internship first project (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NAME: Sramana Routh\n",
    "\n",
    "# Data Science & Business Analytics Tasks\n",
    "\n",
    "\n",
    "# TASK 1:\n",
    "\n",
    "\n",
    "\n",
    "# TOPIC:\n",
    "\n",
    "## Predict the percentage of marks of an student based on the number of study hours \n",
    "\n",
    "# AIM OF THE PROJECT:\n",
    "### To predict the score of a student who studies for 9.25 hrs/ day and 8 hrs/day. We will predict the percentage of marks that a student is expected to score based upon the number of hours they studied.\n",
    "\n",
    "\n",
    "\n",
    "# STEPS:\n",
    "\n",
    "### 1.Here we will see the Python Scikit-Learn library for machine learning which can be used to implement regression functions. This is a simple linear regression task as it involves just 2 variables hours and scores.\n",
    "### 2. Then We will prepare the data so that we have our attributes and labels.\n",
    "### 3. The next step is to split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split() method\n",
    "### 4. The next step is to ceate a test set. We have to split 80% of the data to training set while 20% of the data to test set. The test_size variable is where we actually specify the proportion of test set ( 0.2  sets 20% of the data for test set)\n",
    "### 5.The next step is to train our algorithm\n",
    "### 6.The next step is to plot our linear regression hypothesis with our training data, Here we plot scatter plot of x_train vs y_train, and our hypothesis based on the training data.\n",
    "### 7. At the end we evaluate our hypothesis on the test set and calculate the error margins in our hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours  Scores\n",
       "0     2.5      21\n",
       "1     5.1      47\n",
       "2     3.2      27\n",
       "3     8.5      75\n",
       "4     3.5      30\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here we plot the 2 variables 'hours' and 'scores' using a Scatter-Plot to observe a relationship between the two mentioned variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Graph of Hours vs scores')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAc8AAAGoCAYAAAAgiW7JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3RU9aH+/2fPhIRJSIg5oMtDohJFxbScChQvBYQK5eINTUAuBSlWC4eDBqxCCBAhRkBp1GIVdB2rXzBgfg2oVY8uimhUNGUVtRTBVQExiYBFQHIZk8nM/v1hiSKTODvMzp7MvF9rsZbZs7M/T3bUh8++GqZpmgIAACFzOR0AAICOhvIEAMAiyhMAAIsoTwAALKI8AQCwKM7pAC35179qQl63S5cE1dY22JgmdGQJjizBkSW4aM/SvXtyWLeH9hcVM8+4OLfTEZqRJTiyBEeW4MiCSBcV5QkAQHuK2MO2p6Ozt1auutAP+4aTUdNJiQ0+R8b+vvbOEkhK1teeLu02HgA4JSrL01VXo8ArrzozeGK8AvWNzoz9fe2cxTV6pER5AogBHLYFAMAiyhMAAIsoTwAALKI8AQCwiPIEAMAiyhMAAIsoTwAALKI8AQCwKCofkgDEmnA8VSuWn47Vmtay8FSt2EV5AlEgLE/ViuGnY7WqlSw8VSt2cdgWAACLKE8AACyiPAEAsIjyBADAIi4YQtjEKaDEwweCftZRrp5sb+HKEufzKUIurwFiAuWJsDHq6xV44+3gH3aQqyfbXZiyGEMGhiEMgFBx2BYAAIsoTwAALKI8AQCwiPIEAMAiyhMAAIsoTwAALKI8AQCwiPIEAMAiyhMAAIsoTwAALKI8AQCwiPIEAMAiyhMAAIsoTwAALKI8AQCwiPIEAMAiyhMAAIsoTwAALKI8AQCwiPIEAMAiyhMAAIsoTwAALKI8AQCwiPIEAMAiyhMAAIsoTwAALKI8AQCwiPIEAMAiyhMAAIsoTwAALKI8AQCwiPIEAMAiyhNAzHth90e6ZeP/p7GlJbph3Rr998sv6B9fHAzrGEVF96qkZM1pbWPDhg0aMmSIbr311lM+u+iii3TkyJFT1v/Nb35zWmMiuDinAwCAk/7w13e1/cABLRs2Qmcnp0iStlVXKffVl7X2pnHqmfgfDif81vPPP6/Zs2frhhtucDpKzIvY8uzePbnt63dPlnpfGOZEobOW3F7tnSW+X98WP4vl/dKacGVpbd+HKhr3S2sOHz6sdf/vf7Vp0yadeeaZzct/Linv+Svl+vGPFXf++brh5z9Xnz599PHHH2vOnDmKi4vT6tWr1fjOGzpy5IjGjBmj3NxcVVRUaMWKFfrP//xP7d27V507d9ayZct0/vnnq3PnTvrnPz/SrFm36fDhw+rVq5d+97vfKTEx8aRMNTU1Wrx4sXbv3i3DMDRo0CDNmTNHDzzwgHbs2KGqqiodPXpUU6dOtfSztrTduLg4XXTRRXr33XeVlpYmSc1f//Of/1RRUZESExNVV1enkpIS5efna//+/XK5XMrKytKSJUvkcn17EPPtt9/W8uXL9ec//1mSdPz4cV199dX6y1/+opdfflnr169Xp06dlJCQoCVLluiCCy44KeeePXuUn5+vxsZGmaapnJwcTZo0SU1NTXrwwQf1xhtvyO1269JLL1VBQYEMw9CyZcv07rvvyu12q0+fPsrLy1OXLl308+/93vr06aMlS5bowIED8vl8uuaaazR9+nQ1NTWpsLBQ27dvV6dOnZSenq6lS5cqKSmp5R1qAkCM2rRpk3njjTf+4HpDhw41H330UdM0TTMQCJi//OUvzX379pmmaZoHDx40e/fubX755Zfme++9Z1588cXmtm3bTNM0zZKSkubtz50718zJyTHr6+vNpqYm88YbbzQ3btx4ylj33HOPWVhYaAYCAbOhocGcNm2auXr1atM0TfOXv/yl+X//939BM1544YXmtddea15//fXNf6666irz9ttv/8HtXnjhheaXX3550ra++/NUVVWZpmmaGzduNKdNm2aapmk2NTWZ+fn55qeffnpSjkAgYA4dOtT8+9//bpqmaT777LPmXXfdZTY1NZlZWVnmoUOHmre1fv36U36OvLy85lxffPGFmZuba/r9fvOZZ54xJ02aZHq9XtPv95t33nmnuXHjRvORRx4x/+d//sdsbGw0/X6/OW/ePHPhwoWn/N5M0zQnT55sbt682TRN0/z666/NyZMnmy+//LK5bds2c+TIkWYgEDBN0zQfeOAB829/+1vQ/XxCxM48AcBupmme9HVtba0mTZokSaqvr9eoUaM0Z84cSVL//v0lSYZhaNWqVXrjjTf00ksvac+ePTJNU16vV5J08cUXN6+bnZ2tJUuW6OjRo5KkYcOGyePxSJJ69ep1yjlKSSovL9e6detkGIbi4+M1fvx4PfPMM7r99tt/8Od55plnmmeP0jfnPF977bXT2u7ZZ5+tHj16SJL69eunhx56SJMnT9aVV16pW265Reeee+5J6xuGoezsbG3cuFE//vGPtWHDBt1zzz1yu90aOXKkxo8fryFDhmjgwIG66qqrThlv+PDhmjt3rv7+97/riiuu0IIFC+RyubR161bdcMMN6ty5syTp4YcfliTl5ORo9uzZ6tSpkyRp8uTJmjlzZvP2Tvwu6uvrtW3bNn311Vd65JFHmpft3r1bAwcOlNvt1tixYzVw4ECNGDFCffr0aXW/cMEQgJjVp08f7du3r7ncunTpohdeeEEvvPCCrr/+etXW1jave+Lwan19vW688Ubt3LlTl1xyie655x7FxcU1F7Hb7T5lnBPL4uK+na8YhnFKeUtSIBCQYRgnfd3U1HTaP2uo221sbDzp6+8eVs7IyNCmTZt0++23q7a2Vr/61a/0+uuvn7KNnJwcvfrqq9q1a5dqamo0YMAASdKKFSu0atUqnXPOOXriiSea/2LyXUOHDtVrr72mUaNGadeuXbruuut08ODBk/ad9M0h9y+++CLoz+Xz+U7JHwgEZJqm1q9f3/w7fu655/Sb3/xGKSkpeuGFFzR37ly53W7l5ubq2WefbXV/Up4AYtZZZ52lKVOm6M4779Tnn3/evLy6ulrbt28/6VzeCfv371dtba1yc3P185//XBUVFWpsbFQgEJAk7d69W7t375YkPffcc7r00kuVkpIScqaBAwdq7dq1Mk1TjY2NKi0t1ZVXXnmaP2nr201LS9OOHTskSS+99FKL2ygpKVFeXp4GDhyou+++WwMHDtRHH310ynpnnXWW+vTpo0WLFiknJ0eSdOTIEV111VVKTU3V1KlTlZub2zzmd91111165ZVXdM0116igoEBdunTRZ599piuuuEIvvfRS876+99579fLLL2vQoEFat26dfD6fAoGAnn32Wf3sZz87ZbtdunTRT37yE/3xj3+U9M252AkTJmjz5s3asmWLpk6dqksvvVSzZs3SmDFj9I9//KPV/clhWwAxbfbs2XrxxRd11113yev1qqamRl27dtXo0aObD+F+10UXXaQhQ4Zo1KhRio+P14UXXqgLLrhA+/fvV3x8vLp166aHH35Y1dXVSktL0wMPPGApz4IFC3Tffffpuuuuk8/n06BBgzR9+vTT/jlb2+6CBQu0ZMkSpaSk6Morr1T37t2DbmPMmDH661//qtGjR8vj8ejss8/W5MmTg647duxY3XnnnXr88cclfVPQM2bM0NSpU9W5c2e53W7dd999p3zff//3fys/P1/PPfec3G63hg0bpp/+9Kfq16+fqqurddNNN8k0TQ0YMECTJ09WU1OTli9frjFjxqipqUl9+vTRwoULg2ZasWKFCgsLdd1116mxsVHXXnutrr/+evn9fpWXl+vaa69VYmKiunbtqsLCwlb3p2EGO24AALCsoqJChYWFrc7eEB04bAsAgEXMPAEAsIiZJwAAFlGeAABYFLFX2zY2Numrr7whrdulS4JqaxtsThQasgRHluDIEly0Z7Hy+FEr/y/sSKw+gjXSROzM87s3vf6QuLhTb0p2ClmCI0twZAmOLN+y8v9CtJ+ILU8AACIV5QkAgEWUJwAAFlGeAABYRHkCAGAR5QkAgEWUJwAAFlGeAABYRHkCAGAR5QkA/5ZQVqq0vlnqdlZXpfXNUkJZqdOREKEi9tm2ANCeEspKlTxnlgzvN8+RdVdVKnnOLPkT46VRYxxOh0jDzBMAJCUVLW4uzhMMr1fuhQscSoRIxswTACS5qquCf1BZ2b5BvscV8Cvx8AFHM0hSIClZX3u6OB0jYlCeACAp0CNd7qogRZmR0f5hvisQUOCVV53NIMk1eqREeTbjsC0ASKrLL5Dp8Zy0zPR45C+8z6FEiGSUJwBIasgep5rilfKnZ8g0DPnTM1RTvFLmhIlOR0ME4rAtAPxbQ/Y4NWSPO2mZp4V1EduYeQIAYBHlCQCARZQnAAAWUZ4AAFhEeQIAYBHlCQCARZQnAAAWUZ4AAFhEeQIAYBHlCQCARZQnAAAW2fJs28bGRuXl5amyslJdunTRokWLZBiG5s2bJ8Mw1KtXLxUUFMjlorsBAB2PLeVZWlqqxMRElZaWau/evSosLFSnTp2Um5uryy67TIsWLdLmzZs1fPhwO4YHAMBWtkz9PvnkEw0ePFiSlJmZqT179mjnzp0aMGCAJGnw4MHaunWrHUMDAGA7W2aevXv31pYtWzRs2DB9+OGHOnTokP7jP/5DhmFIkpKSklRTU9PqNtxuQ6mpiSGN53a7Ql7XbmQJjizBkSU4snzLMAx5EuMdG79ZQifFR8jvJBLYUp7Z2dnas2ePpkyZor59+yorK0tffPFF8+d1dXVKSUlpdRt+v6ljx+pDGi81NTHkde1GluDIEhxZgov2LN27J4e8rmma8tY3hnX8tnA1+FQfxv1gZR9EIlsO2+7YsUP9+vXTmjVrNGzYMGVkZOiSSy5RRUWFJKm8vFz9+/e3Y2gAAGxny8zz3HPP1SOPPKKnnnpKycnJKioqUn19vRYuXKji4mJlZmZqxIgRdgwNAIDtbCnPtLQ0Pf3006csX7t2rR3DAUC7SigrVVLRYrmqqxToka66/AI1ZI9zOhbakS3lCQDRKqGsVMlzZsnweiVJ7qpKJc+ZJUkUaAzhKQUAYEFS0eLm4jzB8HqVVLTYoURwAuUJABa4qqssLUd0ojwBwIJAj3RLyxGdKE8AsKAuv0Cmx3PSMtPjUV1+gUOJ4ATKEwAsaMgep5rilfKnZ8g0DPnTM1RTvJKLhWIM5QmgXSWUlSqtb5a6ndVVaX2zlFBW6nQkyxqyx+nI9p06fOgrHdm+k+KMQdyqAqDdcJsHogUzTwDthts8EC0oTwDthts8EC0oTwC2O3GeU6YZ9HNu80BHwzlPALb6/nnO7+M2D3REzDwB2CrYeU5JMiVu80CHxcwTgK1aPJ9pGDqyfWf7hgHChJknAFvxODtEI8oTgK14nB2iEeUJwFY8zg7RiHOeAGzXkD2OskRUYeYJAIBFlCcAABZRngAAWER5AgBgEeUJAIBFlCcAABZRngAAWER5AuiQTrzmrNtZXZXWN0sJZaVOR0IM4SEJADqc77/mzF1VqeQ5sySJhzGgXTDzBNDhBHvNmeH1KqlosUOJEGsoTwAdTkuvOWvx9WdAmNly2Nbn82nevHmqrq6Wy+VSYWGh4uLiNG/ePBmGoV69eqmgoEAuF90NwLpAj3S5qyqDLgfagy3t9eabb6qpqUnr16/XzJkz9fDDD2vp0qXKzc1VSUmJTNPU5s2b7RgaQAzgNWdwmi0zz549e8rv9ysQCKi2tlZxcXH64IMPNGDAAEnS4MGD9c4772j48OEtbsPtNpSamhjSeG63K+R17UaW4MgSHFmC+8Est06VPzFe7oULpMpKKSND/sL75JkwUZ6Wv8ueLDYzDEOexHjHxm+W0EnxEfLvRySwpTwTExNVXV2tUaNG6ejRo1q1apW2bdsmwzAkSUlJSaqpqWl1G36/qWPH6kMaLzU1MeR17UaW4MgSHFmCCynLqDHf/PkuG/LbsV+6d08OeV3TNOWtbwzr+G3havCpPoz7wco+iES2lOfTTz+tgQMH6q677tKBAwd0yy23yOfzNX9eV1enlJQUO4YGAMB2tpzzTElJUXLyN3+r6Nq1q5qamnTJJZeooqJCklReXq7+/fvbMTQAALazZeY5depUzZ8/XxMnTpTP59Ps2bP1ox/9SAsXLlRxcbEyMzM1YsQIO4YGAMB2tpRnUlKSHnnkkVOWr1271o7hAABoV9xoCQCARZQnAAAWUZ4AAFhEeQIAYBHlCQCARZQnAAAWUZ5ADEooK1Va3yx1O6ur0vpmKaGs1OlIQIdiy32eACJXQlmpkufMan6ZtLuqUslzZsmfGH/qs2IBBMXME4gxSUWLm4vzBMPr/eYNJQBCQnkCMcZVXRX8g8pTXy4NIDjKE4gxgR7pwT/IyGjfIDbinC7sRnkCMaYuv0Cm5+RXRpsej/yF9zmUKLxOnNN1V1XKMM3mc7oUKMKJ8gRiTEP2ONUUr5Q/PUOmYcifnqGa4pUyJ0x0OlpYtHRON6losUOJEI242haIQQ3Z49SQPe6kZZ4W1u1oWjqn2+K5XqANmHkCiCotndNt8Vwv0AaUJ4Co0tI53br8AocSIRpRngCiSkvndL9/mBo4HZzzBBB1gp3TBcKJmScAABZRngAAWER5AgBgEeUJAIBFlCcAABZRngAAWER5AgBgEeUJAIBFlCcAABZRngAAWER5AgBgkS3Ptt2wYYM2btwoSWpoaNCuXbtUUlKi+++/X4ZhqFevXiooKJDLRXcDADoeW9rrpptu0po1a7RmzRplZWVpwYIF+sMf/qDc3FyVlJTINE1t3rzZjqEBALCdrW9V2bFjhz755BMVFBTo0Ucf1YABAyRJgwcP1jvvvKPhw4e3+L1ut6HU1MSQxnG7XSGvazeyBEeW4MgSHFm+ZRiGPInxjo3fLKGT4iPkdxIJbC3P1atXa+bMmZIk0zRlGIYkKSkpSTU1Na1+r99v6tix+pDGSU1NDHldu5ElOLIE15GzJJSVKqlosVzVVQr0SFddfkHYXgPWkfdLKLp3Tw55XdM05a1vDOv4beFq8Kk+jPvByj6IRLaV5/Hjx7V3715dfvnlknTS+c26ujqlpKTYNTQAmyWUlSp5ziwZXq8kyV1VqeQ5sySJ92giJth2xc62bdt05ZVXNn99ySWXqKKiQpJUXl6u/v372zU0AJslFS1uLs4TDK9XSUWLHUoEtC/bZp779u1Tenp689dz587VwoULVVxcrMzMTI0YMcKuoQHYzFVdZWk52s4wDCUMGRjWbZqdEtTUqZOl7wkkdezDrOFmW3n++te/Punrnj17au3atXYNB6AdBXqky11VGXQ5wss0TTW88XZYt+kaPVL13c4O6zZjDTdaArCsLr9Apsdz0jLT41FdfoFDiYD2RXkCsKwhe5xqilfKn54h0zDkT89QTfFKLhZCzKA8gQiQUFaqtL5Z6nZWV6X1zVJCWanTkX5QQ/Y4Hdm+U4cPfaUj23dSnIgptt7nCeCHcdsH0PEw8wQcxm0fQMdDeQIO47YPoOOhPAGHtXR7B7d9AJGL8gQcxm0fQMdDeQIO47YPoOPhalsgAjRkj6MsgQ6EmScAABZRngAAWER5AgBgEeUJAIBFlCcAABZRngAAWER5AgBgEeUJAIBFlCcAABZRngAAWER5AgBgEeUJtJOEslKl9c1St7O6Kq1vlhLKSp2OBKCNeDA80A4SykqVPGeWDK9XkuSuqlTynFnyJ8ZLo8Y4nA6AVcw8gXaQVLS4uThPMLxeuRcucCgRgNNBeQLtwFVdFfyDysr2DQIgLChPoB0EeqQH/yAjo32DAAgLyhNoB3X5BTI9npOWmR6P/IX3OZQIwOmgPIF20JA9TjXFK+VPz5BpGPKnZ6imeKXMCROdjgagDWy72nb16tV6/fXX5fP5NGHCBA0YMEDz5s2TYRjq1auXCgoK5HLR3eh4EspKlVS0WK7qKgV6pKsuv0AN2eN+8Psassedsp6nhXUBRDZb2quiokLvv/++1q1bpzVr1ujgwYNaunSpcnNzVVJSItM0tXnzZjuGBmx14pYTd1WlDNNsvuWEezaB2GJLeb799tu68MILNXPmTE2fPl1DhgzRzp07NWDAAEnS4MGDtXXrVjuGBmzV0i0nSUWLHUoEwAm2HLY9evSoPv/8c61atUpVVVWaMWOGTNOUYRiSpKSkJNXU1LS6DbfbUGpqYkjjud2ukNe1G1mCi5YsLd1y4qquatM2o2W/hBtZvmUYhjyJ8eHdaEInxUfI/u2obCnP1NRUZWZmKj4+XpmZmUpISNDBgwebP6+rq1NKSkqr2/D7TR07Vh/ieIkhr2s3sgQXLVnSeqTLXXXqvZmBHult2ma07Jdwi/Ys3bsnh7yuaZry1jeGdXxXg0/1Du9fK/sgEoV02Paf//yn3n//fX344Ye65ZZb9O6777a6fr9+/fTWW2/JNE0dOnRIXq9XV1xxhSoqKiRJ5eXl6t+//+mnB9pZS7ec1OUXOJQIgBNCKs+CggLFx8fr8ccf1+zZs/Xoo4+2uv7QoUPVu3dv5eTkaMaMGVq0aJHmzp2rlStX6uabb5bP59OIESPC8gMA7amlW05CudoWQPQI6bBtXFycevXqJZ/Pp5/85Cfy+/0/+D333HPPKcvWrl1rPSEQYYLdcgIgtoQ08zQMQ3fddZcGDx6sV155RR4Pd6cBAGJXSDPPhx56SDt27NBVV12l9957Tw899JDduQAAiFghzTzj4+O1fft2zZ8/X8ePH9dXX31ldy4AACJWSOU5f/58ZWRk6NNPP1W3bt2Un59vdy4AACJWSOV57Ngx5eTkKC4uTn379pVpmnbnAgAgYoX8eL49e/ZIkg4ePMgD3QEAMS2kFlywYIHmz5+vjz76SHfccYfmzZtndy4AACJWSFfbvvXWW3ruuefszgIAQIcQ0szzzTffDOnBCAAAxIKQZp5Hjx7VoEGDlJ6eLsMwZBiG1q9fb3c2AAAiUkjluWrVKrtzAADQYYRUnm63W/fff7/27Nmj8847T3l5eXbnAgAgYoV8te0NN9ygdevW6cYbb+QhCQCAmBZSeTY0NOjqq69WSkqKhg0bpqamJrtzAQAQsUIqT7/fr48//liS9PHHH8swDFtDAW2VUFaqtL5Z6nZWV6X1zVJCWanTkQBEoZDOeZ54SMK//vUvnXnmmSosLLQ7F2BZQlmpkufMkuH1SpLcVZVKnjNLknj/JoCwCmnmecEFF6iwsFDl5eWaPn26LrjgArtzAZYlFS1uLs4TDK9XSUWLHUoEIFqFVJ6//e1v9eGHH0qS9u3bx+P5EJFc1VWWlgNAW4VUnocOHdKECRMkSbfddpu++OILW0MBbRHokW5pOQC0VcivR9m3b58kaf/+/QoEArYFAtqqLr9Apsdz0jLT41FdfoFDiQBEq5AuGMrPz1dubq727t2rXr16acmSJXbnAiw7cVFQUtFiuaqrFOiRrrr8Ai4WAhB2rc48d+7cqTFjxqh3796aOXOmkpOTVVdXp0OHDrVXPsCShuxxOrJ9pw4f+kpHtu+kOAHYotXyfOihh7Rs2TJ16tRJDz/8sJ588kmVlZXpySefbK98AABEnFYP25qmqYsvvliHDh2S1+tVVlaWJMnlCvlUKQAAUafVFjxxYdBbb72lK664QpLU2Niouro6+5MBABChWp15XnHFFRo/frwOHjyoxx9/XJ999pnuvfdejR49ur3yAQAQcVotz9tvv11XX3210tLSdMYZZ+izzz7ThAkTNHz48PbKBwBAxPnBW1XOP//85n8+55xzdM4559gaCACASMeVPwAAWBTSQxLaYsyYMUpOTpYkpaena/r06Zo3b54Mw1CvXr1UUFDAVbsAgA7JlvJsaGiQJK1Zs6Z52fTp05Wbm6vLLrtMixYt0ubNmzl3CgDokGyZ+u3evVter1fTpk3TlClT9MEHH2jnzp0aMGCAJGnw4MHaunWrHUMDAGA7W2aenTt31q233qqxY8fq008/1W233SbTNGUYhiQpKSlJNTU1rW7D7TaUmpoY0nhutyvkde1GluDIEhxZgiPLtwzDkCcxPrwbTeik+AjZvx2VLeXZs2dPnXvuuTIMQz179lRqaqp27tzZ/HldXZ1SUlJa3Ybfb+rYsfqQxktNTQx5XbuRJTiyBEeW4KI9S/fuySGva5qmvPWNYR3f1eBTvcP718o+iES2HLb905/+pGXLlkn65l2gtbW1+tnPfqaKigpJUnl5ufr372/H0AAA2M6WmWdOTo7y8vI0YcIEGYah+++/X2eccYYWLlyo4uJiZWZmasSIEXYMDQCA7Wwpz/j4eP3ud787ZfnatWvtGA4AgHbFjZYAAFhEeQIAYBHlCQCARZQnYlZCWanS+map21ldldY3SwllpU5HAtBB2PZsWyCSJZSVKnnOLBleryTJXVWp5DmzJEkN2eOcjAagA2DmiZiUVLS4uThPMLxeJRUtdigRgI6E8kRMclVXWVoOAN9FeSImBXqkW1oOAN9FeSIm1eUXyPR4Tlpmejyqyy9wKBGAjoTyRExqyB6nmuKV8qdnyDQM+dMzVFO8kouFAISEq20Rsxqyx1GWANqEmScAABZRngAAWER5AgBgEeUJAIBFlCcAABZRngAAWER5AgBgEeUJAIBFlCcAABZRngAAWMTj+WJcQlmpkooWy1VdpUCPdNXlF/DIOiCCGIahhCEDw7pNUwElHj4Q1m2GIpCUrK89Xdp9XDtQnjEsoaxUyXNmNb8U2l1VqeQ5sySJAgUihGmaanjjbadjhIVr9EgpSsqTw7YxLKlocXNxnmB4vUoqWuxQIgDoGCjPGOaqrrK0HADwDcozhgV6pFta7qSEslKl9c1St7O6Kq1vlhLKSp2OBCCGUZ4xrC6/QKbHc9Iy0+NRXX6BQ4mCO3Fu1l1VKcM0m8/NUqAAnEJ5xrCG7HGqKV4pf3qGTMOQPz1DNcUrI+5iIc7NAog0XG0b4xqyx0VcWX4f52YBRBrbZp5ffvmlrrrqKu3Zs0f79+/XhAkTNHHiRExkgikAAA+qSURBVBUUFCgQCNg1LKJQRzo3CyA22FKePp9PixYtUufOnSVJS5cuVW5urkpKSmSapjZv3mzHsIhSHeXcLIDYYUt5Ll++XOPHj9eZZ54pSdq5c6cGDBggSRo8eLC2bt1qx7CIUh3l3CyA2BH2c54bNmxQWlqaBg0apCeeeELSN0/IMAxDkpSUlKSampof3I7bbSg1NTGkMd1uV8jr2o0swZ12llunKnDrVJ044O/59x9HsoQRWYIjy7cMw5AnMd6x8cMqoZPiI+T3errCXp5lZWUyDEPvvvuudu3apblz5+rIkSPNn9fV1SklJeUHt+P3mzp2rD6kMVNTE0Ne125kCY4swZEluGjP0r17csjrmqYpb31jWMd3iqvBp/p/70sr+yAShb08n3322