1.334685</td>\n",
              "      <td>-0.003364</td>\n",
              "      <td>0.000313</td>\n",
              "      <td>0.206105</td>\n",
              "      <td>0.000606</td>\n",
              "      <td>-0.200762</td>\n",
              "      <td>0.049662</td>\n",
              "      <td>0.869679</td>\n",
              "      <td>-0.049231</td>\n",
              "      <td>-0.022817</td>\n",
              "      <td>0.392719</td>\n",
              "      <td>0.330957</td>\n",
              "      <td>-0.051254</td>\n",
              "      <td>0.302376</td>\n",
              "      <td>-0.190714</td>\n",
              "      <td>-0.005452</td>\n",
              "      <td>-0.003889</td>\n",
              "      <td>0.013430</td>\n",
              "      <td>0.344406</td>\n",
              "      <td>0.038220</td>\n",
              "      <td>0.210446</td>\n",
              "      <td>-0.145128</td>\n",
              "      <td>-0.019172</td>\n",
              "      <td>0.000532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>-0.611648</td>\n",
              "      <td>0.000860</td>\n",
              "      <td>-0.572393</td>\n",
              "      <td>0.894287</td>\n",
              "      <td>-0.191228</td>\n",
              "      <td>-0.111583</td>\n",
              "      <td>-0.011111</td>\n",
              "      <td>0.003231</td>\n",
              "      <td>-0.160582</td>\n",
              "      <td>0.032804</td>\n",
              "      <td>-0.260793</td>\n",
              "      <td>0.071633</td>\n",
              "      <td>-0.488627</td>\n",
              "      <td>0.050279</td>\n",
              "      <td>-0.219198</td>\n",
              "      <td>-0.176530</td>\n",
              "      <td>1.063709</td>\n",
              "      <td>-0.068684</td>\n",
              "      <td>0.023713</td>\n",
              "      <td>0.717563</td>\n",
              "      <td>-0.104735</td>\n",
              "      <td>0.428180</td>\n",
              "      <td>-0.016618</td>\n",
              "      <td>1.603678</td>\n",
              "      <td>0.024298</td>\n",
              "      <td>-0.398335</td>\n",
              "      <td>-0.007962</td>\n",
              "      <td>-0.046635</td>\n",
              "      <td>0.292458</td>\n",
              "      <td>-0.006884</td>\n",
              "      <td>-1.487309e-15</td>\n",
              "      <td>-0.232607</td>\n",
              "      <td>0.996249</td>\n",
              "      <td>-0.110129</td>\n",
              "      <td>0.201924</td>\n",
              "      <td>0.306438</td>\n",
              "      <td>-0.068470</td>\n",
              "      <td>0.843059</td>\n",
              "      <td>-0.003373</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.011783</td>\n",
              "      <td>0.282487</td>\n",
              "      <td>0.024050</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>-0.117799</td>\n",
              "      <td>-0.020776</td>\n",
              "      <td>0.335877</td>\n",
              "      <td>2.269820</td>\n",
              "      <td>0.012039</td>\n",
              "      <td>0.087193</td>\n",
              "      <td>-0.129967</td>\n",
              "      <td>-0.055533</td>\n",
              "      <td>0.469091</td>\n",
              "      <td>1.325833</td>\n",
              "      <td>-0.552737</td>\n",
              "      <td>0.064178</td>\n",
              "      <td>-0.016011</td>\n",
              "      <td>-0.038678</td>\n",
              "      <td>-0.005711</td>\n",
              "      <td>-0.292988</td>\n",
              "      <td>-0.000254</td>\n",
              "      <td>-0.043102</td>\n",
              "      <td>-0.022684</td>\n",
              "      <td>0.467325</td>\n",
              "      <td>0.133324</td>\n",
              "      <td>0.006705</td>\n",
              "      <td>0.199420</td>\n",
              "      <td>0.023973</td>\n",
              "      <td>-0.075215</td>\n",
              "      <td>-0.171552</td>\n",
              "      <td>-0.040437</td>\n",
              "      <td>-0.142134</td>\n",
              "      <td>-0.022771</td>\n",
              "      <td>-0.013134</td>\n",
              "      <td>-0.024869</td>\n",
              "      <td>0.125129</td>\n",
              "      <td>0.215967</td>\n",
              "      <td>0.005288</td>\n",
              "      <td>0.145715</td>\n",
              "      <td>0.001425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>2996</td>\n",
              "      <td>-0.845222</td>\n",
              "      <td>-0.039495</td>\n",
              "      <td>1.234590</td>\n",
              "      <td>-0.165663</td>\n",
              "      <td>0.022385</td>\n",
              "      <td>-0.081482</td>\n",
              "      <td>0.151295</td>\n",
              "      <td>0.003960</td>\n",
              "      <td>0.182052</td>\n",
              "      <td>-0.043686</td>\n",
              "      <td>-0.128087</td>\n",
              "      <td>-0.051021</td>\n",
              "      <td>0.115560</td>\n",
              "      <td>-0.350518</td>\n",
              "      <td>-0.279702</td>\n",
              "      <td>0.213333</td>\n",
              "      <td>0.090187</td>\n",
              "      <td>-0.195367</td>\n",
              "      <td>-0.034255</td>\n",
              "      <td>0.299249</td>\n",
              "      <td>-0.014898</td>\n",
              "      <td>0.298953</td>\n",
              "      <td>0.007382</td>\n",
              "      <td>-4.218973</td>\n",
              "      <td>0.031324</td>\n",
              "      <td>0.358478</td>\n",
              "      <td>0.011585</td>\n",
              "      <td>0.130488</td>\n",
              "      <td>-0.479845</td>\n",
              "      <td>-0.058140</td>\n",
              "      <td>-4.073131e-15</td>\n",
              "      <td>-0.098992</td>\n",
              "      <td>0.486058</td>\n",
              "      <td>0.090468</td>\n",
              "      <td>-0.026605</td>\n",
              "      <td>0.019980</td>\n",
              "      <td>0.011986</td>\n",
              "      <td>0.746015</td>\n",
              "      <td>0.012652</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.058183</td>\n",
              "      <td>-1.679945</td>\n",
              "      <td>0.112679</td>\n",
              "      <td>0.033398</td>\n",
              "      <td>0.010144</td>\n",
              "      <td>-0.732051</td>\n",
              "      <td>0.296837</td>\n",
              "      <td>0.128294</td>\n",
              "      <td>-0.027903</td>\n",
              "      <td>0.281148</td>\n",
              "      <td>0.052526</td>\n",
              "      <td>0.161844</td>\n",
              "      <td>0.479694</td>\n",
              "      <td>-0.209886</td>\n",
              "      <td>-0.288199</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.645677</td>\n",
              "      <td>0.093292</td>\n",
              "      <td>-0.008920</td>\n",
              "      <td>-0.013585</td>\n",
              "      <td>-0.002132</td>\n",
              "      <td>-0.044504</td>\n",
              "      <td>-0.043467</td>\n",
              "      <td>0.092779</td>\n",
              "      <td>-0.072572</td>\n",
              "      <td>-0.072826</td>\n",
              "      <td>-0.224705</td>\n",
              "      <td>0.223074</td>\n",
              "      <td>0.020553</td>\n",
              "      <td>0.648901</td>\n",
              "      <td>-0.489864</td>\n",
              "      <td>0.015473</td>\n",
              "      <td>-0.036980</td>\n",
              "      <td>0.009553</td>\n",
              "      <td>0.300495</td>\n",
              "      <td>-0.036730</td>\n",
              "      <td>0.020080</td>\n",
              "      <td>-0.022211</td>\n",
              "      <td>0.078571</td>\n",
              "      <td>-0.046191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>2997</td>\n",
              "      <td>-0.074436</td>\n",
              "      <td>-0.029679</td>\n",
              "      <td>-0.106747</td>\n",
              "      <td>0.405812</td>\n",
              "      <td>0.256208</td>\n",
              "      <td>0.032415</td>\n",
              "      <td>-0.191955</td>\n",
              "      <td>-0.000964</td>\n",
              "      <td>0.004871</td>\n",
              "      <td>0.012094</td>\n",
              "      <td>-0.271741</td>\n",
              "      <td>0.103258</td>\n",
              "      <td>-0.200379</td>\n",
              "      <td>-0.043331</td>\n",
              "      <td>-0.944118</td>\n",
              "      <td>0.145624</td>\n",
              "      <td>0.955468</td>\n",
              "      <td>0.119958</td>\n",
              "      <td>0.021819</td>\n",
              "      <td>0.298616</td>\n",
              "      <td>-0.060894</td>\n",
              "      <td>-0.546009</td>\n",
              "      <td>-0.002761</td>\n",
              "      <td>-1.036104</td>\n",
              "      <td>-0.031389</td>\n",
              "      <td>0.941634</td>\n",
              "      <td>0.003284</td>\n",
              "      <td>0.018933</td>\n",
              "      <td>-0.021833</td>\n",
              "      <td>0.003802</td>\n",
              "      <td>-5.102689e-15</td>\n",
              "      <td>0.153877</td>\n",
              "      <td>-0.428967</td>\n",
              "      <td>0.099288</td>\n",
              "      <td>0.166323</td>\n",
              "      <td>0.074894</td>\n",
              "      <td>-0.023813</td>\n",
              "      <td>0.254498</td>\n",
              "      <td>0.014518</td>\n",
              "      <td>...</td>\n",
              "      <td>0.003702</td>\n",
              "      <td>-1.056970</td>\n",
              "      <td>-0.042367</td>\n",
              "      <td>-0.005655</td>\n",
              "      <td>0.045119</td>\n",
              "      <td>1.392215</td>\n",
              "      <td>-0.196389</td>\n",
              "      <td>3.658677</td>\n",
              "      <td>0.024185</td>\n",
              "      <td>-0.022014</td>\n",
              "      <td>0.152942</td>\n",
              "      <td>-0.018599</td>\n",
              "      <td>0.731627</td>\n",
              "      <td>0.200391</td>\n",
              "      <td>-0.318611</td>\n",
              "      <td>0.109060</td>\n",
              "      <td>-0.668773</td>\n",
              "      <td>-0.023097</td>\n",
              "      <td>0.017713</td>\n",
              "      <td>0.115054</td>\n",
              "      <td>-0.002060</td>\n",
              "      <td>0.009916</td>\n",
              "      <td>-0.017283</td>\n",
              "      <td>0.523131</td>\n",
              "      <td>-0.048915</td>\n",
              "      <td>-0.022831</td>\n",
              "      <td>0.212646</td>\n",
              "      <td>0.200232</td>\n",
              "      <td>0.064540</td>\n",
              "      <td>0.506904</td>\n",
              "      <td>0.225417</td>\n",
              "      <td>-0.137557</td>\n",
              "      <td>0.011404</td>\n",
              "      <td>-0.020122</td>\n",
              "      <td>-0.381863</td>\n",
              "      <td>-0.244808</td>\n",
              "      <td>0.076713</td>\n",
              "      <td>0.062716</td>\n",
              "      <td>0.016840</td>\n",
              "      <td>-0.002579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>2998</td>\n",
              "      <td>0.368068</td>\n",
              "      <td>-0.044313</td>\n",
              "      <td>0.446860</td>\n",
              "      <td>-0.062968</td>\n",
              "      <td>0.949046</td>\n",
              "      <td>0.023048</td>\n",
              "      <td>0.319711</td>\n",
              "      <td>0.001382</td>\n",
              "      <td>0.255822</td>\n",
              "      <td>-0.009101</td>\n",
              "      <td>0.478251</td>\n",
              "      <td>-0.151759</td>\n",
              "      <td>0.274600</td>\n",
              "      <td>-0.380956</td>\n",
              "      <td>-0.164624</td>\n",
              "      <td>0.030570</td>\n",
              "      <td>0.088489</td>\n",
              "      <td>0.415399</td>\n",
              "      <td>-0.013734</td>\n",
              "      <td>-0.819054</td>\n",
              "      <td>-0.057931</td>\n",
              "      <td>0.307386</td>\n",
              "      <td>-0.038038</td>\n",
              "      <td>1.561042</td>\n",
              "      <td>-0.038095</td>\n",
              "      <td>0.565684</td>\n",
              "      <td>-0.000529</td>\n",
              "      <td>-0.072753</td>\n",
              "      <td>-0.393351</td>\n",
              "      <td>0.003410</td>\n",
              "      <td>-3.667205e-15</td>\n",
              "      <td>-0.708979</td>\n",
              "      <td>0.454766</td>\n",
              "      <td>0.013286</td>\n",
              "      <td>0.000566</td>\n",
              "      <td>0.250795</td>\n",
              "      <td>-0.000049</td>\n",
              "      <td>0.611105</td>\n",
              "      <td>-0.004123</td>\n",
              "      <td>...</td>\n",
              "      <td>0.001830</td>\n",
              "      <td>1.128962</td>\n",
              "      <td>0.014436</td>\n",
              "      <td>0.001823</td>\n",
              "      <td>0.229569</td>\n",
              "      <td>-0.612397</td>\n",
              "      <td>-0.161279</td>\n",
              "      <td>-0.543701</td>\n",
              "      <td>-0.011971</td>\n",
              "      <td>-0.466704</td>\n",
              "      <td>-0.267731</td>\n",
              "      <td>-0.018536</td>\n",
              "      <td>-0.133814</td>\n",
              "      <td>0.300618</td>\n",
              "      <td>-0.325687</td>\n",
              "      <td>-0.002275</td>\n",
              "      <td>0.354799</td>\n",
              "      <td>0.000333</td>\n",
              "      <td>-0.006113</td>\n",
              "      <td>-0.115990</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>-0.205581</td>\n",
              "      <td>-0.022980</td>\n",
              "      <td>0.619348</td>\n",
              "      <td>-0.054469</td>\n",
              "      <td>0.010572</td>\n",
              "      <td>0.689479</td>\n",
              "      <td>0.232870</td>\n",
              "      <td>-0.051912</td>\n",
              "      <td>0.162389</td>\n",
              "      <td>-0.171302</td>\n",
              "      <td>-0.044187</td>\n",
              "      <td>0.000784</td>\n",
              "      <td>-0.005235</td>\n",
              "      <td>0.524747</td>\n",
              "      <td>-0.132455</td>\n",
              "      <td>-0.102490</td>\n",
              "      <td>0.003506</td>\n",
              "      <td>0.106999</td>\n",
              "      <td>0.001011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>2999</td>\n",
              "      <td>0.603725</td>\n",
              "      <td>-0.022034</td>\n",
              "      <td>1.014714</td>\n",
              "      <td>0.523509</td>\n",
              "      <td>-0.079899</td>\n",
              "      <td>0.099595</td>\n",
              "      <td>0.034876</td>\n",
              "      <td>0.005071</td>\n",
              "      <td>0.029159</td>\n",
              "      <td>0.026838</td>\n",
              "      <td>0.675021</td>\n",
              "      <td>0.018827</td>\n",
              "      <td>0.397086</td>\n",
              "      <td>0.378712</td>\n",
              "      <td>-0.038888</td>\n",
              "      <td>0.190919</td>\n",
              "      <td>-0.159130</td>\n",
              "      <td>0.111545</td>\n",
              "      <td>0.064711</td>\n",
              "      <td>-0.171846</td>\n",
              "      <td>-0.079357</td>\n",
              "      <td>-0.200023</td>\n",
              "      <td>-0.007816</td>\n",
              "      <td>3.229934</td>\n",
              "      <td>0.025180</td>\n",
              "      <td>1.193180</td>\n",
              "      <td>0.006620</td>\n",
              "      <td>-0.192889</td>\n",
              "      <td>0.044007</td>\n",
              "      <td>0.006521</td>\n",
              "      <td>-7.127111e-15</td>\n",
              "      <td>0.079879</td>\n",
              "      <td>0.562057</td>\n",
              "      <td>-0.072395</td>\n",
              "      <td>-0.562927</td>\n",
              "      <td>0.446241</td>\n",
              "      <td>0.093546</td>\n",
              "      <td>-0.202908</td>\n",
              "      <td>0.006790</td>\n",
              "      <td>...</td>\n",
              "      <td>0.033570</td>\n",
              "      <td>1.094231</td>\n",
              "      <td>-0.048529</td>\n",
              "      <td>-0.005877</td>\n",
              "      <td>-0.080007</td>\n",
              "      <td>-1.261125</td>\n",
              "      <td>0.123454</td>\n",
              "      <td>0.172464</td>\n",
              "      <td>-0.014182</td>\n",
              "      <td>0.466051</td>\n",
              "      <td>-0.511859</td>\n",
              "      <td>-0.066729</td>\n",
              "      <td>0.975590</td>\n",
              "      <td>-0.800716</td>\n",
              "      <td>0.235803</td>\n",
              "      <td>-0.033175</td>\n",
              "      <td>-1.022476</td>\n",
              "      <td>-0.015881</td>\n",
              "      <td>-0.020990</td>\n",
              "      <td>0.396978</td>\n",
              "      <td>0.000773</td>\n",
              "      <td>0.169285</td>\n",
              "      <td>-0.177596</td>\n",
              "      <td>-0.680885</td>\n",
              "      <td>-0.069847</td>\n",
              "      <td>-0.024044</td>\n",
              "      <td>0.037277</td>\n",
              "      <td>-0.094497</td>\n",
              "      <td>-0.062506</td>\n",
              "      <td>0.149097</td>\n",
              "      <td>0.031655</td>\n",
              "      <td>-0.040731</td>\n",
              "      <td>-0.019667</td>\n",
              "      <td>-0.015320</td>\n",
              "      <td>0.087113</td>\n",
              "      <td>0.107709</td>\n",
              "      <td>0.219753</td>\n",
              "      <td>-0.065209</td>\n",
              "      <td>0.211636</td>\n",
              "      <td>-0.001608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>3000</td>\n",
              "      <td>-0.705101</td>\n",
              "      <td>-0.004345</td>\n",
              "      <td>-0.144680</td>\n",
              "      <td>-0.020045</td>\n",
              "      <td>-0.334467</td>\n",
              "      <td>-0.050625</td>\n",
              "      <td>-0.104376</td>\n",
              "      <td>-0.003019</td>\n",
              "      <td>-0.231594</td>\n",
              "      <td>0.038694</td>\n",
              "      <td>0.233181</td>\n",
              "      <td>0.031905</td>\n",
              "      <td>-0.514952</td>\n",
              "      <td>0.004125</td>\n",
              "      <td>-0.196398</td>\n",
              "      <td>0.057503</td>\n",
