Women's_Clothing_Review_Data_Visualization.ipynb

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    "colab": {
      "name": "Women's Clothing Review Data Visualization",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/nttran8/Clothing-Review-Analytics/blob/master/Women's_Clothing_Review_Data_Visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "CmIZxqPcIM2Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np #Numpy provides fast mathematical function processing\n",
        "import pandas as pd #Pandas is used for easy indexing and data collecting tools\n",
        "import matplotlib as mpl #Machine learning libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns #Import library to create a heatmap\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nL7mc3LfJEhX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reviewData=pd.read_csv(\"https://raw.githubusercontent.com/nttran8/Clothing-Review-Analytics/master/Womens%20Clothing%20E-Commerce%20Reviews.csv\")\n",
        "#Data import"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7WdGG3jsJTCf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "326ae040-57df-4782-97af-905582061f3a"
      },
      "source": [
        "reviewData.head()\n",
        "#Preview data"
      ],
      "execution_count": 6,
      "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",
              "        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>Unnamed: 0</th>\n",
              "      <th>Clothing ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Title</th>\n",
              "      <th>Review Text</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Recommended IND</th>\n",
              "      <th>Positive Feedback Count</th>\n",
              "      <th>Division Name</th>\n",
              "      <th>Department Name</th>\n",
              "      <th>Class Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>767</td>\n",
              "      <td>33</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Initmates</td>\n",
              "      <td>Intimate</td>\n",
              "      <td>Intimates</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1080</td>\n",
              "      <td>34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>General</td>\n",
              "      <td>Dresses</td>\n",
              "      <td>Dresses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1077</td>\n",
              "      <td>60</td>\n",
              "      <td>Some major design flaws</td>\n",
              "      <td>I had such high hopes for this dress and reall...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>General</td>\n",
              "      <td>Dresses</td>\n",
              "      <td>Dresses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1049</td>\n",
              "      <td>50</td>\n",
              "      <td>My favorite buy!</td>\n",
              "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>General Petite</td>\n",
              "      <td>Bottoms</td>\n",
              "      <td>Pants</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>847</td>\n",
              "      <td>47</td>\n",
              "      <td>Flattering shirt</td>\n",
              "      <td>This shirt is very flattering to all due to th...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>General</td>\n",
              "      <td>Tops</td>\n",
              "      <td>Blouses</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Clothing ID  Age  ...   Division Name Department Name  Class Name\n",
              "0           0          767   33  ...       Initmates        Intimate   Intimates\n",
              "1           1         1080   34  ...         General         Dresses     Dresses\n",
              "2           2         1077   60  ...         General         Dresses     Dresses\n",
              "3           3         1049   50  ...  General Petite         Bottoms       Pants\n",
              "4           4          847   47  ...         General            Tops     Blouses\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwOHyoLVJiyO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "b2f583ac-b073-461e-e0ab-1b52c8fe5e12"
      },
      "source": [
        "reviewData.columns.values\n",
        "#Prints out all columns"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Unnamed: 0', 'Clothing ID', 'Age', 'Title', 'Review Text',\n",
              "       'Rating', 'Recommended IND', 'Positive Feedback Count',\n",
              "       'Division Name', 'Department Name', 'Class Name'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab_type": "code",
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          "height": 34
        },
        "outputId": "be35ed9b-795c-4112-841b-6a848e920c64"
      },
      "source": [
        "reviewData.shape\n",
        "#(# of rows and columns in the dataset)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23486, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhNtXgjhJ29a",
        "colab_type": "code",
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          "height": 34
        },
        "outputId": "18cfde49-8036-4a64-f7b9-331a689b265e"
      },
      "source": [
        "reviewData.isnull().values.any()\n",
        "#Checks if data has Null/NAN values\n",
        "#True means yes, there are Null/NAN values\n",
        "#We have to perform further ETL"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YLI4uLTuKY_2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reviewData2 = reviewData.dropna()\n",
        "#Remove all Null/NAN values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mokSuDmaKodD",
        "colab_type": "code",
        "colab": {
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          "height": 419
        },
        "outputId": "1bf44039-26af-4d9d-ddab-a496508a2f30"
      },
      "source": [
        "reviewData2.fillna(0)\n",
        "#Replaces all the Null/NAN values with 0 (just in case)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Clothing ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Title</th>\n",
              "      <th>Review Text</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Recommended IND</th>\n",
              "      <th>Positive Feedback Count</th>\n",
              "      <th>Division Name</th>\n",
              "      <th>Department Name</th>\n",
              "      <th>Class Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1077</td>\n",
              "      <td>60</td>\n",
              "      <td>Some major design flaws</td>\n",
              "      <td>I had such high hopes for this dress and reall...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>General</td>\n",