eZ/njx5su699149+OCDqqio0GWXXaby8nJdfvnl4R4WAIB20y73ec6dO1crV67UzTffLJ/PpxEjRrTHsAAA2MLW+zzXrFnT/M9r1661cygAANoNTxgCAMAiyhMAAIsoTwAALKI8AQCwiPIEAMAiyhMAAIsoTwAALKI8AQCwiPIEAMAiyhMAAIsoT5y2hLJSpfXNUrezuiqtb5YSykqdjgQAtrL12baIfgllpUqeM0uG1ytJcldVKnnOLEniZdUAohYzT5yWpKLFzcV5guH1KqlosUOJAMB+lCdOi6u6ytJyAIgGlCdOS6BHuqXlABANKE+clrr8Apkez0nLTI9HdfkFDiUCAPtRnjgtDdnjVFO8Uv70DJmGIX96hmqKV3KxEICoxtW2OG0N2eMoSwAxhZknAAAWUZ4AAFhEeQIAYBHlCQCARZQnAAAWUZ4AAFhEeQIAYBHlCQCARZQnAAAWUZ4AAFhEeQIAYJEt5en3+5WXl6fx48dr0qRJ+uyzz7R//35NmDBBEydOVEFBgQKBgB1Dd3gJZaVK65ulbmd1VVrfLCWUlYb0WXtnAYBYZsuD4bds2SJJWr9+vSoqKrR06VKZpqnc3FxddtllWrRokTZv3qzhw4fbMXyHlVBWquQ5s2R4vZIkd1WlkufMav68pc/seCh7a1l4CDyAWGfLzHPYsGEqLCyUJH3++efq1q2bdu7cqQEDBkiSBg8erK1bt9oxdIeWVLS4uaxOMLxeJRUtbvWz9s4CALHOtleSxcXFae7cudq0aZN+//vfa8uWLTIMQ5KUlJSkmpqaVr/f7TaUmpoY0lhutyvkde12Ollc1VWWlp/4rKXx7MrSlm1Gy+8o3MgSHFm+ZRiGPInxjo0fVgmdFB8hv9fTZev7PJcvX67f/va3GjdunBoaGpqX19XVKSUlpdXv9ftNHTtWH9I4qamJIa9rt9PJktYjXe6qylOWB3qkS1KLn7U0nl1Z2rLNaPkdhRtZgov2LN27J4e8rmma8tY3hnV8p7gafKr/9760sg8ikS2HbZ9//nmtXr1akuTxeGQYhn70ox+poqJCklReXq7+/fvbMXSHVpdfINPjOWmZ6fGoLr+g1c/aOwsAxDpbZp6/+MUvlJeXp0mTJqmpqUnz58/X+eefr4ULF6q4uFiZmZkaMWKEHUN3aCcuxEkqWixXdZUCPdJVl19w0gU6rX3W3lkAIFYZpmmaTocIxufzx9xh23AjS3BkCY4swTl92Nbv/Vr1jz0R1vGd4ho9UvXdzpbEYVsAAGIO5QkAgEWUJwAAFlGeMYZH7gHA6bP1Pk9EFh65BwDhwcwzhvDIPQAID8ozhrTl8X8AgFNRnjHkxGP+Ql0OAAiO8owhPHIPAMKD8owhDdnjVFO8Uv70DJmGIX96hmqKV3KxEABYRHl2IOG4zaQhe5yObN+pw4e+0pHtOylOAGgDblXpILjNBAAiBzPPDoLbTAAgclCeHQS3mQBA5KA8T0N7PuqO20wAIHJQnm104hyku6pShmk2n4M01pXYMh63mQBA5KA826ilc5DuhQtsGY/bTAAgcnC1bRu1eK6xstK2MRuyx1GWABABmHm2UYvnGjMy2jcIAKDdUZ5t1NI5SH/hfQ4lAgC0F8qzjVo6B2lOmOh0NACAzTjneRqCnYP0tLAuACB6MPMEAMAiyhMAAIsoTwAALKI8I0x7PvIPANA2XDAUQXjtGAB0DMw8IwivHQOAjoHyVOQcKuW1YwDQMYT9sK3P59P8+fNVXV2txsZGzZgxQxdccIHmzZsnwzDUq1cvFRQUyOWKjN6OpEOlgR7pcled+mxcXjsGAJEl7A324osvKjU1VSUlJXryySdVWFiopUuXKjc3VyUlJTJNU5s3bw73sG0WSYdKee0YAHQMhmmaZjg3WFdXJ9M01aVLFx09elQ5OTlqbGxUeXm5DMPQX/7yF73zzjsqKGi9EAKBgPz+0KK53S75/YE25Y1L6CQjyC4wDUNNDT7L2zudLJJkrCv55rVmlZVSRob8hfe1+ZF/p5slnMgSHFmCi/YsnTq5Q1438HWDAn/8Y1jHd8yIETIzzpFkbR9EorAftk1KSpIk1dbW6o477lBubq6WL18uwzCaP6+pqfnB7fj9po4dqw9pzNTUxJDX/b60Vg6VtmWbp5NFkjRqzDd/vquN2zvtLGFEluDIEly0Z+nePTnkdU3TlLe+MazjO8XV4FP9v/ellX0QiWw58XjgwAFNmTJFN9xwg6677rqTzm/W1dUpJSXFjmHbhEOlAACrwl6ehw8f1rRp03T33XcrJydHknTJJZeooqJCklReXq7+/fuHe9g2a+ntKNxXCQBoSdgP265atUrHjx/XY489pscee0ySlJ+fr/vuu0/FxcXKzMzUiBEjwj3saQn2dhQAAFoS9vJcsGCBFixYcMrytWvXhnsoAAAcERk3WwIA0IFQngAAWBRV5Rkpj9kDAES3qHmrSiQ9Zg8AEN2iZuYZSY/ZAwBEt6iZefJGEgBRyeWSa/RIp1OERSCpYz9V6Luipjx5IwmAaBRwuVXf7WynY+B7ouawLY/ZAwC0l6gpTx6zBwBoL1Fz2FbiMXsAgPYRNTNPAADaC+UJAIBFlCcAABZRngAAWER5AgBgEeUJAIBFlCcAABZRngAAWER5AgBgEeUJAIBFhmmaptMhAADoSJh5AgBgEeUJAIBFlCcAABZRngAAWER5AgBgEeUJAIBFlCcAABbFOR3gdH344YdasWKF1qxZ42gOn8+n+fPnq7q6Wo2NjZoxY4auvvpqR7L4/X4tWLBA+/btk9vt1tKlS3XOOec4kkWSvvzyS91000166qmndP755zuWQ5LGjBmj5ORkSVJ6erqWLl3qWJbVq1fr9ddfl8/n04QJEzR27FhHcmzYsEEbN26UJDU0NGjXrl165513lJKS0u5ZfD6f5s2bp+rqarlcLhUWFjr270xjY6Py8vJUWVmpLl26aNGiRTrvvPMcyYLI06HL88knn9SLL74oj8fjdBS9+OKLSk1N1YMPPqijR4/qxhtvdKw8t2zZIklav369KioqtHTpUj3++OOOZPH5fFq0aJE6d+7syPjf1dDQIEmO/0VLkioqKvT+++9r3bp18nq9euqppxzLctNNN+mmm26SJC1evFjZ2dmOFKckvfnmm2pqatL69ev1zjvv6OGHH9bKlSsdyVJaWqrExESVlpZq7969Kiws1P/+7/86kgWRp0Mftj3nnHMc+w/r+0aOHKk777yz+Wu32+1YlmHDhqmwsFCS9Pnnn6tbt26OZVm+fLnGjx+vM88807EMJ+zevVter1fTpk3TlClT9MEHHziW5e2339aFF16omTNnavr06RoyZIhjWU7YsWOHPvnkE918882OZejZs6f8fr8CgYBqa2sVF+fc3