              "      <td>0.101820</td>\n",
              "      <td>-0.291888</td>\n",
              "      <td>0.054797</td>\n",
              "      <td>-0.214427</td>\n",
              "      <td>0.044645</td>\n",
              "      <td>0.132117</td>\n",
              "      <td>0.012129</td>\n",
              "      <td>0.078686</td>\n",
              "      <td>-0.037884</td>\n",
              "      <td>1.038326</td>\n",
              "      <td>-0.008462</td>\n",
              "      <td>-0.119505</td>\n",
              "      <td>-0.154638</td>\n",
              "      <td>0.011236</td>\n",
              "      <td>6.464013e-15</td>\n",
              "      <td>0.017684</td>\n",
              "      <td>-1.192970</td>\n",
              "      <td>-0.092096</td>\n",
              "      <td>0.196868</td>\n",
              "      <td>0.055865</td>\n",
              "      <td>0.087607</td>\n",
              "      <td>-0.524803</td>\n",
              "      <td>-0.028800</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.038818</td>\n",
              "      <td>-0.166156</td>\n",
              "      <td>-0.005093</td>\n",
              "      <td>0.027954</td>\n",
              "      <td>0.010269</td>\n",
              "      <td>1.470634</td>\n",
              "      <td>-0.276868</td>\n",
              "      <td>-0.664932</td>\n",
              "      <td>-0.023986</td>\n",
              "      <td>-0.225111</td>\n",
              "      <td>0.379923</td>\n",
              "      <td>0.052665</td>\n",
              "      <td>1.846554</td>\n",
              "      <td>-0.557809</td>\n",
              "      <td>0.115415</td>\n",
              "      <td>-0.003655</td>\n",
              "      <td>-0.443031</td>\n",
              "      <td>-0.039606</td>\n",
              "      <td>-0.021902</td>\n",
              "      <td>0.297873</td>\n",
              "      <td>0.002228</td>\n",
              "      <td>0.014337</td>\n",
              "      <td>-0.187175</td>\n",
              "      <td>-0.258987</td>\n",
              "      <td>-0.046179</td>\n",
              "      <td>0.050176</td>\n",
              "      <td>-0.066890</td>\n",
              "      <td>-0.158271</td>\n",
              "      <td>-0.139221</td>\n",
              "      <td>-0.768913</td>\n",
              "      <td>0.167214</td>\n",
              "      <td>0.230765</td>\n",
              "      <td>0.014820</td>\n",
              "      <td>0.014386</td>\n",
              "      <td>-0.220913</td>\n",
              "      <td>0.155001</td>\n",
              "      <td>0.149516</td>\n",
              "      <td>-0.118682</td>\n",
              "      <td>0.024679</td>\n",
              "      <td>-0.009011</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 4001 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      comp_id   image_1   image_2  ...  image_3998  image_3999  image_4000\n",
              "0           1  0.484456  0.036087  ...    0.115818   -0.005140    0.017278\n",
              "1           2  0.074533 -0.015655  ...    0.084832    0.146566    0.007997\n",
              "2           3 -0.396809  0.021490  ...   -0.076860    0.008321    0.012555\n",
              "3           4  0.995316  0.012766  ...   -0.145128   -0.019172    0.000532\n",
              "4           5 -0.611648  0.000860  ...    0.005288    0.145715    0.001425\n",
              "...       ...       ...       ...  ...         ...         ...         ...\n",
              "2995     2996 -0.845222 -0.039495  ...   -0.022211    0.078571   -0.046191\n",
              "2996     2997 -0.074436 -0.029679  ...    0.062716    0.016840   -0.002579\n",
              "2997     2998  0.368068 -0.044313  ...    0.003506    0.106999    0.001011\n",
              "2998     2999  0.603725 -0.022034  ...   -0.065209    0.211636   -0.001608\n",
              "2999     3000 -0.705101 -0.004345  ...   -0.118682    0.024679   -0.009011\n",
              "\n",
              "[3000 rows x 4001 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "3VK972ATaqAq",
        "outputId": "ff90a18c-cc6f-4f05-d390-f8b1c9aa40f4"
      },
      "source": [
        "t = pd.read_csv('/content/gdrive/MyDrive/Video Popularity Prediction Challenge Dataset/Dataset/Title Data/train_title_df.csv')\n",
        "t"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comp_id</th>\n",
              "      <th>title_1</th>\n",
              "      <th>title_2</th>\n",
              "      <th>title_3</th>\n",
              "      <th>title_4</th>\n",
              "      <th>title_5</th>\n",
              "      <th>title_6</th>\n",
              "      <th>title_7</th>\n",
              "      <th>title_8</th>\n",
              "      <th>title_9</th>\n",
              "      <th>title_10</th>\n",
              "      <th>title_11</th>\n",
              "      <th>title_12</th>\n",
              "      <th>title_13</th>\n",
              "      <th>title_14</th>\n",
              "      <th>title_15</th>\n",
              "      <th>title_16</th>\n",
              "      <th>title_17</th>\n",
              "      <th>title_18</th>\n",
              "      <th>title_19</th>\n",
              "      <th>title_20</th>\n",
              "      <th>title_21</th>\n",
              "      <th>title_22</th>\n",
              "      <th>title_23</th>\n",
              "      <th>title_24</th>\n",
              "      <th>title_25</th>\n",
              "      <th>title_26</th>\n",
              "      <th>title_27</th>\n",
              "      <th>title_28</th>\n",
              "      <th>title_29</th>\n",
              "      <th>title_30</th>\n",
              "      <th>title_31</th>\n",
              "      <th>title_32</th>\n",
              "      <th>title_33</th>\n",
              "      <th>title_34</th>\n",
              "      <th>title_35</th>\n",
              "      <th>title_36</th>\n",
              "      <th>title_37</th>\n",
              "      <th>title_38</th>\n",
              "      <th>title_39</th>\n",
              "      <th>title_40</th>\n",
              "      <th>title_41</th>\n",
              "      <th>title_42</th>\n",
              "      <th>title_43</th>\n",
              "      <th>title_44</th>\n",
              "      <th>title_45</th>\n",
              "      <th>title_46</th>\n",
              "      <th>title_47</th>\n",
              "      <th>title_48</th>\n",
              "      <th>title_49</th>\n",
              "      <th>title_50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>-0.977637</td>\n",
              "      <td>-0.543310</td>\n",
              "      <td>0.079403</td>\n",
              "      <td>0.205560</td>\n",
              "      <td>-1.497104</td>\n",
              "      <td>0.230466</td>\n",
              "      <td>0.566112</td>\n",
              "      <td>-0.662264</td>\n",
              "      <td>0.130342</td>\n",
              "      <td>-0.277137</td>\n",
              "      <td>0.574511</td>\n",
              "      <td>0.323026</td>\n",
              "      <td>0.664858</td>\n",
              "      <td>0.451264</td>\n",
              "      <td>-0.622091</td>\n",
              "      <td>0.735123</td>\n",
              "      <td>0.198388</td>\n",
              "      <td>0.053159</td>\n",
              "      <td>0.495447</td>\n",
              "      <td>-0.083371</td>\n",
              "      <td>-0.936445</td>\n",
              "      <td>0.704932</td>\n",
              "      <td>0.546585</td>\n",
              "      <td>0.313998</td>\n",
              "      <td>0.705555</td>\n",
              "      <td>1.386698</td>\n",
              "      <td>-0.164988</td>\n",
              "      <td>-0.762484</td>\n",
              "      <td>0.186666</td>\n",
              "      <td>-0.304057</td>\n",
              "      <td>0.991528</td>\n",
              "      <td>0.034073</td>\n",
              "      <td>0.287462</td>\n",
              "      <td>-0.905057</td>\n",
              "      <td>-0.196644</td>\n",
              "      <td>0.303087</td>\n",
              "      <td>0.697851</td>\n",
              "      <td>0.698429</td>\n",
              "      <td>0.113108</td>\n",
              "      <td>0.574730</td>\n",
              "      <td>0.059434</td>\n",
              "      <td>-0.272340</td>\n",
              "      <td>-0.030180</td>\n",
              "      <td>-0.109040</td>\n",
              "      <td>-0.033425</td>\n",
              "      <td>0.728739</td>\n",
              "      <td>0.142030</td>\n",
              "      <td>0.397541</td>\n",
              "      <td>1.270722</td>\n",
              "      <td>-0.816523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.041873</td>\n",
              "      <td>0.644655</td>\n",
              "      <td>0.140869</td>\n",
              "      <td>-0.664714</td>\n",
              "      <td>-0.062992</td>\n",
              "      <td>0.240086</td>\n",
              "      <td>-1.017593</td>\n",
              "      <td>1.019706</td>\n",
              "      <td>0.340133</td>\n",
              "      <td>0.617302</td>\n",
              "      <td>1.085504</td>\n",
              "      <td>0.897807</td>\n",
              "      <td>-0.578818</td>\n",
              "      <td>-0.970466</td>\n",
              "      <td>0.508686</td>\n",
              "      <td>0.155261</td>\n",
              "      <td>-0.915965</td>\n",
              "      <td>-0.068254</td>\n",
              "      <td>0.087122</td>\n",
              "      <td>0.125092</td>\n",
              "      <td>-0.313533</td>\n",
              "      <td>0.220125</td>\n",
              "      <td>-0.358611</td>\n",
              "      <td>1.067428</td>\n",
              "      <td>-0.111773</td>\n",
              "      <td>-1.048396</td>\n",
              "      <td>1.069839</td>\n",
              "      <td>0.254970</td>\n",
              "      <td>-0.254688</td>\n",
              "      <td>-0.670338</td>\n",
              "      <td>0.236818</td>\n",
              "      <td>-0.586383</td>\n",
              "      <td>-0.340639</td>\n",
              "      <td>0.491550</td>\n",
              "      <td>0.739604</td>\n",
              "      <td>0.380507</td>\n",
              "      <td>0.707798</td>\n",
              "      <td>-0.176794</td>\n",
              "      <td>0.230293</td>\n",
              "      <td>0.784910</td>\n",
              "      <td>0.242961</td>\n",
              "      <td>-0.404538</td>\n",
              "      <td>0.335224</td>\n",
              "      <td>-0.158068</td>\n",
              "      <td>-0.178225</td>\n",
              "      <td>0.351125</td>\n",
              "      <td>0.959121</td>\n",
              "      <td>0.673848</td>\n",
              "      <td>-0.530106</td>\n",
              "      <td>0.133466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>-0.905595</td>\n",
              "      <td>0.097928</td>\n",
              "      <td>0.111981</td>\n",
              "      <td>-0.064753</td>\n",
              "      <td>-1.566516</td>\n",
              "      <td>0.140807</td>\n",
              "      <td>-0.946588</td>\n",
              "      <td>-0.342442</td>\n",
              "      <td>0.031226</td>\n",
              "      <td>-0.660981</td>\n",
              "      <td>0.611014</td>\n",
              "      <td>0.092803</td>\n",
              "      <td>0.201623</td>\n",
              "      <td>-0.078797</td>\n",
              "      <td>-0.103042</td>\n",
              "      <td>0.429722</td>\n",
              "      <td>-0.627137</td>\n",
              "      <td>0.031862</td>\n",
              "      <td>0.094095</td>\n",
              "      <td>0.433442</td>\n",
              "      <td>0.651887</td>\n",
              "      <td>0.129370</td>\n",
              "      <td>0.393513</td>\n",
              "      <td>-0.199944</td>\n",
              "      <td>0.223804</td>\n",
              "      <td>-1.170394</td>\n",
              "      <td>0.424186</td>\n",
              "      <td>0.615719</td>\n",
              "      <td>0.130349</td>\n",
              "      <td>0.510412</td>\n",
              "      <td>-0.251082</td>\n",
              "      <td>-0.073207</td>\n",
              "      <td>0.177124</td>\n",
              "      <td>0.271520</td>\n",
              "      <td>1.079512</td>\n",
              "      <td>0.715520</td>\n",
              "      <td>0.490704</td>\n",
              "      <td>-0.357736</td>\n",
              "      <td>0.644506</td>\n",
              "      <td>-0.183714</td>\n",
              "      <td>0.414191</td>\n",
              "      <td>-0.457968</td>\n",
              "      <td>0.157185</td>\n",
              "      <td>-0.116985</td>\n",
              "      <td>0.404937</td>\n",
              "      <td>-0.220503</td>\n",
              "      <td>0.443171</td>\n",
              "      <td>0.643445</td>\n",
              "      <td>0.357957</td>\n",
              "      <td>0.525154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.069220</td>\n",
              "      <td>0.411544</td>\n",
              "      <td>0.177700</td>\n",
              "      <td>-0.739998</td>\n",
              "      <td>-0.932620</td>\n",
              "      <td>-0.717982</td>\n",
              "      <td>-0.406487</td>\n",
              "      <td>-0.267096</td>\n",
              "      <td>0.114066</td>\n",
              "      <td>-0.045532</td>\n",
              "      <td>1.079282</td>\n",
              "      <td>0.548952</td>\n",
              "      <td>0.194681</td>\n",
              "      <td>-0.212802</td>\n",
              "      <td>0.143384</td>\n",
              "      <td>0.398281</td>\n",
              "      <td>-0.644566</td>\n",
              "      <td>-0.199902</td>\n",
              "      <td>0.070513</td>\n",
              "      <td>-0.219756</td>\n",
              "      <td>-0.541676</td>\n",
              "      <td>-0.579037</td>\n",
              "      <td>-0.178361</td>\n",
              "      <td>0.606353</td>\n",
              "      <td>-0.104996</td>\n",
              "      <td>-0.756004</td>\n",
              "      <td>0.928947</td>\n",
              "      <td>-0.425516</td>\n",
              "      <td>-0.025482</td>\n",
              "      <td>0.064210</td>\n",
              "      <td>-0.053877</td>\n",
              "      <td>-0.802950</td>\n",
              "      <td>-0.176944</td>\n",
              "      <td>0.380462</td>\n",
              "      <td>1.195464</td>\n",
              "      <td>1.100969</td>\n",
              "      <td>-0.713316</td>\n",
              "      <td>1.058688</td>\n",
              "      <td>0.211992</td>\n",
              "      <td>0.430793</td>\n",
              "      <td>0.211731</td>\n",
              "      <td>-0.812590</td>\n",
              "      <td>0.258014</td>\n",
              "      <td>-0.180143</td>\n",
              "      <td>-0.564691</td>\n",
              "      <td>-0.515378</td>\n",
              "      <td>0.613686</td>\n",
              "      <td>0.817547</td>\n",
              "      <td>0.325042</td>\n",
              "      <td>0.254415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.503560</td>\n",
              "      <td>-0.210970</td>\n",
              "      <td>-0.085412</td>\n",
              "      <td>0.549240</td>\n",
              "      <td>-0.019521</td>\n",
              "      <td>-0.255597</td>\n",
              "      <td>0.841144</td>\n",
              "      <td>0.250485</td>\n",
              "      <td>0.224235</td>\n",
              "      <td>0.082816</td>\n",
              "      <td>-0.836843</td>\n",
              "      <td>-0.410092</td>\n",
              "      <td>-0.264219</td>\n",
              "      <td>-0.273554</td>\n",
              "      <td>-0.650051</td>\n",
              "      <td>1.750182</td>\n",
              "      <td>-0.861172</td>\n",
              "      <td>-0.527829</td>\n",
              "      <td>0.095693</td>\n",
              "      <td>-0.403627</td>\n",
              "      <td>-1.519472</td>\n",
              "      <td>-0.888237</td>\n",
              "      <td>0.353084</td>\n",
              "      <td>0.039421</td>\n",
              "      <td>-0.367876</td>\n",
              "      <td>1.177869</td>\n",
              "      <td>-1.101644</td>\n",
              "      <td>0.077389</td>\n",
              "      <td>-0.204253</td>\n",
              "      <td>-1.051643</td>\n",
              "      <td>-0.537505</td>\n",
              "      <td>-0.322400</td>\n",
              "      <td>-0.039251</td>\n",
              "      <td>0.118602</td>\n",
              "      <td>0.296560</td>\n",
              "      <td>0.035955</td>\n",
              "      <td>0.139474</td>\n",
              "      <td>0.645116</td>\n",
              "      <td>0.126463</td>\n",
              "      <td>-0.538562</td>\n",
              "      <td>-0.517912</td>\n",
              "      <td>0.543584</td>\n",
              "      <td>0.492052</td>\n",
              "      <td>-0.204867</td>\n",
              "      <td>-0.011538</td>\n",
              "      <td>-0.328607</td>\n",
              "      <td>-0.150055</td>\n",
              "      <td>-0.992373</td>\n",
              "      <td>-0.266003</td>\n",
              "      <td>-0.037134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>2996</td>\n",
              "      <td>-0.555790</td>\n",
              "      <td>-0.634473</td>\n",
              "      <td>-0.339856</td>\n",
              "      <td>0.005876</td>\n",
              "      <td>0.086831</td>\n",
              "      <td>-0.206456</td>\n",
              "      <td>-0.355885</td>\n",
              "      <td>1.002572</td>\n",
              "      <td>-1.010125</td>\n",
              "      <td>-0.325057</td>\n",
              "      <td>0.375583</td>\n",
              "      <td>0.247387</td>\n",
              "      <td>0.309532</td>\n",
              "      <td>-2.208463</td>\n",
              "      <td>-0.211460</td>\n",
              "      <td>-0.511220</td>\n",
              "      <td>0.464247</td>\n",
              "      <td>0.797192</td>\n",
              "      <td>0.009930</td>\n",
              "      <td>1.885652</td>\n",
              "      <td>0.546544</td>\n",
              "      <td>0.420555</td>\n",
              "      <td>-0.337843</td>\n",
              "      <td>-1.315462</td>\n",
              "      <td>-0.019467</td>\n",
              "      <td>0.762943</td>\n",
              "      <td>0.716149</td>\n",
              "      <td>-0.800867</td>\n",
              "      <td>-0.066862</td>\n",
              "      <td>-0.200398</td>\n",
              "      <td>-0.801014</td>\n",
              "      <td>-0.191482</td>\n",
              "      <td>0.340264</td>\n",
              "      <td>-0.461681</td>\n",
              "      <td>-0.068821</td>\n",
              "      <td>0.482545</td>\n",
              "      <td>2.349605</td>\n",
              "      <td>0.109770</td>\n",
              "      <td>-0.238455</td>\n",
              "      <td>-1.510526</td>\n",
              "      <td>0.334074</td>\n",
              "      <td>-0.188084</td>\n",
              "      <td>-0.673507</td>\n",
              "      <td>-0.320320</td>\n",
              "      <td>-0.119507</td>\n",
              "      <td>-2.042197</td>\n",
              "      <td>0.104381</td>\n",
              "      <td>-1.097715</td>\n",
              "      <td>0.589811</td>\n",
              "      <td>-0.030453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>2997</td>\n",
              "      <td>-0.584811</td>\n",
              "      <td>-0.578910</td>\n",
              "      <td>0.561006</td>\n",
              "      <td>0.384265</td>\n",
              "      <td>0.779752</td>\n",
              "      <td>-0.244268</td>\n",
              "      <td>-0.859684</td>\n",
              "      <td>0.426371</td>\n",
              "      <td>0.296343</td>\n",
              "      <td>-0.013430</td>\n",
              "      <td>0.809101</td>\n",
              "      <td>-0.146304</td>\n",
              "      <td>0.990224</td>\n",
              "      <td>0.341211</td>\n",
              "      <td>0.568451</td>\n",
              "      <td>-0.052142</td>\n",
              "      <td>-0.504616</td>\n",
              "      <td>0.919922</td>\n",
              "      <td>0.513638</td>\n",
              "      <td>-1.144571</td>\n",
              "      <td>0.702540</td>\n",
              "      <td>-0.605691</td>\n",
              "      <td>-0.140450</td>\n",
              "      <td>0.093950</td>\n",
              "      <td>-0.774955</td>\n",
              "      <td>-1.937041</td>\n",
              "      <td>-1.801049</td>\n",
              "      <td>-0.783803</td>\n",
              "      <td>-1.122335</td>\n",
              "      <td>-1.286547</td>\n",
              "      <td>0.702354</td>\n",
              "      <td>0.200423</td>\n",
              "      <td>0.406720</td>\n",
              "      <td>0.597101</td>\n",
              "      <td>0.299996</td>\n",
              "      <td>0.106068</td>\n",
              "      <td>1.894365</td>\n",
              "      <td>0.828619</td>\n",
              "      <td>0.942041</td>\n",
              "      <td>-0.407400</td>\n",
              "      <td>0.380635</td>\n",
              "      <td>0.705248</td>\n",
              "      <td>0.664459</td>\n",
              "      <td>-0.444807</td>\n",
              "      <td>-0.027819</td>\n",
              "      <td>-1.750902</td>\n",
              "      <td>0.585283</td>\n",
              "      <td>-1.624563</td>\n",
              "      <td>0.267947</td>\n",
              "      <td>-0.320521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>2998</td>\n",
              "      <td>0.953752</td>\n",
              "      <td>0.033205</td>\n",
              "      <td>-0.462647</td>\n",
              "      <td>0.610091</td>\n",
              "      <td>0.642509</td>\n",
              "      <td>0.149174</td>\n",
              "      <td>0.732204</td>\n",
              "      <td>0.001087</td>\n",
              "      <td>0.413459</td>\n",
              "      <td>0.251144</td>\n",
              "      <td>-0.689569</td>\n",
              "      <td>-0.140625</td>\n",
              "      <td>-0.248790</td>\n",
              "      <td>0.285954</td>\n",
              "      <td>-0.280361</td>\n",
              "      <td>0.248284</td>\n",
              "      <td>-0.293370</td>\n",
              "      <td>0.586049</td>\n",
              "      <td>0.230031</td>\n",
              "      <td>1.110923</td>\n",
              "      <td>0.073926</td>\n",
              "      <td>-0.481501</td>\n",
              "      <td>-0.012235</td>\n",
              "      <td>-0.043196</td>\n",
              "      <td>0.161211</td>\n",
              "      <td>3.251971</td>\n",
              "      <td>-2.229213</td>\n",
              "      <td>-0.261574</td>\n",
              "      <td>-0.099620</td>\n",
              "      <td>0.116055</td>\n",
              "      <td>-0.562454</td>\n",
              "      <td>-0.071100</td>\n",
              "      <td>0.447037</td>\n",
              "      <td>0.133065</td>\n",
              "      <td>0.003292</td>\n",
              "      <td>0.223467</td>\n",
              "      <td>1.566817</td>\n",
              "      <td>0.231280</td>\n",
              "      <td>-0.280118</td>\n",
              "      <td>0.604268</td>\n",
              "      <td>0.578262</td>\n",
              "      <td>-0.616080</td>\n",
              "      <td>0.085226</td>\n",
              "      <td>-0.468162</td>\n",
              "      <td>-0.069771</td>\n",
              "      <td>0.042086</td>\n",
              "      <td>-0.056725</td>\n",
              "      <td>-1.810434</td>\n",
              "      <td>-0.489969</td>\n",
              "      <td>-0.228624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>2999</td>\n",
              "      <td>-1.830269</td>\n",
              "      <td>-0.981212</td>\n",
              "      <td>0.012925</td>\n",
              "      <td>-0.351799</td>\n",
              "      <td>0.915522</td>\n",
              "      <td>-1.061758</td>\n",
              "      <td>0.152227</td>\n",
              "      <td>-0.119314</td>\n",
              "      <td>-0.207047</td>\n",
              "      <td>0.553193</td>\n",
              "      <td>-2.091016</td>\n",
              "      <td>-1.073643</td>\n",
              "      <td>0.162722</td>\n",
              "      <td>2.334106</td>\n",
              "      <td>0.897847</td>\n",
              "      <td>0.389963</td>\n",
              "      <td>1.165908</td>\n",
              "      <td>-0.465031</td>\n",
              "      <td>1.510779</td>\n",
              "      <td>1.778044</td>\n",
              "      <td>1.318378</td>\n",
              "      <td>-0.334704</td>\n",
              "      <td>-0.430927</td>\n",
              "      <td>1.942861</td>\n",
              "      <td>-1.030750</td>\n",
              "      <td>-3.483094</td>\n",
              "      <td>2.421420</td>\n",
              "      <td>-1.724577</td>\n",
              "      <td>0.352449</td>\n",
              "      <td>0.178085</td>\n",
              "      <td>-0.476900</td>\n",
              "      <td>0.317029</td>\n",
              "      <td>-0.519446</td>\n",
              "      <td>0.526414</td>\n",
              "      <td>-1.427469</td>\n",
              "      <td>-1.509564</td>\n",
              "      <td>-1.782482</td>\n",
              "      <td>-0.835274</td>\n",
              "      <td>-1.174398</td>\n",
              "      <td>-1.536416</td>\n",
              "      <td>-0.893163</td>\n",
              "      <td>-1.703396</td>\n",
              "      <td>0.009605</td>\n",
              "      <td>-0.487247</td>\n",
              "      <td>-0.383803</td>\n",
              "      <td>0.818402</td>\n",
              "      <td>-0.648870</td>\n",
              "      <td>3.659653</td>\n",
              "      <td>0.871033</td>\n",
              "      <td>0.720780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>3000</td>\n",
              "      <td>-1.964440</td>\n",
              "      <td>0.990954</td>\n",
              "      <td>0.687315</td>\n",
              "      <td>0.333667</td>\n",
              "      <td>0.449629</td>\n",
              "      <td>0.011339</td>\n",
              "      <td>-0.198056</td>\n",
              "      <td>-2.234372</td>\n",
              "      <td>-0.294367</td>\n",
              "      <td>0.913091</td>\n",
              "      <td>-1.814855</td>\n",
              "      <td>-0.420422</td>\n",
              "      <td>0.371574</td>\n",
              "      <td>0.910125</td>\n",
              "      <td>-0.123776</td>\n",
              "      <td>1.244987</td>\n",
              "      <td>0.922092</td>\n",
              "      <td>0.838900</td>\n",
              "      <td>-0.229174</td>\n",
              "      <td>-0.318651</td>\n",
              "      <td>0.551668</td>\n",
              "      <td>-0.381497</td>\n",
              "      <td>-0.218977</td>\n",
              "      <td>0.203886</td>\n",
              "      <td>-0.016589</td>\n",
              "      <td>0.773363</td>\n",
              "      <td>-0.733395</td>\n",
              "      <td>0.323217</td>\n",
              "      <td>0.815494</td>\n",
              "      <td>-0.142172</td>\n",
              "      <td>-0.261128</td>\n",
              "      <td>0.410713</td>\n",
              "      <td>-0.235535</td>\n",
              "      <td>0.091041</td>\n",
              "      <td>-0.997357</td>\n",
              "      <td>-1.127203</td>\n",
              "      <td>-1.156287</td>\n",
              "      <td>1.607852</td>\n",
              "      <td>0.711446</td>\n",
              "      <td>0.722951</td>\n",
              "      <td>0.423330</td>\n",
              "      <td>-0.971956</td>\n",
              "      <td>0.225077</td>\n",
              "      <td>0.134018</td>\n",
              "      <td>-0.227706</td>\n",
              "      <td>-0.395383</td>\n",
              "      <td>-0.570513</td>\n",
              "      <td>-1.255753</td>\n",
              "      <td>0.379903</td>\n",
              "      <td>0.199309</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 51 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      comp_id   title_1   title_2  ...  title_48  title_49  title_50\n",
              "0           1 -0.977637 -0.543310  ...  0.397541  1.270722 -0.816523\n",
              "1           2  0.041873  0.644655  ...  0.673848 -0.530106  0.133466\n",
              "2           3 -0.905595  0.097928  ...  0.643445  0.357957  0.525154\n",
              "3           4  0.069220  0.411544  ...  0.817547  0.325042  0.254415\n",
              "4           5  0.503560 -0.210970  ... -0.992373 -0.266003 -0.037134\n",
              "...       ...       ...       ...  ...       ...       ...       ...\n",
              "2995     2996 -0.555790 -0.634473  ... -1.097715  0.589811 -0.030453\n",
              "2996     2997 -0.584811 -0.578910  ... -1.624563  0.267947 -0.320521\n",
              "2997     2998  0.953752  0.033205  ... -1.810434 -0.489969 -0.228624\n",
              "2998     2999 -1.830269 -0.981212  ...  3.659653  0.871033  0.720780\n",
              "2999     3000 -1.964440  0.990954  ... -1.255753  0.379903  0.199309\n",
              "\n",
              "[3000 rows x 51 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "POp2C3jXcMh6",
        "outputId": "2a9e7098-511c-484f-e72e-83c1edeb6583"
      },
      "source": [
        "d = pd.read_csv('/content/gdrive/MyDrive/Video Popularity Prediction Challenge Dataset/Dataset/Description Data/train_desc_df.csv')\n",
        "d"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>comp_id</th>\n",
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              "      <th>desc_34</th>\n",
              "      <th>desc_35</th>\n",
              "      <th>desc_36</th>\n",
              "      <th>desc_37</th>\n",
              "      <th>desc_38</th>\n",
              "      <th>desc_39</th>\n",
              "      <th>desc_40</th>\n",
              "      <th>desc_41</th>\n",
              "      <th>desc_42</th>\n",
              "      <th>desc_43</th>\n",
              "      <th>desc_44</th>\n",
              "      <th>desc_45</th>\n",
              "      <th>desc_46</th>\n",
              "      <th>desc_47</th>\n",
              "      <th>desc_48</th>\n",
              "      <th>desc_49</th>\n",
              "      <th>desc_50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>-0.009555</td>\n",
              "      <td>0.002479</td>\n",
              "      <td>0.002927</td>\n",
              "      <td>0.015774</td>\n",
              "      <td>-0.008177</td>\n",
              "      <td>-0.016036</td>\n",
              "      <td>0.026697</td>\n",
              "      <td>-0.000106</td>\n",
              "      <td>0.025788</td>\n",
              "      <td>0.052237</td>\n",
              "      <td>0.028440</td>\n",
              "      <td>-0.085249</td>\n",
              "      <td>0.007856</td>\n",
              "      <td>-0.000305</td>\n",
              "      <td>0.008731</td>\n",
              "      <td>-0.068400</td>\n",
              "      <td>0.003454</td>\n",
              "      <td>0.001329</td>\n",
              "      <td>0.013231</td>\n",
              "      <td>-0.007687</td>\n",
              "      <td>0.030407</td>\n",
              "      <td>-0.001134</td>\n",
              "      <td>0.000494</td>\n",
              "      <td>-6.461385</td>\n",
              "      <td>0.038131</td>\n",
              "      <td>0.026232</td>\n",
              "      <td>-0.001567</td>\n",
              "      <td>0.009113</td>\n",
              "      <td>0.016394</td>\n",
              "      <td>-0.005538</td>\n",
              "      <td>-0.009708</td>\n",
              "      <td>-0.013093</td>\n",
              "      <td>-0.005617</td>\n",
              "      <td>0.006824</td>\n",
              "      <td>0.012795</td>\n",
              "      <td>-0.021388</td>\n",
              "      <td>-0.105503</td>\n",
              "      <td>0.006901</td>\n",
              "      <td>0.000284</td>\n",
              "      <td>0.027454</td>\n",
              "      <td>-0.029430</td>\n",
              "      <td>-0.029423</td>\n",
              "      <td>0.052256</td>\n",
              "      <td>0.007814</td>\n",
              "      <td>0.023472</td>\n",
              "      <td>-0.021134</td>\n",
              "      <td>0.003324</td>\n",
              "      <td>-0.004393</td>\n",
              "      <td>0.004294</td>\n",
              "      <td>-0.002223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>-0.009555</td>\n",
              "      <td>0.002479</td>\n",
              "      <td>0.002927</td>\n",
              "      <td>0.015774</td>\n",
              "      <td>-0.008177</td>\n",
              "      <td>-0.016036</td>\n",
              "      <td>0.026697</td>\n",
              "      <td>-0.000106</td>\n",
              "      <td>0.025788</td>\n",
              "      <td>0.052237</td>\n",
              "      <td>0.028440</td>\n",
              "      <td>-0.085249</td>\n",
              "      <td>0.007856</td>\n",
              "      <td>-0.000305</td>\n",
              "      <td>0.008731</td>\n",
              "      <td>-0.068400</td>\n",
              "      <td>0.003454</td>\n",
              "      <td>0.001329</td>\n",
              "      <td>0.013231</td>\n",
              "      <td>-0.007687</td>\n",
              "      <td>0.030407</td>\n",
              "      <td>-0.001134</td>\n",
              "      <td>0.000494</td>\n",
              "      <td>-6.461385</td>\n",
              "      <td>0.038131</td>\n",
              "      <td>0.026232</td>\n",
              "      <td>-0.001567</td>\n",
              "      <td>0.009113</td>\n",
              "      <td>0.016394</td>\n",
              "      <td>-0.005538</td>\n",
              "      <td>-0.009708</td>\n",
              "      <td>-0.013093</td>\n",
              "      <td>-0.005617</td>\n",
              "      <td>0.006824</td>\n",
              "      <td>0.012795</td>\n",
              "      <td>-0.021388</td>\n",
              "      <td>-0.105503</td>\n",
              "      <td>0.006901</td>\n",
              "      <td>0.000284</td>\n",
              "      <td>0.027454</td>\n",
              "      <td>-0.029430</td>\n",
              "      <td>-0.029423</td>\n",
              "      <td>0.052256</td>\n",
              "      <td>0.007814</td>\n",
              "      <td>0.023472</td>\n",
              "      <td>-0.021134</td>\n",
              "      <td>0.003324</td>\n",
              "      <td>-0.004393</td>\n",
              "      <td>0.004294</td>\n",
              "      <td>-0.002223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>-0.356706</td>\n",
              "      <td>0.213562</td>\n",
              "      <td>0.252663</td>\n",
              "      <td>0.090735</td>\n",
              "      <td>0.328961</td>\n",
              "      <td>-0.482705</td>\n",
              "      <td>0.067300</td>\n",
              "      <td>0.384217</td>\n",
              "      <td>-0.147253</td>\n",
              "      <td>-0.463378</td>\n",
              "      <td>-0.175824</td>\n",
              "      <td>2.032145</td>\n",
              "      <td>-0.143330</td>\n",
              "      <td>-0.394972</td>\n",
              "      <td>-0.157462</td>\n",
              "      <td>0.245942</td>\n",
              "      <td>0.405734</td>\n",
              "      <td>0.010298</td>\n",
              "      <td>-0.601022</td>\n",
              "      <td>-0.022109</td>\n",
              "      <td>0.284740</td>\n",
              "      <td>0.170112</td>\n",
              "      <td>-0.057594</td>\n",
              "      <td>3.057399</td>\n",
              "      <td>-0.146002</td>\n",
              "      <td>0.143649</td>\n",
              "      <td>0.914032</td>\n",
              "      <td>-0.035008</td>\n",
              "      <td>-0.156502</td>\n",
              "      <td>0.083768</td>\n",
              "      <td>0.597216</td>\n",
              "      <td>0.656176</td>\n",
              "      <td>-0.071332</td>\n",
              "      <td>0.730609</td>\n",
              "      <td>0.038204</td>\n",
              "      <td>-0.837585</td>\n",
              "      <td>-0.977806</td>\n",
              "      <td>0.663729</td>\n",
              "      <td>0.014156</td>\n",
              "      <td>0.530039</td>\n",
              "      <td>-0.626436</td>\n",
              "      <td>-0.102908</td>\n",
              "      <td>0.576792</td>\n",
              "      <td>-0.805986</td>\n",
              "      <td>0.310637</td>\n",
              "      <td>0.422263</td>\n",
              "      <td>0.403496</td>\n",
              "      <td>0.264240</td>\n",
              "      <td>7.057590</td>\n",
              "      <td>-0.252417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>-0.294013</td>\n",
              "      <td>0.165262</td>\n",
              "      <td>0.257102</td>\n",
              "      <td>0.421037</td>\n",
              "      <td>0.463214</td>\n",
              "      <td>-0.769155</td>\n",
              "      <td>0.159450</td>\n",
              "      <td>0.236385</td>\n",
              "      <td>-0.183974</td>\n",
              "      <td>-0.357842</td>\n",
              "      <td>-0.051879</td>\n",
              "      <td>2.339253</td>\n",
              "      <td>-0.136640</td>\n",
              "      <td>-0.248848</td>\n",
              "      <td>-0.404962</td>\n",
              "      <td>0.199895</td>\n",
              "      <td>0.182226</td>\n",
              "      <td>0.033816</td>\n",
              "      <td>-0.706218</td>\n",
              "      <td>0.130868</td>\n",
              "      <td>0.501670</td>\n",
              "      <td>0.186079</td>\n",
              "      <td>-0.162675</td>\n",