+8/+eQTDR48WJKUmZmpPXv2OJYFkadDzzxHjBihqqoqp2NIkpKSkiRJtbW1uuOOO5Sbm+tonri4OM2dO1ebNm3S73//e0cybNiwQWlpaRo0aJCeeOIJRzJ8V+fOnXXrrbdq7Nix+vTTT3Xbbbfp1VdfdeR/0EePHtXnn3+uVatWqaqqSjNmzNCrr74qwzDaPcsJq1ev1syZMx0bX5ISExNVXV2tUaNG6ejRo1q1apVjWXr37q0tW7Zo2LBh+vDDD3Xo0CH5/X5H/2KMyNGhZ56R5sCBA5oyZYpuuOEGXXfddU7H0fLly/Xaa69p4cKFqq+vb/fxy8rKtHXrVk2ePFm7du3S3Llz9a9//avdc5zQs2dPXX/99TIMQz179lRqaqpjeVJTUzVw4EDFx8crMzNTCQkJOnLkiCNZJOn48ePau3evLr/8cscySNLTTz+tgQMH6rXXXtMLL7ygefPmNR9ub2/Z2dnq0qWLpkyZoi1btigrK4viRDPKM0wOHz6sadOm6e6771ZOTo6jWZ5//nmtXr1akuTxeGQYhiP/0T/77LNau3at1qxZo969e2v58uXq3r17u+c44U9/+pOWLVsmSTp06JBqa2sdy9OvXz+99dZbMk1Thw4dktfrVWpqqiNZJGnbtm268sorHRv/hJSUlOYLurp27aqmpib5/X5HsuzYsUP9+vXTmjVrNGzYMGVkZDiSA5GpQx+2jSSrVq3S8ePH9dhjj+mxxx6T9M0FTU5cKPOLX/xCeXl5mjRpkpqamjR//nwlJCS0e45Ik5OTo7y8PE2YMEGGYej+++937Jza0KFDtW3bNuXk5Mg0TS1atMjRWc2+ffuUnp7u2PgnTJ06VfPnz9fEiRPl8/k0e/ZsJSYmOpLl3HPP1SOPPKKnnnpKycnJKioqciQHIhOvJAMAwCIO2wIAYBHlCQCARZQnAAAWUZ4AAFhEeQIAYBHliahUUVGh2bNnn7RsxYoV2rBhg0OJAEQTyhMAAIt4SAJizrJly/S3v/1NknTttdfqlltu0bx58zR69GgNHjxY5eXleuWVV7Rs2TINHTpUmZmZyszM1E9/+lM9+eSTiouLU48ePfTAAw/I5eLvn0AsojwRtd577z1Nnjy5+evKykr9+te/VlVVlUpLS9XU1KSJEye2+jzXAwcOaMOGDTrjjDN0xx13aOrUqbrmmmv0/PPPq7a21rFXdwFwFuWJqHX55ZfroYceav56xYoV+vrrr9W/f38ZhqFOnTrpv/7rv0551dR3H7p1xhln6IwzzpAk5eXlafXq1Vq3bp0yMzM1bNiw9vlBAEQcjjkhpnTu3Ln5kK3P59P777+vc889V/Hx8c1vWPnoo4+a1//uYdnnnntOs2bN0tq1ayVJmzZtasfkACIJM0/ElMTERKWnp+vmm2+Wz+fTyJEjlZWVpbFjx2r+/Pn685//rPPOOy/o9/bp00e/+tWvlJqaqqSkpIh4gTUAZ/BgeAAALOKwLQAAFlGeAABYRHkCAGAR5QkAgEWUJwAAFlGeAABYRHkCAGDR/w8mQGn7rvUQkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "sns.jointplot(x=data['Hours'] , y=data['Scores'], color='r', kind='scatter');\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Graph of Hours vs scores')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comment:\n",
    "### From the graph above, we can clearly observe that there is a positive linear relationship between the number of hours studied and the scores obtained. We can say that with the increase of Hours studied(x), there is an increase in the scores obtained(y). There is a positive correlation between the two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plotting the regression line')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"Hours\", y=\"Scores\", data=data);\n",
    "plt.title(\"Plotting the regression line\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The next step is to divide the data into 'attributes' and 'labels'. Attributes are the independent variables while labels are dependent variables whose values are to be predicted. In our dataset ,we have two columns. We want to predict the percentage score depending upon the hours studied. Therefore our attribute set will consist of the \"Hours\" column while the label will be the \"Score\" column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.5\n",
       "1     5.1\n",
       "2     3.2\n",
       "3     8.5\n",
       "4     3.5\n",
       "5     1.5\n",
       "6     9.2\n",
       "7     5.5\n",
       "8     8.3\n",
       "9     2.7\n",
       "10    7.7\n",
       "11    5.9\n",
       "12    4.5\n",
       "13    3.3\n",
       "14    1.1\n",
       "15    8.9\n",
       "16    2.5\n",
       "17    1.9\n",
       "18    6.1\n",
       "19    7.4\n",
       "20    2.7\n",
       "21    4.8\n",
       "22    3.8\n",
       "23    6.9\n",
       "24    7.8\n",