              "      <td>3.070982</td>\n",
              "      <td>-0.032498</td>\n",
              "      <td>0.068264</td>\n",
              "      <td>1.034647</td>\n",
              "      <td>0.028793</td>\n",
              "      <td>-0.244596</td>\n",
              "      <td>0.037327</td>\n",
              "      <td>0.607908</td>\n",
              "      <td>0.415434</td>\n",
              "      <td>0.059239</td>\n",
              "      <td>0.319092</td>\n",
              "      <td>0.092515</td>\n",
              "      <td>-0.831052</td>\n",
              "      <td>-0.817347</td>\n",
              "      <td>0.602119</td>\n",
              "      <td>-0.067764</td>\n",
              "      <td>0.176569</td>\n",
              "      <td>-0.435836</td>\n",
              "      <td>0.052975</td>\n",
              "      <td>0.108777</td>\n",
              "      <td>-0.599593</td>\n",
              "      <td>0.408430</td>\n",
              "      <td>0.591615</td>\n",
              "      <td>0.415667</td>\n",
              "      <td>0.334706</td>\n",
              "      <td>7.025648</td>\n",
              "      <td>-0.309093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>-0.028657</td>\n",
              "      <td>0.157017</td>\n",
              "      <td>0.282709</td>\n",
              "      <td>-2.674227</td>\n",
              "      <td>-0.711383</td>\n",
              "      <td>2.259387</td>\n",
              "      <td>-0.162175</td>\n",
              "      <td>0.605468</td>\n",
              "      <td>0.712229</td>\n",
              "      <td>0.027828</td>\n",
              "      <td>-0.529120</td>\n",
              "      <td>1.597859</td>\n",
              "      <td>1.062390</td>\n",
              "      <td>0.169534</td>\n",
              "      <td>-0.207715</td>\n",
              "      <td>-1.352804</td>\n",
              "      <td>0.181599</td>\n",
              "      <td>0.210282</td>\n",
              "      <td>0.515549</td>\n",
              "      <td>-0.411344</td>\n",
              "      <td>0.509503</td>\n",
              "      <td>0.897175</td>\n",
              "      <td>-0.348758</td>\n",
              "      <td>2.843993</td>\n",
              "      <td>-0.599135</td>\n",
              "      <td>0.031930</td>\n",
              "      <td>-0.849955</td>\n",
              "      <td>0.012406</td>\n",
              "      <td>0.019594</td>\n",
              "      <td>-0.332295</td>\n",
              "      <td>0.389301</td>\n",
              "      <td>-0.292774</td>\n",
              "      <td>0.386677</td>\n",
              "      <td>-3.005544</td>\n",
              "      <td>0.383045</td>\n",
              "      <td>-0.167495</td>\n",
              "      <td>-0.326522</td>\n",
              "      <td>0.134930</td>\n",
              "      <td>0.089576</td>\n",
              "      <td>0.669632</td>\n",
              "      <td>-1.714496</td>\n",
              "      <td>0.297421</td>\n",
              "      <td>-0.097744</td>\n",
              "      <td>0.000669</td>\n",
              "      <td>-1.639307</td>\n",
              "      <td>0.244313</td>\n",
              "      <td>0.099975</td>\n",
              "      <td>0.371806</td>\n",
              "      <td>-2.259024</td>\n",
              "      <td>-0.131085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>2996</td>\n",
              "      <td>-0.117646</td>\n",
              "      <td>-0.416358</td>\n",
              "      <td>0.225466</td>\n",
              "      <td>-0.270870</td>\n",
              "      <td>0.491771</td>\n",
              "      <td>-0.169219</td>\n",
              "      <td>0.202514</td>\n",
              "      <td>0.150615</td>\n",
              "      <td>0.120734</td>\n",
              "      <td>-1.878840</td>\n",
              "      <td>0.091018</td>\n",
              "      <td>1.567480</td>\n",
              "      <td>0.650090</td>\n",
              "      <td>0.192301</td>\n",
              "      <td>-0.220214</td>\n",
              "      <td>-0.453192</td>\n",
              "      <td>0.094879</td>\n",
              "      <td>-0.420745</td>\n",
              "      <td>-0.426305</td>\n",
              "      <td>-0.193110</td>\n",
              "      <td>0.448235</td>\n",
              "      <td>0.595069</td>\n",
              "      <td>-0.322287</td>\n",
              "      <td>4.020459</td>\n",
              "      <td>-0.618975</td>\n",
              "      <td>-0.639583</td>\n",
              "      <td>0.553451</td>\n",
              "      <td>0.141830</td>\n",
              "      <td>0.285943</td>\n",
              "      <td>-0.269721</td>\n",
              "      <td>0.028233</td>\n",
              "      <td>0.483574</td>\n",
              "      <td>-0.609051</td>\n",
              "      <td>0.351858</td>\n",
              "      <td>0.596665</td>\n",
              "      <td>1.118226</td>\n",
              "      <td>-1.344260</td>\n",
              "      <td>0.143427</td>\n",
              "      <td>0.442637</td>\n",
              "      <td>-1.036403</td>\n",
              "      <td>0.190193</td>\n",
              "      <td>-0.755953</td>\n",
              "      <td>-0.231644</td>\n",
              "      <td>0.308153</td>\n",
              "      <td>-0.373529</td>\n",
              "      <td>0.876928</td>\n",
              "      <td>0.422415</td>\n",
              "      <td>0.074538</td>\n",
              "      <td>-3.646102</td>\n",
              "      <td>-0.302841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>2997</td>\n",
              "      <td>-0.217174</td>\n",
              "      <td>-0.559902</td>\n",
              "      <td>0.107421</td>\n",
              "      <td>-2.919515</td>\n",
              "      <td>-1.488848</td>\n",
              "      <td>1.903834</td>\n",
              "      <td>0.043642</td>\n",
              "      <td>-0.645082</td>\n",
              "      <td>0.071144</td>\n",
              "      <td>-0.670671</td>\n",
              "      <td>0.337075</td>\n",
              "      <td>-0.989741</td>\n",
              "      <td>0.367956</td>\n",
              "      <td>-1.781477</td>\n",
              "      <td>-0.497582</td>\n",
              "      <td>3.185903</td>\n",
              "      <td>0.807017</td>\n",
              "      <td>-1.547830</td>\n",
              "      <td>0.410105</td>\n",
              "      <td>-0.927344</td>\n",
              "      <td>1.661425</td>\n",
              "      <td>0.156075</td>\n",
              "      <td>-0.419432</td>\n",
              "      <td>3.085930</td>\n",
              "      <td>0.844582</td>\n",
              "      <td>-2.119712</td>\n",
              "      <td>-0.350849</td>\n",
              "      <td>-0.339692</td>\n",
              "      <td>0.211236</td>\n",
              "      <td>-0.591241</td>\n",
              "      <td>0.077780</td>\n",
              "      <td>-0.805586</td>\n",
              "      <td>1.197226</td>\n",
              "      <td>-1.431773</td>\n",
              "      <td>-0.380102</td>\n",
              "      <td>-2.614701</td>\n",
              "      <td>1.179236</td>\n",
              "      <td>0.720504</td>\n",
              "      <td>1.122849</td>\n",
              "      <td>-0.978720</td>\n",
              "      <td>-1.433418</td>\n",
              "      <td>1.234688</td>\n",
              "      <td>0.406196</td>\n",
              "      <td>0.877129</td>\n",
              "      <td>-1.218880</td>\n",
              "      <td>1.145661</td>\n",
              "      <td>-0.112073</td>\n",
              "      <td>0.230396</td>\n",
              "      <td>-3.713165</td>\n",
              "      <td>-0.525256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>2998</td>\n",
              "      <td>0.263279</td>\n",
              "      <td>0.830375</td>\n",
              "      <td>-0.211163</td>\n",
              "      <td>0.410727</td>\n",
              "      <td>-0.032350</td>\n",
              "      <td>-0.342914</td>\n",
              "      <td>-0.380353</td>\n",
              "      <td>-0.613578</td>\n",
              "      <td>-0.162543</td>\n",
              "      <td>0.497122</td>\n",
              "      <td>0.009663</td>\n",
              "      <td>2.200954</td>\n",
              "      <td>-0.341892</td>\n",
              "      <td>0.743775</td>\n",
              "      <td>-0.278086</td>\n",
              "      <td>-1.127912</td>\n",
              "      <td>1.387607</td>\n",
              "      <td>0.082762</td>\n",
              "      <td>0.018902</td>\n",
              "      <td>-0.586323</td>\n",
              "      <td>-0.247133</td>\n",
              "      <td>-0.040012</td>\n",
              "      <td>0.114449</td>\n",
              "      <td>3.104870</td>\n",
              "      <td>-0.268833</td>\n",
              "      <td>0.303874</td>\n",
              "      <td>0.875373</td>\n",
              "      <td>0.147913</td>\n",
              "      <td>-0.252781</td>\n",
              "      <td>0.287648</td>\n",
              "      <td>0.161341</td>\n",
              "      <td>-0.593966</td>\n",
              "      <td>0.155817</td>\n",
              "      <td>-1.261732</td>\n",
              "      <td>-0.015874</td>\n",
              "      <td>-1.001576</td>\n",
              "      <td>0.521639</td>\n",
              "      <td>0.607386</td>\n",
              "      <td>0.198213</td>\n",
              "      <td>0.145269</td>\n",
              "      <td>-0.115696</td>\n",
              "      <td>-0.746977</td>\n",
              "      <td>0.685888</td>\n",
              "      <td>0.203295</td>\n",
              "      <td>-0.379776</td>\n",
              "      <td>0.512881</td>\n",
              "      <td>-0.275278</td>\n",
              "      <td>0.459139</td>\n",
              "      <td>2.927374</td>\n",
              "      <td>0.219621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>2999</td>\n",
              "      <td>0.711288</td>\n",
              "      <td>0.173976</td>\n",
              "      <td>0.551748</td>\n",
              "      <td>0.315852</td>\n",
              "      <td>-0.612846</td>\n",
              "      <td>0.539549</td>\n",
              "      <td>-0.327575</td>\n",
              "      <td>1.720605</td>\n",
              "      <td>1.238591</td>\n",
              "      <td>-1.189375</td>\n",
              "      <td>-1.641473</td>\n",
              "      <td>0.081543</td>\n",
              "      <td>0.333393</td>\n",
              "      <td>0.874835</td>\n",
              "      <td>-0.058675</td>\n",
              "      <td>-1.177250</td>\n",
              "      <td>-0.410042</td>\n",
              "      <td>-0.282713</td>\n",
              "      <td>0.876611</td>\n",
              "      <td>1.682381</td>\n",
              "      <td>-0.386957</td>\n",
              "      <td>1.841339</td>\n",
              "      <td>0.219033</td>\n",
              "      <td>3.419959</td>\n",
              "      <td>-0.484191</td>\n",
              "      <td>2.049087</td>\n",
              "      <td>-0.710066</td>\n",
              "      <td>0.236388</td>\n",
              "      <td>1.165403</td>\n",
              "      <td>-0.053473</td>\n",
              "      <td>1.992675</td>\n",
              "      <td>-0.318399</td>\n",
              "      <td>-0.028148</td>\n",
              "      <td>1.923319</td>\n",
              "      <td>1.205491</td>\n",
              "      <td>-2.441168</td>\n",
              "      <td>-2.297159</td>\n",
              "      <td>-0.816323</td>\n",
              "      <td>1.524811</td>\n",
              "      <td>-0.009186</td>\n",
              "      <td>-1.174133</td>\n",
              "      <td>-1.123509</td>\n",
              "      <td>0.387533</td>\n",
              "      <td>-0.117955</td>\n",
              "      <td>1.226050</td>\n",
              "      <td>-1.122658</td>\n",
              "      <td>-0.056242</td>\n",
              "      <td>-0.078198</td>\n",
              "      <td>-4.572043</td>\n",
              "      <td>-0.803208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>3000</td>\n",
              "      <td>0.439702</td>\n",
              "      <td>0.521675</td>\n",
              "      <td>0.065447</td>\n",
              "      <td>-0.370665</td>\n",
              "      <td>-1.045153</td>\n",
              "      <td>0.115062</td>\n",
              "      <td>1.091020</td>\n",
              "      <td>-0.223977</td>\n",
              "      <td>-0.358226</td>\n",
              "      <td>0.732570</td>\n",
              "      <td>0.227516</td>\n",
              "      <td>-2.345043</td>\n",
              "      <td>-0.259910</td>\n",
              "      <td>0.774630</td>\n",
              "      <td>-0.663236</td>\n",
              "      <td>0.369377</td>\n",
              "      <td>-0.843837</td>\n",
              "      <td>0.357219</td>\n",
              "      <td>-1.024045</td>\n",
              "      <td>-0.637269</td>\n",
              "      <td>0.967215</td>\n",
              "      <td>0.889042</td>\n",
              "      <td>-0.246002</td>\n",
              "      <td>2.093875</td>\n",
              "      <td>-0.513510</td>\n",
              "      <td>-0.354618</td>\n",
              "      <td>-1.598668</td>\n",
              "      <td>0.526591</td>\n",
              "      <td>0.424314</td>\n",
              "      <td>-0.573932</td>\n",
              "      <td>0.334873</td>\n",
              "      <td>0.472387</td>\n",
              "      <td>0.404662</td>\n",
              "      <td>0.903297</td>\n",
              "      <td>-0.307765</td>\n",
              "      <td>-0.310245</td>\n",
              "      <td>-0.251537</td>\n",
              "      <td>-0.490589</td>\n",
              "      <td>-0.449359</td>\n",
              "      <td>-0.987128</td>\n",
              "      <td>0.961226</td>\n",
              "      <td>0.267603</td>\n",
              "      <td>0.389177</td>\n",
              "      <td>0.024658</td>\n",
              "      <td>0.593088</td>\n",
              "      <td>-0.912221</td>\n",
              "      <td>-0.960675</td>\n",
              "      <td>-0.498283</td>\n",
              "      <td>0.549810</td>\n",
              "      <td>0.213448</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 51 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      comp_id    desc_1    desc_2  ...   desc_48   desc_49   desc_50\n",
              "0           1 -0.009555  0.002479  ... -0.004393  0.004294 -0.002223\n",
              "1           2 -0.009555  0.002479  ... -0.004393  0.004294 -0.002223\n",
              "2           3 -0.356706  0.213562  ...  0.264240  7.057590 -0.252417\n",
              "3           4 -0.294013  0.165262  ...  0.334706  7.025648 -0.309093\n",
              "4           5 -0.028657  0.157017  ...  0.371806 -2.259024 -0.131085\n",
              "...       ...       ...       ...  ...       ...       ...       ...\n",
              "2995     2996 -0.117646 -0.416358  ...  0.074538 -3.646102 -0.302841\n",
              "2996     2997 -0.217174 -0.559902  ...  0.230396 -3.713165 -0.525256\n",
              "2997     2998  0.263279  0.830375  ...  0.459139  2.927374  0.219621\n",
              "2998     2999  0.711288  0.173976  ... -0.078198 -4.572043 -0.803208\n",
              "2999     3000  0.439702  0.521675  ... -0.498283  0.549810  0.213448\n",
              "\n",
              "[3000 rows x 51 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "2Axj1P5fj7mK",
        "outputId": "2bccc577-1743-4211-89f3-81ed2e4fef42"
      },
      "source": [
        "df = pd.read_csv('/content/gdrive/MyDrive/Video Popularity Prediction Challenge Dataset/Dataset/Metadata/train_meta_df.csv')\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
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              "      <th>partner_active</th>\n",
              "      <th>n_likes</th>\n",
              "      <th>views</th>\n",
              "      <th>n_tags</th>\n",
              "      <th>n_formats</th>\n",
              "      <th>dayofweek</th>\n",
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              "      <th>2995</th>\n",
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              "      <th>2996</th>\n",
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              "      <th>2997</th>\n",
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              "      <th>2999</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      comp_id  ad_blocked  embed    ratio  ...  n_tags  n_formats  dayofweek  hour\n",
              "0           1       False   True  1.77778  ...       3          0          5     6\n",
              "1           2       False   True  1.33333  ...       0          2          5     6\n",
              "2           3       False   True  1.76667  ...       0          1          5     6\n",
              "3           4       False   True  1.77778  ...       0          3          5     6\n",
              "4           5       False   True  1.77273  ...       0          0          5     6\n",
              "...       ...         ...    ...      ...  ...     ...        ...        ...   ...\n",
              "2995     2996       False   True  1.76667  ...       8          3          2     4\n",
              "2996     2997       False   True  1.77778  ...       1          4          2     4\n",
              "2997     2998       False   True  1.77778  ...       6          4          2     4\n",
              "2998     2999       False   True  1.77778  ...       5          4          2     3\n",
              "2999     3000       False   True  1.33333  ...       1          4          2     3\n",
              "\n",
              "[3000 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6m__K6sLkKAj"
      },
      "source": [
        "df_test = pd.read_csv('/content/gdrive/MyDrive/Video Popularity Prediction Challenge Dataset/Dataset/Metadata/public_meta_df.csv')\n",
        "i_test = pd.read_csv('/content/gdrive/MyDrive/Video Popularity Prediction Challenge Dataset/Dataset/Image Data/public_image_df.csv')\n",
        "d_test = pd.read_csv('/content/gdrive/MyDrive/Video Popularity Prediction Challenge Dataset/Dataset/Description Data/public_desc_df.csv')\n",
        "t_test = pd.read_csv('/content/gdrive/MyDrive/Video Popularity Prediction Challenge Dataset/Dataset/Title Data/public_title_df.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UsVGyBx6kv3k"
      },
      "source": [
        "del i['comp_id']\n",
        "del t['comp_id']\n",
        "del d['comp_id']\n",
        "del i_test['comp_id']\n",
        "del d_test['comp_id']\n",
        "del t_test['comp_id']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuuEwxElv39-"
      },
      "source": [
        "df['title'] = t.mean(numeric_only=True, axis=1)\n",
        "df['desc'] = d.mean(numeric_only=True, axis=1)\n",
        "df['image'] = i.mean(numeric_only=True, axis=1)\n",
        "df_test['title'] = t_test.mean(numeric_only=True, axis=1)\n",
        "df_test['desc'] = d_test.mean(numeric_only=True, axis=1)\n",
        "df_test['image'] = i_test.mean(numeric_only=True, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "_Ax1wNlfoiXH",
        "outputId": "c2e30271-5288-410b-c5e3-923c82aaec70"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </tbody>\n",
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              "<p>3000 rows × 17 columns</p>\n",
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              "      comp_id  ad_blocked  embed    ratio  ...  hour     title      desc     image\n",
              "0           1       False   True  1.77778  ...     6  0.113135 -0.129038 -0.024423\n",
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              "...       ...         ...    ...      ...  ...   ...       ...       ...       ...\n",
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              "\n",
              "[3000 rows x 17 columns]"
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              "     comp_id  ad_blocked  embed    ratio  ...  hour     title      desc     image\n",
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      "source": [
        "df.isnull().sum()"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "comp_id           0\n",
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              "embed             0\n",
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              "      <td>0</td>\n",
              "      <td>329</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.067066</td>\n",
              "      <td>0.278721</td>\n",
              "      <td>-0.013306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>1326</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.016965</td>\n",
              "      <td>0.281460</td>\n",
              "      <td>0.016042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>1.77273</td>\n",
              "      <td>2612</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>-0.110706</td>\n",
              "      <td>-0.053555</td>\n",
              "      <td>-0.002058</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   comp_id  ad_blocked  embed    ratio  ...  hour     title      desc     image\n",
              "0        1       False   True  1.77778  ...     6  0.113135 -0.129038 -0.024423\n",
              "1        2       False   True  1.33333  ...     6  0.107456 -0.129038  0.011685\n",
              "2        3       False   True  1.76667  ...     6  0.067066  0.278721 -0.013306\n",
              "3        4       False   True  1.77778  ...     6  0.016965  0.281460  0.016042\n",
              "4        5       False   True  1.77273  ...     6 -0.110706 -0.053555 -0.002058\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-hgrH0X5rZT"
      },
      "source": [
        "df['ad_blocked'] = df['ad_blocked'].replace({True: 1, False: 0})\n",
        "df['embed'] = df['embed'].replace({True: 1, False: 0})\n",
        "df['partner'] = df['partner'].replace({True: 1, False: 0})\n",
        "df['partner_active'] = df['partner_active'].replace({True: 1, False: 0})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "JH1YYLmN6sgZ",
        "outputId": "92b4c42c-8c96-4ee9-8614-45944e38b837"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comp_id</th>\n",
              "      <th>ad_blocked</th>\n",
              "      <th>embed</th>\n",
              "      <th>ratio</th>\n",
              "      <th>duration</th>\n",
              "      <th>language</th>\n",
              "      <th>partner</th>\n",
              "      <th>partner_active</th>\n",
              "      <th>n_likes</th>\n",
              "      <th>views</th>\n",
              "      <th>n_tags</th>\n",
              "      <th>n_formats</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>hour</th>\n",
              "      <th>title</th>\n",
              "      <th>desc</th>\n",
              "      <th>image</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>86</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.113135</td>\n",
              "      <td>-0.129038</td>\n",
              "      <td>-0.024423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.33333</td>\n",
              "      <td>1129</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1443</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.107456</td>\n",
              "      <td>-0.129038</td>\n",
              "      <td>0.011685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.76667</td>\n",
              "      <td>1163</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>329</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.067066</td>\n",
              "      <td>0.278721</td>\n",
              "      <td>-0.013306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>1326</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.016965</td>\n",
              "      <td>0.281460</td>\n",
              "      <td>0.016042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.77273</td>\n",
              "      <td>2612</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>-0.110706</td>\n",
              "      <td>-0.053555</td>\n",
              "      <td>-0.002058</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   comp_id  ad_blocked  embed    ratio  ...  hour     title      desc     image\n",
              "0        1           0      1  1.77778  ...     6  0.113135 -0.129038 -0.024423\n",
              "1        2           0      1  1.33333  ...     6  0.107456 -0.129038  0.011685\n",
              "2        3           0      1  1.76667  ...     6  0.067066  0.278721 -0.013306\n",
              "3        4           0      1  1.77778  ...     6  0.016965  0.281460  0.016042\n",
              "4        5           0      1  1.77273  ...     6 -0.110706 -0.053555 -0.002058\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2n-sKCm46_fR"
      },
      "source": [
        "num = ['ratio', 'duration', 'title', 'desc', 'image']\n",
        "cat = ['ad_blocked', 'embed','partner','partner_active','language','n_likes', 'n_tags', 'n_formats','dayofweek', 'hour']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EkXmnfS_9XeJ",
        "outputId": "8c4c02ef-ff48-4e3e-aef3-b7fdafffead2"
      },
      "source": [
        "remove_index=[0,1]\n",
        "remove_col=[cat[i] for i in remove_index]\n",
        "remove_col"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ad_blocked', 'embed']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJCYpGM490Qd"
      },
      "source": [
        "df.drop(columns=remove_col,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hyxxvyytA-zI",
        "outputId": "37c64629-9f9b-42b3-a18d-498e6e4405ae"
      },
      "source": [
        "for i in remove_col:\n",
        "  cat.remove(i)\n",
        "cat"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['partner',\n",
              " 'partner_active',\n",
              " 'language',\n",
              " 'n_likes',\n",
              " 'n_tags',\n",
              " 'n_formats',\n",
              " 'dayofweek',\n",
              " 'hour']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1sAyJqUCv8t"
      },
      "source": [
        "\n",
        "# remove outlier for # 0,1,2 numerical variables \n",
        "\n",
        "# create a function to remove outliers \n",
        "def outlier_removal(df,col,quantile):\n",
        "    plt.figure(figsize=(10,4))\n",
        "    plt.subplot(1,2,1)\n",
        "    sns.distplot(df[col])\n",
        "    plt.title('Distribution Before Outlier Removal')\n",
        "    plt.subplot(1,2,2)\n",
        "    df_new=df[df[col]<df[col].quantile(quantile)]\n",
        "    sns.distplot(df_new[col])\n",
        "    plt.title('Distribution After Outlier Removal')\n",
        "    plt.tight_layout()\n",
        "    return df_new"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "3hwT4skHDNsn",
        "outputId": "5e1e8990-c69d-48e4-a8a3-55d7d7966039"
      },
      "source": [
        "df1 = outlier_removal(df,num[0],0.95)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "3dZAFEE2DWfc",
        "outputId": "70bf00ab-616a-48ea-9586-3d60ec848570"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comp_id</th>\n",
              "      <th>ratio</th>\n",
              "      <th>duration</th>\n",
              "      <th>language</th>\n",
              "      <th>partner</th>\n",
              "      <th>partner_active</th>\n",
              "      <th>n_likes</th>\n",
              "      <th>views</th>\n",
              "      <th>n_tags</th>\n",
              "      <th>n_formats</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>hour</th>\n",
              "      <th>title</th>\n",
              "      <th>desc</th>\n",
              "      <th>image</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>86</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>290</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.113135</td>\n",
              "      <td>-0.129038</td>\n",
              "      <td>-0.024423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.33333</td>\n",
              "      <td>1129</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1443</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.107456</td>\n",
              "      <td>-0.129038</td>\n",
              "      <td>0.011685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1.76667</td>\n",
              "      <td>1163</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>329</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.067066</td>\n",
              "      <td>0.278721</td>\n",
              "      <td>-0.013306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>1326</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.016965</td>\n",
              "      <td>0.281460</td>\n",
              "      <td>0.016042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1.77273</td>\n",
              "      <td>2612</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>-0.110706</td>\n",
              "      <td>-0.053555</td>\n",
              "      <td>-0.002058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>2996</td>\n",
              "      <td>1.76667</td>\n",
              "      <td>138</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>-0.098040</td>\n",
              "      <td>0.003037</td>\n",
              "      <td>-0.034469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>2997</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>113</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>234</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>-0.023772</td>\n",
              "      <td>-0.157895</td>\n",
              "      <td>-0.035876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>2998</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0.077394</td>\n",
              "      <td>0.176008</td>\n",
              "      <td>0.018452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>2999</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>115</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>77</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.111084</td>\n",
              "      <td>0.079970</td>\n",
              "      <td>-0.013169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>3000</td>\n",
              "      <td>1.33333</td>\n",
              "      <td>644</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.020852</td>\n",
              "      <td>-0.030328</td>\n",
              "      <td>-0.001468</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      comp_id    ratio  duration  language  ...  hour     title      desc     image\n",
              "0           1  1.77778        86         3  ...     6  0.113135 -0.129038 -0.024423\n",
              "1           2  1.33333      1129         2  ...     6  0.107456 -0.129038  0.011685\n",
              "2           3  1.76667      1163         2  ...     6  0.067066  0.278721 -0.013306\n",
              "3           4  1.77778      1326         2  ...     6  0.016965  0.281460  0.016042\n",
              "4           5  1.77273      2612         2  ...     6 -0.110706 -0.053555 -0.002058\n",
              "...       ...      ...       ...       ...  ...   ...       ...       ...       ...\n",
              "2995     2996  1.76667       138         2  ...     4 -0.098040  0.003037 -0.034469\n",
              "2996     2997  1.77778       113         2  ...     4 -0.023772 -0.157895 -0.035876\n",
              "2997     2998  1.77778        30         2  ...     4  0.077394  0.176008  0.018452\n",
              "2998     2999  1.77778       115         2  ...     3 -0.111084  0.079970 -0.013169\n",
              "2999     3000  1.33333       644         4  ...     3 -0.020852 -0.030328 -0.001468\n",
              "\n",
              "[3000 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "tecPWGQIDqir",
        "outputId": "bbceffd9-e0b2-4761-8d33-9242eb559d33"
      },
      "source": [
        "df2 = outlier_removal(df1,num[1],0.95)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "YFEOT8lBFqbL",
        "outputId": "deaf5df4-b000-4fed-ea59-452e989ad758"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comp_id</th>\n",
              "      <th>ratio</th>\n",
              "      <th>duration</th>\n",
              "      <th>language</th>\n",
              "      <th>partner</th>\n",
              "      <th>partner_active</th>\n",
              "      <th>n_likes</th>\n",
              "      <th>views</th>\n",
              "      <th>n_tags</th>\n",
              "      <th>n_formats</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>hour</th>\n",
              "      <th>title</th>\n",
              "      <th>desc</th>\n",
              "      <th>image</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>86</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>290</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.113135</td>\n",
              "      <td>-0.129038</td>\n",
              "      <td>-0.024423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.33333</td>\n",
              "      <td>1129</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1443</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.107456</td>\n",
              "      <td>-0.129038</td>\n",
              "      <td>0.011685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1.76667</td>\n",
              "      <td>1163</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>329</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.067066</td>\n",
              "      <td>0.278721</td>\n",
              "      <td>-0.013306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>1326</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.016965</td>\n",
              "      <td>0.281460</td>\n",
              "      <td>0.016042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1.77273</td>\n",
              "      <td>2612</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>-0.110706</td>\n",
              "      <td>-0.053555</td>\n",
              "      <td>-0.002058</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   comp_id    ratio  duration  language  ...  hour     title      desc     image\n",
              "0        1  1.77778        86         3  ...     6  0.113135 -0.129038 -0.024423\n",
              "1        2  1.33333      1129         2  ...     6  0.107456 -0.129038  0.011685\n",
              "2        3  1.76667      1163         2  ...     6  0.067066  0.278721 -0.013306\n",
              "3        4  1.77778      1326         2  ...     6  0.016965  0.281460  0.016042\n",
              "4        5  1.77273      2612         2  ...     6 -0.110706 -0.053555 -0.002058\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "2jfvXnH6Eo30",
        "outputId": "09c1d752-98c9-4b55-ab07-f5d9a8617714"
      },
      "source": [
        "df3 = outlier_removal(df2, num[2],0.95)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "eXxH2JinEyL8",
        "outputId": "fedcd1ad-5650-4b75-cd57-701f7e2163ce"
      },
      "source": [
        "df4 = outlier_removal(df3, num[3],0.95)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XoH3GRJPqS01Pdffd4f09wNQUr19EJCOZ2dXAxcC57q7CV0RkEGk7SS9M0AMmaR3mExFJDDO7APgUcKm7t6Y7HhGRsS7VBfJeM5sOEP6tGWhGd7/R3Ze6+9KqqqqUBSgiMp6Z2S3AU8BiM9thZu8HvgeUAg+Y2Uoz+2FagxQRGeNS3cXibuAq4Gvh37tSvH4RkQnN3d/Rz+SfpDwQEZFxLJnDvPXXivE14Hwz2wCcFz4WERERERkzkjmKRX+tGADnJmudIiIiIiKjpSvpiYiIiIhEqEAWEREREYlQgSwiIiIiEqECWUREREQkQgWyiIiIiEiECmQRERERkQgVyCIiIiIiESqQRUREREQiVCCLiIiIiESoQBYRERERiVCBLCIiIiISoQJZRERERCRCBbKIiIiISIQKZBERERGRCBXIIiIiIiIRKpBFRERERCJUIIuIiIiIRKhAFhERERGJUIEsIiIiIhKhAllEREREJEIFsoiIiIhIhApkEREREZEIFcgiIiIiIhEqkEVEREREIlQgi4hMIGb2UzOrMbM1kWmTzOwBM9sQ/q1MZ4wiImOdCmQRkYnlJuCCPtOuBR5y94XAQ+FjEREZgApkEZEJxN0fA+r7TL4M+Hl4/+fAm1IalIjIOKMCWURk4pvq7rvD+3uAqQPNaGbXmNlyM1teW1ubmuhERMYYFcgiIhnE3R3wQZ6/0d2XuvvSqqqqFEYmIjJ2qEAWEZn49prZdIDwb02a4xERGdNUIIuITHx3A1eF968C7kpjLCIiY54KZBGRCcTMbgGeAhab2Q4zez/wNeB8M9sAnBc+FhGRAeSkOwAREUkcd3/HAE+dm9JARETGsbS0IJvZJ8zsBTNbY2a3mFlBOuIQEREREekr5QWymc0EPgYsdffjgGzgilTHISIiIiLSn3T1Qc4BCs0sBygCdqUpDhERERGRQ6S8QHb3ncA3gW3AbuCAu9/fdz4NVi8iIiIi6ZCOLhaVBJc9nQ/MAIrN7Mq+82mwehERERFJh3R0sTgP2Ozute7eBfweOCMNcYiIiIiIHCYdBfI24DQzKzIzIxh6aF0a4hAREREROUw6+iA/A9wOPAesDmO4MdVxiIiIiIj0Jy0XCnH3LwFfSse6RUREREQGo0tNi4iIiIhEqEAWEREREYlQgSwiIiIiEqECWUREREQkQgWyiIiIiEiECmQRERERkQgVyCIiIiIiESqQRUREREQiVCCLiIiIiESoQBYRERERiVCBLCIiIiISoQJZRERERCRCBbKIiIiISIQKZBERERGRCBXIIiIiIiIRKpBFRERERCJUIIuIiIiIRKhAFhHJEGb2CTN7wczWmNktZlaQ7phERMYiFcgiIhnAzGYCHwOWuvtxQDZwRXqjEhEZm1Qgi4hkjhyg0MxygCJgV5rjEREZk1Qgi4hkAHffCXwT2AbsBg64+/195zOza8xsuZktr62tTXWYIiJjggpkEZEMYGaVwGXAfGAGUGxmV/adz91vdPel7r60qqoq1WGKiIwJKpBFRDLDecBmd6919y7g98AZaY5JRGRMUoEsIpIZtgGnmVmRmRlwLrAuzTGJiIxJKpBFRDKAuz8D3A48B6wmyP83pjUoEZExKifdAYiISGq4+5eAL6U7DhGRsU4tyCIiIiIiESqQRUREREQiVCCLiIiIiESoQBYRERERiVCBLCIiIiISoQJZRERERCQiLQWymVWY2e1mtt7M1pnZ6emIQ0RERESkr3SNg3wD8Cd3v9zM8oCiNMUhIiIiInKIuFqQzez3ZnaRmY26xdnMyoHXAj8BcPdOd28Y7XJFRCaaROZeERGJX7xJ9/vAO4ENZvY1M1s8inXOB2qBn5nZ82b2YzMr7juTmV1jZsvNbHltbe0oViciMm4lMveKiEic4iqQ3f1Bd38XcDKwBXjQzJ40s/eaWe4w15kTLucH7n4S0AJc2886b3T3pe6+tKqqapirEBEZ/xKce0VEJE5xH7Yzs8nA1cAHgOcJ+hGfDDwwzHXuAHa4+zPh49vD5YiISB8JzL0iIhKnuE7SM7M7gMXAL4FL3H13+NRtZrZ8OCt09z1mtt3MFrv7i8C5wNrhLENEJBMkMveKiEj84h3F4v/c/d7oBDPLd/cOd186gvV+FPh1OILFJuC9I1iGiMhEl+jcKyIicYi3i8V/9TPtqZGu1N1Xhv2LT3D3N7n7/pEuS0RkAkto7hURkfgM2oJsZtOAmUChmZ0EWPhUGRq7WEQkKZR7RUTSa6guFm8gODlkFnB9ZHoT8NkkxSQikumUe0VE0mjQAtndfw783Mze6u6/S1FMIiIZTblXRCS9hupicaW7/wqYZ2b/2vd5d7++n5eJiMgoKPeKiKTXUF0sYle4K0l2ICIicpByr4hIGg3VxeJH4d8vpyYcERFR7hURSa+4hnkzs6+bWZmZ5ZrZQ2ZWa2ZXJjs4EZFMptwrIpIe8Y6D/Hp3bwQuBrYARwL/nqygREQEUO4VEUmLeAvkWFeMi4DfuvuBJMUjIiKvUO4VEUmDeC81/UczWw+0AR82syqgPXlhiYgIyr0iImkRVwuyu18LnAEsdfcuoAW4LJmBiYhkOuVeEZH0iLcFGeAogjE5o6/5RYLjERGRQyUs95pZBfBj4DjAgfe5+1OjD1FEZGKJq0A2s18CC4CVQE842VGBLCKSNEnIvTcAf3L3y80sDygafZQimavXnaderuOoaaXpDkUSLN4W5KXAMe7uyQxGREQOkbDca2blwGuBqwHcvRPoHO1yRTLZ9vpW7lm9m8dequWC46axcKoK5Yki3lEs1gDTkhmIyFjQ2NbFPat20dbZM/TMIsmXyNw7H6gFfmZmz5vZj82suO9MZnaNmS03s+W1tbUJWrXIxLS1rhUIWpI/e8fqNEcjiRRvC/IUYK2ZPQt0xCa6+6VJiUokDZrau/jJ45upbe5gVqWOPMuYkMjcmwOcDHzU3Z8xsxuAa4EvRGdy9xuBGwGWLl2qo4Yig9hS18KUknwWTi3h79sb6O11srIs3WFJAsRbIF+XzCBExoIH1u5lf2snWQa7D7SlOxwRSGzu3QHscPdnwse3ExTIIjICve5srWvl2BllTCst4KnOHnY2tDF7khpYJoK4CmR3/4uZzQUWuvuDZlYEZCc3NJHUqmnqYM6kItq6eth9QEPNSvolMve6+x4z225mi939ReBcYG0i4xXJJDVNHbR19TBvcjFTSvIAeHFPkwrkCSKuPshm9kGC1oYfhZNmAncmKyiRdKhv6WRScR7TywvYowJZxoAk5N6PAr82s1XAEuC/RxehSObaWtcCwNzJRVSXFQDw4t6mdIYkCRRvF4t/Ak4FngFw9w1mVp20qERSrLO7l+aObiYV55GTncVz2xqobeqgqjQ/3aFJZkto7nX3lQQjY4jIKO1tbKcgN4tJxXmYGTMrCnlxjwrkiSLeUSw6wiGBAAgHrNfJGzJh1LcEX+9YCzLAut2N6QxJBJR7RcashtYuKgqD4hhg8bRSXlIL8oQRb4H8FzP7LFBoZucDvwX+kLywRFLrkAK5TAWyjBnKvSJj1IG2LsoLcw8+XjS1lJdrm+nq6U1jVJIo8RbI1xKMn7ka+EfgXuDzyQpKJNXqW18pkIvycygvzGW9DpVJ+in3ioxRDa1dlBe9UiAvnlZCV48f7Jss41u8o1j0mtmdwJ3urpHjZcKpb+mgIDeLwtxggICKolx2NWioN0kv5V6Rsamzu5e2rh4qIi3IsfHzdzW0c2S1rqg33g3agmyB68xsH/Ai8KKZ1ZrZF1MTnkhq1Ld0Mqnolb5kZQW51DZ1DPEqkeRQ7hUZ2xragqOOFZEW5Glh9zyNgjQxDNXF4hPAmcCr3H2Su08ClgFnmtknkh6dSIrUt3RRWZx38HFZQQ57G5XkJG2Ue0XGsAOtXQCUF76y35gaFsgaR39iGKpAfjfwDnffHJvg7puAK4H3JDMwkVTp7XX2t3YyOVIglxbk0tLZQ3NHdxojkwym3Csyhh1oCwrkaBeLvJwsppTksUeNKxPCUAVyrrvv6zsx7AuX28/8IuPO/tZOenqdskiiKy0IuuerFVnSRLlXZAxraOvC4JD9BsC08gL2HND5KxPBUAVy5wifExk36sIh3orzXzlnNZb0ahrVD1nSQrlXZAw70NpFaUEO2Vl2yPRpZYXqYjFBDDWKxYlm1t9gsAYUJCEekZSraw7qjZJIgRxrQa5pUqKTtFDuFRnDGto6DxkDOWZ6eQHLt9anISJJtEELZHfPTlUgIukSu0hIcV6kBbkgSHzqYiHpoNwrMrYdaOtiennhYdOnlRfQ0NpFe1cPBbn6Nx7P4r1QiMiEVdcSdKMozn8lmeXnBGMi71UXCxER6aOxrZuygsPbGDXU28ShAlkyXqyLRVGkBdnMmFqWT43GQhYRkYjO7l46e3oP6ZYXM71cQ71NFGkrkM0s28yeN7M/pisGEQi6WBTmZh92skV1WYG6WIiIyCFawuE/i/spkKeFBfKeRo1kMd6lswX548C6NK5fBAi6WPSX6KaWFVCjAllERCJi4+P314I8TS3IE0ZaCmQzmwVcBPw4HesXiapr7qQk//CTKaaW5rO3sQN3T0NUIiIyFg3WglyUl0NZQY76IE8A6WpB/jbwKaB3oBnM7BozW25my2tra1MXmWSc+pbOfhNddVk+bV26mp6IiLyipXPgFmQIuufV6vyVcS/lBbKZXQzUuPuKweZz9xvdfam7L62qqkpRdJKJ6lo6DxniLWZKSX7wfLOuyyAiIoHmjh6g/xZkgKoSneA9EaSjBflM4FIz2wLcCpxjZr9KQxwi9PQ6+1v7b0GeHCuQW5ToREQk0NLRTV52Fnk5/ZdQ1WX5akGeAFJeILv7Z9x9lrvPA64AHnb3K1MdhwhAQ2sn7oeOgRwzuTgPgNomtSCLiEiguaO7331GTHVpPjVN7Tp/ZZzTOMiS0epiV9HrpwW5qlQtyCIicqiWju4B+x9DsO9o7+qlSeevjGtpLZDd/VF3vzidMUhmi/Uv7i/ZVRblHTKPiIhI0II8cIFcXRoM9aZuFuObWpAlo9XHWpD7OUkvLyeL8sJc6pqV5EREJBBPCzJATaP2HeOZCmTJaPVh94mB+pNNLsljX4takEVEBNw9jhbkoECuVePKuKYCWTJafUsXEAzu3p8pxflqQRYREQAa27rp9YGHeINXuljoSqzjmwpkyWj7Wzspzc8hO8v6fX5ySZ76IIuICPDKSdv9XX01pqwwh7ycLPVBHudUIEtGO9DWRUVx7oDPTy7JOzjShchEYGbZZva8mf0x3bGIjDeDjXwUY2ZUlWgs5PFOBbJktP2tnVQU5g34/OTifPa3dtLdM+BV0UXGm48D69IdhMh4FOtyN9hJehCcqKer6Y1vKpAlo+1v7aKiaOAW5CklebgH84mMd2Y2C7gI+HG6YxEZj/Y1D92CDMGJempBHt9UIEtGa2jtPDjecX9il5vepxP1ZGL4NvApYMBDImZ2jZktN7PltbW1qYtMZByInZPS39CgUdVlwdX0ZPxSgSwZraG1i8pBW5DDq+npRD0Z58zsYqDG3VcMNp+73+juS919aVVVVYqiExkf6lo6KMzNHvDE7piqkgL2t3bR2a3ueeOVCmTJWD29TmN7FxWDtiCHV9PT5aZl/DsTuNTMtgC3AueY2a/SG5LI+FLX3Dlk/2MIWpBBRx/HMxXIkrEOtHXhzuB9kIvDAd/Vl0zGOXf/jLvPcvd5wBXAw+5+ZZrDEhlX9jV3DNn/GKAqPPqoE/XGLxXIkrH2twbdJgbrg1xWmENedtbBEzNERCRz1bV0DjoGckysBVmNK+OXCmTJWA3hyBSDtSCbGVU6G1kmGHd/1N0vTnccIuNNXZwtyAevpqcT9cYtFciSsRriaEEGmFKaT636kYmIZLTunl4a2rriKpAnl+RhBjWN2neMVyqQJWPFxjYeqkCuKsmnplGtACIimWx/a3DeSjwn6eVmZzGpKE+NK+OYCmTJWLEW5PJBulhA0JdMZyKLiGS22GhG8bQgQ3g1PbUgj1sqkCVjNbR2kZ1llBUMccnQknzqWnS5aRGRTBYbDz+eFmQICmS1II9fKpAlY+1v7aSiMBezIQZ8L7ZtdLUAACAASURBVM3HPTh7WUREMlPsSGJxHKNYQFggq3veuKUCWTJWQ2vXoCNYxFSXargeEZFMN9wW5OrSAmqbO3D3ZIYlSaICWTLW/tbOIU/Qg6AVAFQgi4hksrqWDnKyjILc+FqQq0vz6erxg0OKyviiAlkyVrwtyLECWeNZiohkrrrmTiYV55E1RLe8mFf2HWpcGY9UIEvGUguyiIjEa19YIMdL3fPGt/g60ohMMO5OfUsnk0qGTnb5OdmUF+YmJcnd/My2w6a9c9mchK9HRERGZ19zx8EGk3hUlwVX09urE/XGJbUgS0Zq7eyho7uXSXG0IEM4nqVaAUREMta+5g6mlMRfIE8vDwrk3QfakhWSJJEKZMlI9eGQbZVxHi6rLs3XYTIRkQzl7mGBHH8Xi4LcbCYX57GzQS3I45EKZMlIsQJ5OC3Ie5N4kl5TexfNHd1JW76IiIxcS2cP7V29w2pBBphRUciuBrUgj0fqgywZqT68zHQ8fZABZlYUcs+q3fT0OtlZ8Z3BPJQDrV08vL6G5VvrDw4DVFmUy4t7Glk8reyQedUvWUQkffaFRxCnlOTT0R3/VVVnVhSysbY5WWFJEqkFWTLS/mG2IM+eVER3ryfkZAt35zd/287Z33yEB9ftpaoknwuPn86Fx08nNzuLnz+1lcc37hv1ekREJDFil4wezkl68EoLsi4WMv6oBVky0nD7IM+qLARge30rMyoKR7zexvYuPn37Ku5bs4dXzatk2fzJhyzvtPmTuG35du5bvZvp5QUsqCoZ8bpERCQxoi3IO/bH32ViZmUhrZ09NLR2xb2/SaT+RkoCHZWMh1qQJSPVt3SSk2WUFcT3G3FWZRHAsBJjX/tbOnnn/z3NA2v38tkLj+K2a04/rNjOyc7i8pNnUVWaz2+Wb6dzGIfyREQkOfaFLchTSodX5M6sCEay2Kl+yOOOWpAlI9W3dFJZnIfFeUWkGRUFmMH2/a1xzd/3V3tXTy+3r9jBS3ubufE9p3DOUVMHfG1+bjZvPmkmP3psE0+8vI/XLa6Oa50iIpIctc2dmMXfLS8m1giyq6GN42aWJyO0IfWE3QMPtHVRkp/D1LICtSzHQQWyZKT6ls5hJbr8nGymlhaMqAXZ3blr5U5W7zzAu0+by54DHQMmp5i5k4s5ZnoZj71Uy6nzJg17nSIikjj7mjuYVJRHTvbwDrzPDAvkRLUgD6ewXbe7kXtW7WLl9gZaOnsOTi/Mzeb0BZM5e1HVsN9PJkl5gWxms4FfAFMBB2509xtSHYdktv2tw7tkKAT9kHfE2YIctXrnAZ7b1sDrFldz9PSyoV8QOv+Yqax9qJFnt9TzwdceMez1iohIYtQ2De8iITGTivMoyM1K2VBv+1s6uWvlTm5/bgdrdjaSbcZR00s5bmY5k4ryaGrv4vntDTy8voaNNc28a9kcSgtyUxLbeJOOFuRu4N/c/TkzKwVWmNkD7r42DbFIhqpr6eToafEXqxCMZPHs5vphvaa9q4d7Vu9mRkUB5x49vK4SU8sKWFhdwjOb6ujq6SVXv/RFRNJiuJeZjjGzcCSL5I2j7+6s2FrPL5/ayr2r99DZ08uxM8q47pJj6OpxivMPLfWOmVHO6p0HuH3Fdm56cgvXvOYI8nOzkxbfeJXyPa6773b358L7TcA6YGaq45DMtr+lk8ri4f1qnlVZyJ7Gdrp74j9x7uH1NTS3d/OmJTPJirO/c9TpR0ymsb2bP7+wZ9ivFRGRxBjuVfSiZlYUsq1++Ecf47GtvpXvP/oyb/3BUzy0roZ3nDqbez/2Gu752Gu4+sz5hxXHMcfPLOddy+ayt7GdW/+2nV4NQ3eYtPZBNrN5wEnAM/08dw1wDcCcOeo0LonT0+s0tHUxqXh4rQGzKgvp6XV2H2hn9qSiIec/0NbF05vqOHlO5cFRMIZr0bRSKotyufmZbVx8wowRLUNEREZnX1PniLpYABxZXcJtf9tOb6+TlaALTbk7D79Yw0PraigryOGyJTNYMruC/JxsVm5vYOX2hiGXsWhqKRcdP50/rNrNs5vrOe2IyQmJbaJI2zFbMysBfgf8i7s39n3e3W9096XuvrSqqir1AcqE1dDaiTtMKhpeC3KsKN5S1xLX/H95qYZed1531MhHocgy46Q5lTy1qY7dBzRMkIhIqrV0dNPW1cOUEXSxAFhYXUprZw+7EpTD3Z07nt/JQ+tqOGl2BZ84bxHL5k8mP2f43SROO2IyC6tLuG/N7oPXB5BAWgpkM8slKI5/7e6/T0cMkrn2tw7vIiExsT7LL+w67PfcYRpaO/nblv2cMnfSsE8G7Ouk2RW4w10rd41qOSIiMnw14UVCqkbYgrxwanDBpw01ibnk9ANr97J8637OXlTF5afMGlX/YTPjLSfPwsy4Z5X2MVEpL5AtGHj2J8A6d78+1esX2dccFMiTh9nForI4j9mTClm948CQ8z7yYi0Ar1s8+qMfk0vyOWlOBXc+v3PUyxIRkeGJHb2bXl4wotcfGV4RdePe0RfIa3c18uhLtbxqXiXnHzM17rH8B1NemMvrFlWxbk8Tj2/YN+rlTRTpaEE+E3g3cI6ZrQxvF6YhDslQsdaA6rLhtwacMLOCVTsH79u1vb6VFVvredW8SiqGOaj8QN5y0kzW72libRyt1yL9MbPZZvaIma01sxfM7OPpjklkPNgdjkAxvc+VT+NVWZzHlJJ8NtQ0jSqO+pZO7li5k+nlBVxy4oyEFMcxZxw5hUnFeXzl3nX09uqEPUjPKBaPu7u5+wnuviS83ZvqOCRz1TQGyW5q6fBbA46fVc72+jb2D9JX67sPbyDLjLMWJe4KeBedMIOcLOPOlWpFlhGLDbF5DHAa8E9mdkyaYxIZ8/aE+4xpZSNrQQZYWF0y6i4WX7xrDe2dPVx+yixyshJbvuVmZ3HuUdWs292oUZNCGlhVMk5NUwf5OVmUFQ5/EJcTwkuFrt7ZfzeLLfta+N1zOzl1/iTKCxM3+Pqk4jzOXlzFXSt30qNf9zICGmJTZGR2NbRRWZRLYd7I+/ounFrCxr3N+AiHU7tn1W7+uGo35xxdzfTykbVkD+XE2RUcUVXMtx/coFZkVCBLBtrb2M7UsoIRHZ46dogC+YaHNpCbbZy1KPEjr7z5pFnsbezgqZfrEr5sySxDDbFpZsvNbHltbW2qQxMZc/YcaGfaKIvShdUlNHV0H2yNHo59zR184a41nDCrnNcuTN6oXllmfPzchby4t4l71+xO2nrGCxXIknGCAnlkZyOXF+ayoKqYJzYefiLDc9v2c8fzO7nqjHlJuXTnuUdXU5qfwx06WU9GQUNsigzPrgPtzBjhCXoxx8+qAGD5lv3Dep2787k7VtPc3s233nYi2QkaR3kgF58wg4XVJXz7wQ0Zf7RSBbJknJrGDqpH0ZfsLSfP4smX63i59pX+ZD29zhfuXMPUsnw+es7CRIR5mILcbC48fjp/WrObts6epKxDJjYNsSkyfLsPtDG9YnQF8nEzyijNz+HJYR4BvPvvu/jzC3v519cvYuHU0lHFEI/sLOPj5y1kY00zf8zwYd9UIEvG2dvYPqIT9GLevnQ2udnGr57eenDa1/+0nhd2NfL5i46hZIBLeybCm06aSUtnD/ev1UkUMjwaYlNk+No6e2ho7Rp1v9+c7CyWHTGJJ1+Ofxi1vY3tfPGuFzh5TgUffM0Ro1r/cFx43HQWTy3lOw9ldiuyCmTJKM0d3bR09oxoiLeYqtJ83njcdH67fAd/fmEP1z/wEj96bBPvPm0ul5yY3MtBL5s/iZkVhepmISOhITZFhmm0YyBHnbFgClvrWtmxv3XIeXt7nU/dvor2rh6+mYKuFVFZWcZHzz2Sl2tbuGd15vZFVoEsGWVvbIi3URTIAP96/iKmlRfwj79cwXce2sBFx0/nS5ckf8SsrCzjsiUz+OuGfdSG4zmLxENDbIoM354D4RBviSiQj5wMEFc3i+8/upG/vFTL5y8+hiPCC42k0oXHTWdhdQnffShzR7RI3rFgkTGopjEoKkfTxQJg3pRi7vnYq/nj33ezoLqEJbMrEhHegG5+ZtvB+7nZWfT0On/4+y7e9+r5SV2viEgm2xUWyDMSMLTa4qmlTCsr4K6VO3n70tkDznff6t1c/8BLvGnJDK5cNmfU6x2JoBV5IR+75XnuXbObi09I7tHRsUgtyJJRapqCZDeak/Ri8nOyeesps5JeHPc1tayAGRUF6mYhIpJkuxuCLhaJaEE2M9736nk8sbGO57f1P5rFI+tr+PitK5lVWcSS2ZXc8ux2bn5m28FbKl10/HQWVBXz3Yc2ZmQrsgpkySiJ6mIRFU1eqUpiS2ZXsnrnATbsHd2lS0VEZGBb61upLs2nIHfkFwmJeteyuVQU5R52MY7eXucnj2/m/T//GwunlnDV6fPIy0lviZadZXz0nMwdF1ldLCSj7G3soCgve8QjTaT6F/xAlsyu4IG1e/j1M9u47tJj0x2OiMiEtKm2mSOqihO2vOL8HD5y9gL++971fOzW57n6jHns2N/GTU9uYeX2Bs47eio3XLGEu1amZ4i1vvu4XnemluXzjT+/yOuPmZb2oj2VMuedihCckTxthFfRG0tK8nO48Pjp/G7FDlo7u9MdjojIhLRpXwvzpyT2JLkPvuYIrn3jUfxx1W4u/+FT/MttK9nb2M71bz+R/3vPKRQncajQ4coy443HTWdrXeshQ5tmgrHzKYikwOZ9rcybkrjWgHR692lzuWvlLu58fhfvTNOJHCIiE1V9SycNrV0sSGALMgR9kT901gIuOn46G2ubKS/MZcmsCrJSOJTbcCysLuHVR07hOw9v4K2nzKK8MPFXih2L1IIsGaO319myr4X5E6RAPmVuJcdML+PHj2/K6MHcRUSSYVN4tdREdrGImj2piNctrubkOZVjtjiGoKD/zIVHcaCti+8/sjHd4aSMWpAlY+xtaqetq2fCtCCbGR953QL++ebnuS9Dh+EREUmWTbUtAByRgC4WY+X8lZE6dkY5bzlpFj97cgvvXDaHuZMnxn50MGpBloyx+WCymzj/2G88LhiG53sPZ+YwPCIiyfLyvmZys41ZlaMfA3ki+Pc3LCYvO4vP3bEG94m/v1GBLBljc11QIE+ULhYQDMPzsXMXsn5PE79dsT3d4YiITBibaluYO7mYnGyVSjc/s42H19dwzlHVPL5xH5/87apx3yo+FHWxkIyxubaF/JwspiXgIiFjQSw5uTtzJxfx5T+spam9mw+85og0RyYiMv5tqm1mQRou8zyWnTp/En/f0cAfV+2aUI1N/dHPIskYm8MT9MbyyRAjYWZcduJM2rt6uOP5nRlx6EtEJJlaOrrZUtfK4mml6Q5lTMky4+2nBJfJ/s3y7XR296Y5ouRRgSwZY3PdxBnBoq9p5QVccOw0XtjVyP/LoLOMRUSS4e87GujpdU6eW5nuUMacyuI83nzSTLbVt/Klu1+YsI0yKpAlI3T19LKtbuKMgdyfM4+cwomzyvnm/S9x/QMvTdikJSKSbM9t3Q/AybNVIPfnhFkVnLWoilue3caNj21KdzhJoT7IkhHW7DxAd69z7IyyuF8z3k5AMDMuP2U2i6aW8p2HNvDs5jquu/RYjpoW/3sWERFYsXU/C6tLKC/KjItijMT5x0yltCCHr963noLcbK46Y166Q0ooFciSEf62pR4ITjCYyLKzjK9ffgInz63kq/eu44Jv/5Vzj6rmyOqSw8at1NX3REQO19vrPLetgTceNy3doYxpWWZc//YltHf18KW7X2BPYzv//vrFE+Y8HxXIkhGe3byf+VOKqS6dGCNYDMbMeMepc7jg2Gn84qmt3PTkZh5aX8PcSUWctaiKRdNKybKJkcBERBJt075mDrR1qf9xHPJysvjhlafwxbtf4AePvsy2+la+9bYTKcjNTndoo6Y+yDLh9fY6f9tSz6vmZVayqyzO4+PnLeSJa8/h4hOmc6Cti188vZXvPryBjTXN6Q5PRGRMevTFWgCWTfAjjolw8zPb+M3yHRw7vYwLjp3GPat2c+63/sKO/a3pDm3U1IIsE96GmqA14NT5k9MdSkr013f6jAVTWDZ/Mqt2NPDQ+hp++sRm6lo6+NxFR2dEq7qISLx+/9xOTpxVPuzLKY+381YSycx47aIqppTk89sV27n4u49zwxUncdaiqnSHNmIqkGXCe3zjPgBOnZfZrQHZWcZJcyo5bmY5f3mplvtW7+Hh9TV86oKjeOepc8ieIP3GRERGav2eRtbubuS6S45Jdyjj0jEzyvinsiO5+ZltXP3TZzn36GrOXlx9sFvfeDr3RV0sZELr7XV+/cxWTpxdwZzJRekOZ0zIzc7ivKOn8qd/eQ0nzCrnC3eu4S0/eJI1Ow+kOzQRkbT6/XM7yckyLjlxRrpDGbemlOTzobMWcOLsCh5cV8Ovnt5KR1dPusMaNhXIMqH9deM+NtW2cPUZc9MdyphzRFUJv3r/Mm64Ygk797dy6fce58t/eIGm9q50hyYiknI79rfyy6e28oZjpzG5JD/d4YxreTlZvO2UWVxywnRe2tvEjx7bxP7WznSHNSzqYiETVm+vc+NjLzOlJJ8Lj59+cHom9xPry8y4bMlMzl5UzTfuX89NT2zhdyt2cNEJMzhuRhlmNq4OiYmIjIS786W7XgDgsxcdneZoJgYz4/QFU5hSms8tz27j+4++zOkLJnPynPFxwrxakGXC+t4jG3liYx3//LoF5OeM/yFnkqm8KJf/etPxfOisBRTn53DLs9u46ckt7GpoS3doIiJJ1dvrfPkPa3lofQ2fOH8hMysK0x3ShLKwupQPnbWA/Jwsrrjxae5auTPdIcVFLcgy4TS1d/G/D2zgp09s5s0nzZxwV/dJlP5a0mdPKuIjZx/J05vqeGj9Xr73yEY21jTzsXMXcswwrkIoIjLWuTsrtzfw9T+9yFOb6nj/q+fzgVcfcfB5HW1MnOrSAj5y1gLuX7eXj9+6kpdrmvmX8xaN6YuKqECWcauhtZNt9a3sbeygtqmDnQ2trN3VyJMv19HR3cu7ls1h0dRSbnl2e7pDHVeys4wzj5zCyXMqeeLlfTy+cR9/emEPx80s45zF1Zy+YAonzamYEAPBi8jE1dPrtHX10NzeTXNHF03t3TS0dfFyTTPrdjfx3Lb9bN7XQlFeNpctmcERU4q59W/aXyRLUX4Ov3r/Mj5/52q+8/BGVmzbz/+89QRmVY7NE+jN3VO/UrMLgBuAbODH7v61weZfunSpL1++PCWxydjh7uxv7WJLXQtb9rWwpa6VrXXB35f2NNHW56zYLAvOnl1QXcLJsyuZWanDZIlw4fHTuOP5ndy1cherdjTQ65BtRlVpPtPKC5hWVsC08gI+fPYCqkvzsTiu0ufudHT30tbZQ2lBDjnZE7+3l5mtcPelaY5BuVfGtc7uXvY2trP7QDu7D7QFfxuCv3sa29nX1MH+1i66enrp7h24vqkuzeeYGWW84dhptHX26Ad/irxz2RzcnVue3c5X7llLd69z9ZnzeO8Z85lWnpwx+Ueae1NeIJtZNvAScD6wA/gb8A53XzvQa8Z6ko5tw9im9L7TDz6OPX/o/Azw/HCWyQDLHvJ10fUd9po4l9nP++js6aG5o4fWjm5aOnto6eimpbM7+NsRexz8be3sprmjm9bOnuBvRw8tncHjnkiCM6CiKJfJJflMLs5jcnEek4rzKSvMobQgl5L8HI3lm2TtXT1s3tfC1rpW9jYGO6QDba+MelGYm8308qBgzs8Jit4eh9aO4DNuau+mvqWTju4eYh+tEfSBnlKSz/TygvBWyLTyAkrycyjKy6YwL5uivByyzTADM8gK72eZYQQnhEQfZ0XmNTOyDAyj151ed9wJ7wdx5GQZOdlGTlbWofezjZwsi6vwH0y6C+SJmHsluaI53jl0PxJMc7p7nM7uXjp7euns7qWju5eO7p5gWp/psWkdB6dF5gvn7ejq85qeXjq6emjr6mHPgXZqmzsO2+cU5GZRVpBLRVGwH8jLySIvO4vc8G9BTjZ5uVkU5GRRkJvN5JJ8SvJ1AD0doid979jfyrfuf4k7V+4ky4xT503i9AWTOaKqmHmTi6kuy6cgN5v88HMcaQ4eae5NxzfkVGCju28CMLNbgcuAAZP0SBzzxT8BQxd5oyksZeSK8rLJMgu++DlZB/8W5+VQWZR3yOPJJXlMLs6nsig3I1oax7KC3GyOnl7G0dNf6Y/c1tnDnsZ2ZlUWsr2+ld2N7ew50E5zRzd1zZ2YBUP+FORkU12az+xJRRTkZJGfm01OltHWFfxQamrvZvO+FlZua6C5o5ux9q+WnWVkZxm5WcaKL5w/Hluckp57G1o7OeNrDydqcZIAsUL2kH1bP9PcPW37uSzjkB+j2VnW53EWeTnG7ElFHDeznIrCXMojt/zx978owKzKIv73H5bwifMWcdvybTy0robrH3ip33nNoKIwl+e/+PqUxZeOAnkmEO3kswNY1ncmM7sGuCZ82GxmL6Ygtr6mAPvSsN6BKJ7BKZ7BKZ7BxR1P4X+OaPnpHox7POXevsbadyVqrMY2VuMCxTZS4z62d41yJfalEb1s8UheNGaPMbj7jcCN6YzBzJanu89glOIZnOIZnOIZ3FiLJ13GQu7tayx/NmM1trEaFyi2kVJsI2NmI+onlo7j1TuB2ZHHs8JpIiKSPMq9IiJxSkeB/DdgoZnNN7M84Arg7jTEISKSSZR7RUTilPIuFu7ebWb/DPyZYKihn7r7C6mOI05j6jAjimcoimdwimdwYy2ehBpnubevsfzZjNXYxmpcoNhGSrGNzIhiS8s4yCIiIiIiY5XGzBIRERERiVCBLCIiIiISkfEFspm9zcxeMLNeMxtwiBIzu8DMXjSzjWZ2bWT6fDN7Jpx+W3jyy2jimWRmD5jZhvBvZT/zvM7MVkZu7Wb2pvC5m8xsc+S5JcmOJ5yvJ7LOuyPT07F9lpjZU+HnusrM/iHyXEK2z0Dfh8jz+eH73Ri+/3mR5z4TTn/RzN4wkvWPIJ5/NbO14fZ4yMzmRp7r97NLcjxXm1ltZL0fiDx3Vfj5bjCzq1IUz/9GYnnJzBoizyV8+8jgRpuXkxxbvDnx6+F7WGdm3zEb5aUYExfXHDO7P4xrbTQ3pTu2cN4yM9thZt9LdlzxxjbYPiVJMY14/5LmuAbcz6Q7tsh8bzUzHyyvHOTuGX0DjiYYRPpRYOkA82QDLwNHAHnA34Fjwud+A1wR3v8h8OFRxvN14Nrw/rXA/wwx/ySgHigKH98EXJ7A7RNXPEDzANNTvn2ARcDC8P4MYDdQkajtM9j3ITLPR4AfhvevAG4L7x8Tzp8PzA+Xk52CeF4X+Y58OBbPYJ9dkuO5GvjeAN/nTeHfyvB+ZbLj6TP/RwlOYEvK9tEtrs9sVHk5ybHFk4POAJ4IY8wGngLOTndc4XOPAueH90tieWEsxBY+fwNwc3/5IY2f54D7lCTEM+L9S5K306j2M+mOLZyvFHgMeHqgvBK9ZXwLsruvc/ehrhR18BKt7t4J3ApcFrYInAPcHs73c+BNowzpsnA58S7vcuA+d28d5XoTFc9B6do+7v6Su28I7+8CaoCqUa43qt/vwyBx3g6cG26Py4Bb3b3D3TcDG8PlJTUed38k8h15mmAM3GSJZ/sM5A3AA+5e7+77gQeAC1IczzuAW0a5ThmF0eTl5EcXV050oIBgZ50P5AJ70x2XmR0D5Lj7AwDu3pzEfcewYgvjOwWYCtyfgphixsI+JWo0+5dkGmv7mWHFFvpP4H+A9ngWmvEFcpz6u0TrTGAy0ODu3X2mj8ZUd98d3t9DkCwGcwWH78y/Eh7i+F8zy09RPAVmttzMnrawuwdjYPuY2akEO6mXI5NHu30G+j70O0/4/g8QbI94XpuMeKLeD9wXedzfZ5eKeN4afg63m1nsAhZp3T7hIcH5wMORyYnePpIYyfiuxGPIHOTuTwGPELQ07gb+7O7r0h0XQUtog5n93syeN7NvmFl2kuOKKzYzywK+BXwyBfFEJWKfkkij2b8k02j3M8k0ZGxmdjIw293viXehY/ZS04lkZg8C0/p56nPuftdYiif6wN3dzAYch8/MpgPHE4xrGvMZgn/yPIKx/z4N/EcK4pnr7jvN7AjgYTNbTfBPO2wJ3j6/BK5y995w8rC3z0RiZlcCS4GzIpMP++zcPVnJP+YPwC3u3mFm/0jQGnJOktcZjyuA2929JzItHdtnwhtreTlqtDnIzI4k6CYSa0F7wMxe4+5/TWdcBPv81wAnAduA2wi6O/1kNHElKLaPAPe6+45EN4YmeZ8ifQywn0mb8MfX9QTf9bhlRIHs7ueNchEDXaK1Dqgws5zwV1xcl24dLB4z22tm0919d/jPWDPIot4O3OHuXZFlx34Jd5jZz4jj13gi4nH3neHfTWb2KEEC/h1p2j5mVgbcQ7CzfTqy7GFvn37Ec8ne2Dw7zCwHKCf4viTjcr9xLdPMziPYIZzl7h2x6QN8dqMpAIeMx93rIg9/TNAPMPbas/u89tFRxBJXPBFXAP8UnZCE7SMkNS+PWgJy0JuBp929OXzNfcDpwKgK5ATEtQNY6e6bwtfcCZxGAgrkBMR2OvAaM/sIQd/oPDNrdvdRn3yZzH1KEoxm/5JMo9rPpDm2UuA44NHwx9c04G4zu9Tdlw+0UHWxiE+/l2h1dyc4jHZ5ON9VwGhbPu4OlxPP8g7rKxn+g8f6/74JWJPseMysMtZVwcymAGcCa9O1fcLP6A7gF+5+e5/nErF94rlkbzTOy4GHw+1xN3CFBWchzwcWAs+OIIZhxWNmJwE/Ai5195rI9H4/uxTEMz3y8FIgdvj5z8Drw7gqgddz6BGSpMQTxnQUwYmBT0WmJWP7SGKk69LZ8eTobcBZZpZjZrkELWnJ7mIRT1x/m24q5gAAA0dJREFUI2i0iPWfPYfUfJ+HjM3d3+Xuc9x9HkHDxS8SURwnIrbB9ilJMJr9S1rjGmg/kwKDxubuB9x9irvPC79fT4cxDlgcx16Y0TeCX/o7gA6Ckyj+HE6fQXC4JzbfhcBLBC1Hn4tMP4KgwNkI/BbIH2U8k4GHgA3Ag8CkcPpS4MeR+eYR/ELK6vP6h4HVBIXfr4CSZMdDcMb2aoIzR1cD70/n9gGuBLqAlZHbkkRun/6+DwRdNS4N7xeE73dj+P6PiLz2c+HrXgTemKDv8VDxPBh+v2Pb4+6hPrskx/NV4IVwvY8AR0Ve+75wu20E3puKeMLH1wFf6/O6pGwf3Yb8vEaVl5McWzw5KJugUFhHUIBePxbiCh+fD6wKv883AXljJbbI/FeTulEsRrVPSVJMI96/JHlbjWg/MxZi6zPvo8QxioUuNS0iIiIiEqEuFiIiIiIiESqQRUREREQiVCCLiIiIiESoQBYRERERiVCBLCIiIiISoQJZMoaZXWdmqb6MqYhIRlPulfFIBbKIiIiISIQKZJnQzOxzZvaSmT0OLA6nLTCzP5nZCjP7a3gFNczsbWa2xsz+bmaPhdOyzeyb4fRVZvbRNL4dEZFxQblXxrucdAcgkixmdgrBJSeXEHzXnwNWADcCH3L3DWa2DPg+wSVXvwi8wd13mllFuJhrCK5auMTdu81sUorfhojIuKLcKxOBCmSZyF4D3OHurQBmdjfBJTrPAH5rZrH58sO/TwA3mdlvgN+H084Dfuju3QDuXp+i2EVExivlXhn3VCBLpskCGtx9Sd8n3P1DYavGRcCKsBVE/n+7dotbZRCGYfh+E0xFl4BiAayEtAZTJIJdsAG20A2wgoo6BAkhaVKgmqQWXzsIPvEpDEnLObkuNZmfZMa8eTIzAP9O7eWg+IPMMftUnc/MycycVq+qh+rnzLyumj9ebu0Xa60va6331a/qeXVdvZuZZ9scz3wAf6f2cvAEZI7WWuum+ljdVlfV123oTfV2Zm6ru+ps6/8wM99n5kf1eVt3Wd1X37b5F494BICDo/ZyDGat9dR7AACA/4YbZAAA2BGQAQBgR0AGAIAdARkAAHYEZAAA2BGQAQBgR0AGAICd3/pUTHZmwY1nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "OsIu6haoE4lm",
        "outputId": "0a662e15-e74c-4db4-af76-84a48b68e9c0"
      },
      "source": [
        "df4.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comp_id</th>\n",
              "      <th>ratio</th>\n",
              "      <th>duration</th>\n",
              "      <th>language</th>\n",
              "      <th>partner</th>\n",
              "      <th>partner_active</th>\n",
              "      <th>n_likes</th>\n",
              "      <th>views</th>\n",
              "      <th>n_tags</th>\n",
              "      <th>n_formats</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>hour</th>\n",
              "      <th>title</th>\n",
              "      <th>desc</th>\n",
              "      <th>image</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>86</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>290</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.113135</td>\n",
              "      <td>-0.129038</td>\n",
              "      <td>-0.024423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.33333</td>\n",
              "      <td>1129</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1443</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.107456</td>\n",
              "      <td>-0.129038</td>\n",
              "      <td>0.011685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1.77273</td>\n",
              "      <td>2612</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>-0.110706</td>\n",
              "      <td>-0.053555</td>\n",
              "      <td>-0.002058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2233</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.117400</td>\n",
              "      <td>-0.129038</td>\n",
              "      <td>0.028751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>1510</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>-0.266918</td>\n",
              "      <td>0.024060</td>\n",
              "      <td>0.017698</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   comp_id    ratio  duration  language  ...  hour     title      desc     image\n",
              "0        1  1.77778        86         3  ...     6  0.113135 -0.129038 -0.024423\n",
              "1        2  1.33333      1129         2  ...     6  0.107456 -0.129038  0.011685\n",
              "4        5  1.77273      2612         2  ...     6 -0.110706 -0.053555 -0.002058\n",
              "6        7  1.77778        24         2  ...     6  0.117400 -0.129038  0.028751\n",
              "8        9  1.77778      1510         2  ...     6 -0.266918  0.024060  0.017698\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iE08bUukFJUa"
      },
      "source": [
        "inputs = df4[cat+num]\n",
        "target = np.log(df4.views)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDPgyJXbF8sm"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Create a scaler object\n",
        "scaler = StandardScaler()\n",
        "# Fit the inputs (calculate the mean and standard deviation feature-wise)\n",
        "scaler.fit(inputs)\n",
        "# Scale the features and store them in a new variable (the actual scaling procedure)\n",
        "inputs_scaled = scaler.transform(inputs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "uKY4fOJ7GCCM",
        "outputId": "0246be41-985b-425b-df54-d155451ede8a"
      },
      "source": [
        "inputs_scaled=pd.DataFrame(data=inputs_scaled,columns=inputs.columns)\n",
        "inputs_scaled.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>partner</th>\n",
              "      <th>partner_active</th>\n",
              "      <th>language</th>\n",
              "      <th>n_likes</th>\n",
              "      <th>n_tags</th>\n",
              "      <th>n_formats</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>hour</th>\n",
              "      <th>ratio</th>\n",
              "      <th>duration</th>\n",
              "      <th>title</th>\n",
              "      <th>desc</th>\n",