       "Name: Hours, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= data['Hours']\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.5, 5.1, 3.2, 8.5, 3.5, 1.5, 9.2, 5.5, 8.3, 2.7, 7.7, 5.9, 4.5,\n",
       "       3.3, 1.1, 8.9, 2.5, 1.9, 6.1, 7.4, 2.7, 4.8, 3.8, 6.9, 7.8])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_array=np.array(x)\n",
    "x_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.5],\n",
       "       [5.1],\n",
       "       [3.2],\n",
       "       [8.5],\n",
       "       [3.5],\n",
       "       [1.5],\n",
       "       [9.2],\n",
       "       [5.5],\n",
       "       [8.3],\n",
       "       [2.7],\n",
       "       [7.7],\n",
       "       [5.9],\n",
       "       [4.5],\n",
       "       [3.3],\n",
       "       [1.1],\n",
       "       [8.9],\n",
       "       [2.5],\n",
       "       [1.9],\n",
       "       [6.1],\n",
       "       [7.4],\n",
       "       [2.7],\n",
       "       [4.8],\n",
       "       [3.8],\n",
       "       [6.9],\n",
       "       [7.8]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new=x_array.reshape(-1,1)\n",
    "x_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     21\n",
       "1     47\n",
       "2     27\n",
       "3     75\n",
       "4     30\n",
       "5     20\n",
       "6     88\n",
       "7     60\n",
       "8     81\n",
       "9     25\n",
       "10    85\n",
       "11    62\n",
       "12    41\n",
       "13    42\n",
       "14    17\n",
       "15    95\n",
       "16    30\n",
       "17    24\n",
       "18    67\n",
       "19    69\n",
       "20    30\n",
       "21    54\n",
       "22    35\n",
       "23    76\n",
       "24    86\n",
       "Name: Scores, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y= data['Scores']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 47, 27, 75, 30, 20, 88, 60, 81, 25, 85, 62, 41, 42, 17, 95, 30,\n",
       "       24, 67, 69, 30, 54, 35, 76, 86], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_array=np.array(y)\n",
    "y_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21],\n",
       "       [47],\n",
       "       [27],\n",
       "       [75],\n",
       "       [30],\n",
       "       [20],\n",
       "       [88],\n",
       "       [60],\n",
       "       [81],\n",
       "       [25],\n",
       "       [85],\n",
       "       [62],\n",
       "       [41],\n",
       "       [42],\n",
       "       [17],\n",
       "       [95],\n",
       "       [30],\n",
       "       [24],\n",
       "       [67],\n",
       "       [69],\n",
       "       [30],\n",
       "       [54],\n",
       "       [35],\n",
       "       [76],\n",
       "       [86]], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_new=y_array.reshape(-1,1)\n",
    "y_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we will split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_new ,y_new , test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We have split our data into training and testing sets, and now is finally the time to train our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.64567042])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.50235884]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.64567042]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.50235884]]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_train,y_train,color='r')\n",