              "      <th>image</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.81295</td>\n",
              "      <td>-0.567225</td>\n",
              "      <td>0.060953</td>\n",
              "      <td>-0.186616</td>\n",
              "      <td>-0.211253</td>\n",
              "      <td>-3.474421</td>\n",
              "      <td>0.488728</td>\n",
              "      <td>-0.296824</td>\n",
              "      <td>0.518641</td>\n",
              "      <td>-0.476507</td>\n",
              "      <td>1.194889</td>\n",
              "      <td>-0.890721</td>\n",
              "      <td>-1.355517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.81295</td>\n",
              "      <td>-0.567225</td>\n",
              "      <td>-0.473781</td>\n",
              "      <td>-0.186616</td>\n",
              "      <td>-0.784678</td>\n",
              "      <td>-1.765748</td>\n",
              "      <td>0.488728</td>\n",
              "      <td>-0.296824</td>\n",
              "      <td>-0.967343</td>\n",
              "      <td>1.388190</td>\n",
              "      <td>1.144467</td>\n",
              "      <td>-0.890721</td>\n",
              "      <td>0.700525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.81295</td>\n",
              "      <td>-0.567225</td>\n",
              "      <td>-0.473781</td>\n",
              "      <td>2.968292</td>\n",
              "      <td>-0.784678</td>\n",
              "      <td>-3.474421</td>\n",
              "      <td>0.488728</td>\n",
              "      <td>-0.296824</td>\n",
              "      <td>0.501757</td>\n",
              "      <td>4.039528</td>\n",
              "      <td>-0.792597</td>\n",
              "      <td>-0.272672</td>\n",
              "      <td>-0.082039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.81295</td>\n",
              "      <td>-0.567225</td>\n",
              "      <td>-0.473781</td>\n",
              "      <td>-0.186616</td>\n",
              "      <td>-0.402395</td>\n",
              "      <td>-0.911411</td>\n",
              "      <td>0.488728</td>\n",
              "      <td>-0.296824</td>\n",
              "      <td>0.518641</td>\n",
              "      <td>-0.587352</td>\n",
              "      <td>1.232762</td>\n",
              "      <td>-0.890721</td>\n",
              "      <td>1.672290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.81295</td>\n",
              "      <td>-0.567225</td>\n",
              "      <td>-0.473781</td>\n",
              "      <td>-0.186616</td>\n",
              "      <td>-0.784678</td>\n",
              "      <td>0.797262</td>\n",
              "      <td>0.488728</td>\n",
              "      <td>-0.296824</td>\n",
              "      <td>0.518641</td>\n",
              "      <td>2.069350</td>\n",
              "      <td>-2.179611</td>\n",
              "      <td>0.362832</td>\n",
              "      <td>1.042923</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   partner  partner_active  language  ...     title      desc     image\n",
              "0  0.81295       -0.567225  0.060953  ...  1.194889 -0.890721 -1.355517\n",
              "1  0.81295       -0.567225 -0.473781  ...  1.144467 -0.890721  0.700525\n",
              "2  0.81295       -0.567225 -0.473781  ... -0.792597 -0.272672 -0.082039\n",
              "3  0.81295       -0.567225 -0.473781  ...  1.232762 -0.890721  1.672290\n",
              "4  0.81295       -0.567225 -0.473781  ... -2.179611  0.362832  1.042923\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDQvAoNuGFk8"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the variables with an 80-20 split and some random state\n",
        "# To have the same split as mine, use random_state = 365\n",
        "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled, target, test_size=0.2, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "et4bfCd8GXmJ"
      },
      "source": [
        "#prepare our data \n",
        "feature_columns=inputs.columns\n",
        "x_train = pd.DataFrame(data=x_train, columns=feature_columns)\n",
        "x_test = pd.DataFrame(data=x_test, columns=feature_columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4NLJhCPaGfU5",
        "outputId": "cccfa19d-2f2d-4283-9197-4abf2591d949"
      },
      "source": [
        "sns.set(style=\"white\")\n",
        "\n",
        "\n",
        "# Compute the correlation matrix\n",
        "corr = x_train.corr()\n",
        "\n",
        "# Generate a mask for the upper triangle\n",
        "mask = np.zeros_like(corr, dtype=np.bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(25, 25))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=2, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\":.5})\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1800 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "po7B_QrKGiy3",
        "outputId": "28f6a9bb-0890-4429-d0f3-1e58b264e979"
      },
      "source": [
        "# Create correlation matrix\n",
        "corr_matrix = x_train.corr().abs()\n",
        "\n",
        "# Select upper triangle of correlation matrix\n",
        "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
        "# Find index of feature columns with correlation greater than 0.9\n",
        "to_drop = [column for column in upper.columns if any(upper[column] > 0.9)]\n",
        "x_train.drop(columns=to_drop, inplace=True)\n",
        "x_test.drop(columns=to_drop, inplace=True)\n",
        "\n",
        "len(x_train.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zx8v7Km5G4Wu"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUXWmaanHKcp"
      },
      "source": [
        "n_estimators = list(range(3,202,10))\n",
        "\n",
        "# Maximum number of levels in tree\n",
        "max_depth = list(range(2,50))\n",
        "# Minimum number of samples required to split a node\n",
        "max_features = ['sqrt']\n",
        "min_samples_split = list(range(2,30))\n",
        "# Minimum number of samples required at each leaf node\n",
        "min_samples_leaf = list(range(2,30))\n",
        "# Method of selecting samples for training each tree\n",
        "bootstrap = [True, False]\n",
        "# Create the random grid\n",
        "random_grid = {'n_estimators': n_estimators,\n",
        "                'max_depth': max_depth,\n",
        "                'max_features': max_features,\n",
        "                'min_samples_split': min_samples_split,\n",
        "                'min_samples_leaf': min_samples_leaf,\n",
        "                'bootstrap': bootstrap\n",
        "              }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NR6iBTNpHO8f",
        "outputId": "c62674a2-ee29-4942-ab5e-5018d65fcd69"
      },
      "source": [
        "# Use the random grid to search for best hyperparameters\n",
        "# First create the base model to tune\n",
        "rf = RandomForestRegressor()\n",
        "# Random search of parameters, using 50 fold cross validation and use all available cores\n",
        "rf_random = RandomizedSearchCV(rf,random_grid,cv=5,n_iter=100,verbose=1, random_state=7, n_jobs = -1)\n",
        "# Fit the random search model\n",
        "rf_random.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   11.7s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   38.0s\n",
            "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  1.5min\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:  1.7min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score=nan,\n",
              "                   estimator=RandomForestRegressor(bootstrap=True,\n",
              "                                                   ccp_alpha=0.0,\n",
              "                                                   criterion='mse',\n",
              "                                                   max_depth=None,\n",
              "                                                   max_features='auto',\n",
              "                                                   max_leaf_nodes=None,\n",
              "                                                   max_samples=None,\n",
              "                                                   min_impurity_decrease=0.0,\n",
              "                                                   min_impurity_split=None,\n",
              "                                                   min_samples_leaf=1,\n",
              "                                                   min_samples_split=2,\n",
              "                                                   min_weight_fraction_leaf=0.0,\n",
              "                                                   n_estimators=100,\n",
              "                                                   n_jobs=None, oob_score=Fals...\n",
              "                                        'min_samples_leaf': [2, 3, 4, 5, 6, 7,\n",
              "                                                             8, 9, 10, 11, 12,\n",
              "                                                             13, 14, 15, 16, 17,\n",
              "                                                             18, 19, 20, 21, 22,\n",
              "                                                             23, 24, 25, 26, 27,\n",
              "                                                             28, 29],\n",
              "                                        'min_samples_split': [2, 3, 4, 5, 6, 7,\n",
              "                                                              8, 9, 10, 11, 12,\n",
              "                                                              13, 14, 15, 16,\n",
              "                                                              17, 18, 19, 20,\n",
              "                                                              21, 22, 23, 24,\n",
              "                                                              25, 26, 27, 28,\n",
              "                                                              29],\n",
              "                                        'n_estimators': [3, 13, 23, 33, 43, 53,\n",
              "                                                         63, 73, 83, 93, 103,\n",
              "                                                         113, 123, 133, 143,\n",
              "                                                         153, 163, 173, 183,\n",
              "                                                         193]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=7, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxvKf6YoHZyC",
        "outputId": "ed840dbc-b17f-4f2f-aff3-c6e31b3e5af6"
      },
      "source": [
        "# We can view the best parameters from fitting the random search:\n",
        "rf_random.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': False,\n",
              " 'max_depth': 41,\n",
              " 'max_features': 'sqrt',\n",
              " 'min_samples_leaf': 2,\n",
              " 'min_samples_split': 9,\n",
              " 'n_estimators': 133}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "esA5yLFNH9Xe",
        "outputId": "f0945577-d55c-4a74-e7d8-258b46462ef5"
      },
      "source": [
        "# Random Forest model with best parameters\n",
        "rfr = RandomForestRegressor(n_estimators=rf_random.best_params_[\"n_estimators\"],\n",
        "                           max_features=rf_random.best_params_[\"max_features\"],\n",
        "                           max_depth=rf_random.best_params_[\"max_depth\"], \n",
        "#                          min_samples_split=rf_random.best_params_[\"min_samples_split\"],\n",
        "#                          min_samples_leaf=rf_random.best_params_[\"min_samples_leaf\"],\n",
        "                           bootstrap=rf_random.best_params_['bootstrap'],\n",
        "                           n_jobs=-1, \n",
        "                           random_state=7)\n",
        "rfr.fit(inputs_scaled,target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=41, max_features='sqrt', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=133, n_jobs=-1, oob_score=False,\n",
              "                      random_state=7, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ROCVuCkYIKQs",
        "outputId": "529037f7-4e66-4111-c91d-b39f69a11ad0"
      },
      "source": [
        "#Scores for Train data and test data\n",
        "rf_y_train_pre = rfr.predict(x_train)\n",
        "print('Accuracy score of train data :{}'.format(rfr.score(x_train,y_train)))\n",
        "rf_y_test_pre = rfr.predict(x_test)\n",
        "print('Accuracy score of validation data :{}'.format(rfr.score(x_test,y_test)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy score of train data :0.9998361589990935\n",
            "Accuracy score of validation data :0.9999999999981277\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRqJ7Gr4IV6d"
      },
      "source": [
        "\n",
        "#RMSE\n",
        "def RMSE(actual,prediction):\n",
        "    rmse=np.sqrt(mean_squared_error(actual, prediction))\n",
        "    return rmse"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sQFvES3E8YX",
        "outputId": "ebd3b33a-7674-4a22-d439-0a7df9d9d06e"
      },
      "source": [
        "print('RMSE of Random Forest Regressor - {}'.format(RMSE(y_test,rf_y_test_pre)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE of Random Forest Regressor - 2.011191439192569e-06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "eXny9TLsIdrL",
        "outputId": "2ccfefca-7bd4-41f8-87bc-0b5b0eb34b03"
      },
      "source": [
        "# distribution of residual\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.subplot(1,2,1)\n",
        "sns.distplot(np.exp(y_test)-np.exp(rf_y_test_pre),color='g')\n",
        "plt.title('Distribution of  Residual', fontsize=15)\n",
        "plt.xlabel('Distribution')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.scatter(np.exp(y_test), np.exp(rf_y_test_pre),color='r',alpha=0.6)\n",
        "plt.plot(np.exp(y_test),np.exp(y_test))\n",
        "plt.title('Prediction VS Actual Values',fontsize=15)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-R3OSj-IhnP",
        "outputId": "cddf26b7-ee10-45f6-aab4-8c2e803634d7"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV \n",
        "from sklearn.svm import SVR\n",
        "\n",
        "\n",
        "\n",
        "# defining parameter range \n",
        "param_grid = {'C': [0.1, 1, 10, 100, 1000],  \n",
        "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001], \n",
        "              'kernel': ['rbf']}  \n",
        "  \n",
        "grid = GridSearchCV(SVR(), param_grid, refit = True, verbose = 3) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ......... C=0.1, gamma=1, kernel=rbf, score=-0.008, total=   0.2s\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.093, total=   0.2s\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.058, total=   0.2s\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.066, total=   0.2s\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.044, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.054, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.152, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.167, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.198, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.152, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ...... C=0.1, gamma=0.01, kernel=rbf, score=-0.012, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.072, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.076, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.102, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.072, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ..... C=0.1, gamma=0.001, kernel=rbf, score=-0.058, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.019, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ..... C=0.1, gamma=0.001, kernel=rbf, score=-0.005, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.011, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.006, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] .... C=0.1, gamma=0.0001, kernel=rbf, score=-0.075, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] .... C=0.1, gamma=0.0001, kernel=rbf, score=-0.001, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] .... C=0.1, gamma=0.0001, kernel=rbf, score=-0.028, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] .... C=0.1, gamma=0.0001, kernel=rbf, score=-0.016, total=   0.2s\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] .... C=0.1, gamma=0.0001, kernel=rbf, score=-0.019, total=   0.2s\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.089, total=   0.2s\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.202, total=   0.2s\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.147, total=   0.2s\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.152, total=   0.2s\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.153, total=   0.2s\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.156, total=   0.2s\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.171, total=   0.2s\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.198, total=   0.2s\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.299, total=   0.2s\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.251, total=   0.2s\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.054, total=   0.2s\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.130, total=   0.2s\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.173, total=   0.2s\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.237, total=   0.2s\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.178, total=   0.2s\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ....... C=1, gamma=0.001, kernel=rbf, score=-0.007, total=   0.2s\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.062, total=   0.2s\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.065, total=   0.2s\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.099, total=   0.2s\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.078, total=   0.2s\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ...... C=1, gamma=0.0001, kernel=rbf, score=-0.056, total=   0.2s\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.020, total=   0.2s\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ...... C=1, gamma=0.0001, kernel=rbf, score=-0.004, total=   0.2s\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.012, total=   0.2s\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.008, total=   0.2s\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.077, total=   0.3s\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.119, total=   0.4s\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.066, total=   0.4s\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.100, total=   0.4s\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.105, total=   0.3s\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.151, total=   0.4s\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.102, total=   0.4s\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.069, total=   0.4s\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.226, total=   0.4s\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.245, total=   0.4s\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.091, total=   0.2s\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.137, total=   0.2s\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.215, total=   0.2s\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.286, total=   0.2s\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.257, total=   0.2s\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.014, total=   0.2s\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.063, total=   0.2s\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.105, total=   0.2s\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.160, total=   0.2s\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.120, total=   0.2s\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ..... C=10, gamma=0.0001, kernel=rbf, score=-0.011, total=   0.2s\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.060, total=   0.2s\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.063, total=   0.2s\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.097, total=   0.2s\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.076, total=   0.2s\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.017, total=   0.5s\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.076, total=   0.4s\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] ......... C=100, gamma=1, kernel=rbf, score=-0.014, total=   0.5s\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.076, total=   0.5s\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.073, total=   0.5s\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ....... C=100, gamma=0.1, kernel=rbf, score=-0.036, total=   1.9s\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ....... C=100, gamma=0.1, kernel=rbf, score=-0.300, total=   1.9s\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ....... C=100, gamma=0.1, kernel=rbf, score=-0.275, total=   2.3s\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ....... C=100, gamma=0.1, kernel=rbf, score=-0.191, total=   2.2s\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ....... C=100, gamma=0.1, kernel=rbf, score=-0.183, total=   2.0s\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.096, total=   0.6s\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.098, total=   0.6s\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.184, total=   0.6s\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.278, total=   0.6s\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.271, total=   0.6s\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.056, total=   0.3s\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.112, total=   0.3s\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.180, total=   0.3s\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.247, total=   0.2s\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.184, total=   0.3s\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] .... C=100, gamma=0.0001, kernel=rbf, score=-0.035, total=   0.2s\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.032, total=   0.2s\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.086, total=   0.2s\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.142, total=   0.2s\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.093, total=   0.2s\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ........ C=1000, gamma=1, kernel=rbf, score=-0.007, total=   0.6s\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.035, total=   0.6s\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ........ C=1000, gamma=1, kernel=rbf, score=-0.308, total=   0.6s\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ........ C=1000, gamma=1, kernel=rbf, score=-0.042, total=   0.6s\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ........ C=1000, gamma=1, kernel=rbf, score=-0.121, total=   0.5s\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ...... C=1000, gamma=0.1, kernel=rbf, score=-0.791, total=  10.8s\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ...... C=1000, gamma=0.1, kernel=rbf, score=-1.399, total=  11.7s\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ...... C=1000, gamma=0.1, kernel=rbf, score=-1.458, total=  10.7s\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ...... C=1000, gamma=0.1, kernel=rbf, score=-0.891, total=  13.1s\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ...... C=1000, gamma=0.1, kernel=rbf, score=-1.204, total=  10.2s\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.017, total=   4.0s\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ..... C=1000, gamma=0.01, kernel=rbf, score=-0.246, total=   4.3s\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.044, total=   4.2s\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.164, total=   4.5s\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.152, total=   4.9s\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.077, total=   0.6s\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.099, total=   0.7s\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.219, total=   0.7s\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.264, total=   0.7s\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.223, total=   0.6s\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.005, total=   0.3s\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.049, total=   0.3s\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.109, total=   0.3s\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.163, total=   0.3s\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.120, total=   0.3s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 125 out of 125 | elapsed:  2.0min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
              "                           epsilon=0.1, gamma='scale', kernel='rbf',\n",
              "                           max_iter=-1, shrinking=True, tol=0.001,\n",
              "                           verbose=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
              "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         'kernel': ['rbf']},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZ3-VcivLg-T",
        "outputId": "f4cd158d-967c-4832-e495-bfcda01c5df4"
      },
      "source": [
        "grid.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8eH0CwYDMnZi",
        "outputId": "b6c4ecc2-b291-4588-f1dd-6fa33faf92b7"
      },
      "source": [
        "grid.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.1,\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lt6ZbO_uMHxK",
        "outputId": "3148e606-0ae6-4300-f41a-844cb8e872dc"
      },
      "source": [
        "# Random Forest model with best parameters\n",
        "svr1= SVR(C=1, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.1,\n",
        "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)\n",
        "svr1.fit(inputs_scaled,target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.1,\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kV6BSVTqMyC2",
        "outputId": "c8eddb0b-ac93-4fc8-c1d7-4ebf19d0d15c"
      },
      "source": [
        "sv_y_train_pre=svr1.predict(x_train)\n",
        "print('Accuracy score of train data :{}'.format(svr1.score(x_train,y_train)))\n",
        "sv_y_test_pre=svr1.predict(x_test)\n",
        "print('Accuracy score of validation data :{}'.format(svr1.score(x_test,y_test)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy score of train data :0.40101663213218586\n",
            "Accuracy score of validation data :0.38789775066921983\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vzNri7OgSxfR",
        "outputId": "ed32b534-df6b-4548-928c-3454c7b255f9"
      },
      "source": [
        "%%time\n",
        "\n",
        "import xgboost as xgb\n",
        "import matplotlib.pyplot as plt\n",
        "from xgboost import plot_importance, plot_tree\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "\n",
        "parameters = {\n",
        "    'n_estimators': [100, 200, 300, 400],\n",
        "    'learning_rate': [0.001, 0.005, 0.01, 0.05],\n",
        "    'max_depth': [8, 10, 12, 15],\n",
        "    'gamma': [0.001, 0.005, 0.01, 0.02],\n",
        "    'random_state': [42]\n",
        "}\n",
        "\n",
        "eval_set = [(x_train, y_train)]\n",
        "model = xgb.XGBRegressor(eval_set=eval_set, objective='reg:squarederror', verbose=False)\n",
        "clf = GridSearchCV(model, parameters)\n",
        "\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "print(f'Best params: {clf.best_params_}')\n",
        "print(f'Best validation score = {clf.best_score_}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best params: {'gamma': 0.001, 'learning_rate': 0.05, 'max_depth': 8, 'n_estimators': 100, 'random_state': 42}\n",
            "Best validation score = 0.2583365867471545\n",
            "CPU times: user 20min 15s, sys: 9.68 s, total: 20min 25s\n",
            "Wall time: 20min 27s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4i3iAUtiZnBz",
        "outputId": "d7c23ab7-b026-4854-baca-2b24910c5c18"
      },
      "source": [
        "%%time\n",
        "\n",
        "model = xgb.XGBRegressor(**clf.best_params_, objective='reg:squarederror')\n",
        "model.fit(inputs_scaled,target, eval_set=eval_set, verbose=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 582 ms, sys: 2.97 ms, total: 585 ms\n",
            "Wall time: 598 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h3KHUoSbaRGc",
        "outputId": "fc4ebc55-ccd4-4e7a-a874-f7185a69438d"
      },
      "source": [
        "mod_y_train_pre=model.predict(x_train)\n",
        "print('Accuracy score of train data :{}'.format(model.score(x_train,y_train)))\n",
        "mod_y_test_pre=model.predict(x_test)\n",
        "print('Accuracy score of validation data :{}'.format(model.score(x_test,y_test)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy score of train data :0.7299408861968775\n",
            "Accuracy score of validation data :0.7199461272830929\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tfnZBZC5aq_7",
        "outputId": "fbf94db3-42c7-4882-a5a7-a4bc27d1d000"
      },
      "source": [
        "#RMSE\n",
        "def RMSE(actual,prediction):\n",
        "    rmse=np.sqrt(mean_squared_error(actual, prediction))\n",
        "    return rmse\n",
        "print('RMSE - {}'.format(RMSE(y_test,mod_y_test_pre)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE - 0.7778162011801297\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-X71_rcjNFwP",
        "outputId": "4d1be040-a96d-4c31-81c2-30ba13c607df"
      },
      "source": [
        "df_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(986, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "BJqmigJxN8qh",
        "outputId": "a74ec297-182b-47c1-dd7c-7e154e8f0046"
      },
      "source": [
        "test_df = df_test[cat+num]\n",
        "test_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>partner</th>\n",
              "      <th>partner_active</th>\n",
              "      <th>language</th>\n",
              "      <th>n_likes</th>\n",
              "      <th>n_tags</th>\n",
              "      <th>n_formats</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>hour</th>\n",
              "      <th>ratio</th>\n",
              "      <th>duration</th>\n",
              "      <th>title</th>\n",
              "      <th>desc</th>\n",
              "      <th>image</th>\n",
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              "      <td>0.021999</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.004049</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
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              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>1.33333</td>\n",
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              "      <td>0.073919</td>\n",
              "      <td>-0.007975</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   partner  partner_active  language  ...     title      desc     image\n",
              "0    False           False         2  ...  0.058258  0.073919  0.033200\n",
              "1    False           False         2  ...  0.052847  0.073919  0.021999\n",
              "2    False           False         2  ...  0.031518  0.073919 -0.020418\n",
              "3     True            True         2  ...  0.024948  0.007918  0.004049\n",
              "4    False           False         2  ...  0.071102  0.073919 -0.007975\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtGSPBWMOIi1",
        "outputId": "3850c814-45ea-43c1-fe9b-a7675b5e66b5"
      },
      "source": [
        "test_df['partner'] = test_df['partner'].replace({True: 1, False: 0})\n",
        "test_df['partner_active'] = test_df['partner_active'].replace({True: 1, False: 0})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "bVVYIyv4ORa0",
        "outputId": "c0a60aa7-3b96-43c0-c162-9fc021a46df0"
      },
      "source": [
        "test_df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>n_tags</th>\n",
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              "      <td>6</td>\n",
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              "      <td>0.071102</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>46</td>\n",
              "      <td>0.248127</td>\n",
              "      <td>0.062650</td>\n",
              "      <td>0.005859</td>\n",
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              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>1.33333</td>\n",
              "      <td>91</td>\n",
              "      <td>-0.088227</td>\n",
              "      <td>-0.024695</td>\n",
              "      <td>0.017360</td>\n",
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              "    <tr>\n",
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              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>1.77778</td>\n",
              "      <td>72</td>\n",
              "      <td>0.145461</td>\n",
              "      <td>0.084659</td>\n",
              "      <td>0.021877</td>\n",
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              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>1.33333</td>\n",
              "      <td>82</td>\n",
              "      <td>-0.145118</td>\n",
              "      <td>-0.151111</td>\n",
              "      <td>-0.037762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>985</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>1.34091</td>\n",
              "      <td>88</td>\n",
              "      <td>-0.101057</td>\n",
              "      <td>-0.100476</td>\n",
              "      <td>0.000421</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>986 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     partner  partner_active  language  ...     title      desc     image\n",
              "0          0               0         2  ...  0.058258  0.073919  0.033200\n",
              "1          0               0         2  ...  0.052847  0.073919  0.021999\n",
              "2          0               0         2  ...  0.031518  0.073919 -0.020418\n",
              "3          1               1         2  ...  0.024948  0.007918  0.004049\n",
              "4          0               0         2  ...  0.071102  0.073919 -0.007975\n",
              "..       ...             ...       ...  ...       ...       ...       ...\n",
              "981        1               1         3  ...  0.248127  0.062650  0.005859\n",
              "982        0               0         2  ... -0.088227 -0.024695  0.017360\n",
              "983        1               1         3  ...  0.145461  0.084659  0.021877\n",
              "984        0               0         7  ... -0.145118 -0.151111 -0.037762\n",
              "985        0               0         2  ... -0.101057 -0.100476  0.000421\n",
              "\n",
              "[986 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ft6XgpD4OTT1"
      },
      "source": [
        "# Create a scaler object\n",
        "scaler = StandardScaler()\n",
        "# Fit the inputs (calculate the mean and standard deviation feature-wise)\n",
        "scaler.fit(test_df[x_train.columns])\n",
        "# Scale the features and store them in a new variable (the actual scaling procedure)\n",
        "scaled_test_input = scaler.transform(test_df[x_train.columns])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6khkiUNOb0w"
      },
      "source": [
        "# Now use the model to predict\n",
        "test_df_prediction = rfr.predict(scaled_test_input)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "3HLqZhdIOhqi",
        "outputId": "0fa12ca9-ba0d-4cf0-a85d-cf9ab68b10d4"
      },
      "source": [
        "submission=pd.DataFrame()\n",
        "submission['comp_id']=df_test['comp_id']\n",
        "submission['views']=np.exp(test_df_prediction)\n",
        "submission.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comp_id</th>\n",
              "      <th>views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3001</td>\n",
              "      <td>147.717578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3002</td>\n",
              "      <td>87.943873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3003</td>\n",
              "      <td>96.021273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3004</td>\n",
              "      <td>489.078390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3005</td>\n",
              "      <td>124.595612</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   comp_id       views\n",
              "0     3001  147.717578\n",
              "1     3002   87.943873\n",
              "2     3003   96.021273\n",
              "3     3004  489.078390\n",
              "4     3005  124.595612"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBP5Dpj2FtCd"
      },
      "source": [
        "submission['views'] = submission['views'].astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "NRGT7scaGQkP",
        "outputId": "9075e45f-e9a9-4375-ad1b-de5526c4a515"
      },
      "source": [
        "submission.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comp_id</th>\n",
              "      <th>views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3001</td>\n",
              "      <td>147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3002</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3003</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3004</td>\n",
              "      <td>489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3005</td>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   comp_id  views\n",
              "0     3001    147\n",
              "1     3002     87\n",
              "2     3003     96\n",
              "3     3004    489\n",
              "4     3005    124"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXQjkm8fOpcq"
      },
      "source": [
        "submission.to_csv('vp_challenge_11.csv',index=False,header=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nywk0ZXGGVYK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}