    "plt.plot(x_train,regressor.predict(x_train),color='k')\n",
    "plt.xlabel(\"No. of Hours Studied\")\n",
    "plt.ylabel(\"Exam Scores Obtained\")\n",
    "plt.title(\"Plotting the regression line\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now that we have trained our algorithm, it's time to make some predictions. To do so, we will use our test data and see how accurately our algorithm predicts the percentage score. Then we will compare the actual output values for X_test with the predicted values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85</td>\n",
       "      <td>76.813833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>49.256993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55.908644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>46.406285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>27.401568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      85  76.813833\n",
       "1      54  49.256993\n",
       "2      60  55.908644\n",
       "3      41  46.406285\n",
       "4      21  27.401568"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted=regressor.predict(x_test)\n",
    "df=pd.DataFrame({\"Actual\": y_test.flatten(), 'Predicted': y_predicted.flatten()})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we will  predict the score of a student who studies for 9.25 hrs/ day and of a student who studies for 8 hrs/day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[91.54248968],\n",
       "       [79.66454113]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hours=np.array([9.25,8])\n",
    "hours_array=hours.reshape(-1,1)\n",
    "own_prediction= regressor.predict(hours_array)\n",
    "own_prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### At last we will evaluate the performance of our algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. For this, we will use:\n",
    "### 1. Mean Absolute Error (MAE) is the mean of the absolute value of the errors. It is calculated as:1/n*sum(i=1 to n)|Actual-Predicted|\n",
    "### 2. Root Mean Squared Error (RMSE) is the square root of the mean of the squared errors:sqrt{1/n*sum(i=1 to n)|Actual-Predicted|}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 5.76567647027072\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_predicted))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comment:\n",
    "\n",
    "###  The mean absolute error (MAE) is the simplest regression error metric to understand. We’ll calculate the residual for every data point, taking only the absolute value of each so that negative and positive residuals do not cancel out. We then take the average of all these residuals.The MAE is also the most intuitive of the metrics since we’re just looking at the absolute difference between the data and the model’s predictions.Because we use the absolute value of the residual(actual-predicted), the MAE does not indicate underperformance or overperformance of the model.Each residual contributes proportionally to the total amount of error, meaning that larger errors will contribute linearly to the overall error. A small MAE suggests the model is great at prediction, while a large MAE suggests that our model may have trouble in certain areas. The obtained MAE (5.7657) from our algorithm is small, hence we can say that our algorithm did a good job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}