              "      <td>Dresses</td>\n",
              "      <td>Dresses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1049</td>\n",
              "      <td>50</td>\n",
              "      <td>My favorite buy!</td>\n",
              "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>General Petite</td>\n",
              "      <td>Bottoms</td>\n",
              "      <td>Pants</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>847</td>\n",
              "      <td>47</td>\n",
              "      <td>Flattering shirt</td>\n",
              "      <td>This shirt is very flattering to all due to th...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>General</td>\n",
              "      <td>Tops</td>\n",
              "      <td>Blouses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>1080</td>\n",
              "      <td>49</td>\n",
              "      <td>Not for the very petite</td>\n",
              "      <td>I love tracy reese dresses, but this one is no...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>General</td>\n",
              "      <td>Dresses</td>\n",
              "      <td>Dresses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>858</td>\n",
              "      <td>39</td>\n",
              "      <td>Cagrcoal shimmer fun</td>\n",
              "      <td>I aded this in my basket at hte last mintue to...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>General Petite</td>\n",
              "      <td>Tops</td>\n",
              "      <td>Knits</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23481</th>\n",
              "      <td>23481</td>\n",
              "      <td>1104</td>\n",
              "      <td>34</td>\n",
              "      <td>Great dress for many occasions</td>\n",
              "      <td>I was very happy to snag this dress at such a ...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>General Petite</td>\n",
              "      <td>Dresses</td>\n",
              "      <td>Dresses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23482</th>\n",
              "      <td>23482</td>\n",
              "      <td>862</td>\n",
              "      <td>48</td>\n",
              "      <td>Wish it was made of cotton</td>\n",
              "      <td>It reminds me of maternity clothes. soft, stre...</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>General Petite</td>\n",
              "      <td>Tops</td>\n",
              "      <td>Knits</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23483</th>\n",
              "      <td>23483</td>\n",
              "      <td>1104</td>\n",
              "      <td>31</td>\n",
              "      <td>Cute, but see through</td>\n",
              "      <td>This fit well, but the top was very see throug...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>General Petite</td>\n",
              "      <td>Dresses</td>\n",
              "      <td>Dresses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23484</th>\n",
              "      <td>23484</td>\n",
              "      <td>1084</td>\n",
              "      <td>28</td>\n",
              "      <td>Very cute dress, perfect for summer parties an...</td>\n",
              "      <td>I bought this dress for a wedding i have this ...</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>General</td>\n",
              "      <td>Dresses</td>\n",
              "      <td>Dresses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23485</th>\n",
              "      <td>23485</td>\n",
              "      <td>1104</td>\n",
              "      <td>52</td>\n",
              "      <td>Please make more like this one!</td>\n",
              "      <td>This dress in a lovely platinum is feminine an...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>General Petite</td>\n",
              "      <td>Dresses</td>\n",
              "      <td>Dresses</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19662 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Unnamed: 0  Clothing ID  Age  ...   Division Name Department Name  Class Name\n",
              "2               2         1077   60  ...         General         Dresses     Dresses\n",
              "3               3         1049   50  ...  General Petite         Bottoms       Pants\n",
              "4               4          847   47  ...         General            Tops     Blouses\n",
              "5               5         1080   49  ...         General         Dresses     Dresses\n",
              "6               6          858   39  ...  General Petite            Tops       Knits\n",
              "...           ...          ...  ...  ...             ...             ...         ...\n",
              "23481       23481         1104   34  ...  General Petite         Dresses     Dresses\n",
              "23482       23482          862   48  ...  General Petite            Tops       Knits\n",
              "23483       23483         1104   31  ...  General Petite         Dresses     Dresses\n",
              "23484       23484         1084   28  ...         General         Dresses     Dresses\n",
              "23485       23485         1104   52  ...  General Petite         Dresses     Dresses\n",
              "\n",
              "[19662 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLgBilLXKxco",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c67b97e-4e0e-462b-ab97-337a3ff47156"
      },
      "source": [
        "reviewData2.isnull().values.any()\n",
        "#Recheck our new cleansed dataset for null/NAN values\n",
        "#False means our dataset is free of null/NAN values and is ready for analytics"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkIdB_nYLIQ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "outputId": "1ccc70c8-337a-4bf9-91cb-22004ddc52a4"
      },
      "source": [
        "f, ax = plt.subplots(figsize=(17,14))\n",
        "plt.title('Women Clothing Review Correlation')\n",
        "\n",
        "#Draw the heatmap using seaborn\n",
        "sns.heatmap(reviewData2.corr(),linewidths=0.15,vmax=1.0,square=True,cmap='YlGnBu',linecolor='black',annot=True)\n",
        "b,t = plt.ylim() # discover the values for bottom and top\n",
        "b += 0.75 # add 0.75 to the bottom\n",
        "t -= 0.75 # subtract 0.75 from the top\n",
        "plt.ylim(b, t) # update the ylim(bottom,top) values\n",
        "plt.show()\n",
        "\n",
        "#The resulted heatmap shows that the closer it is to 1, the stronger the correlation.\n",
        "#Let's ignore all the attributes with a correlation of 1 as they are 100% directly correlated to themselves\n",
        "#Rating and Recommended columns have a .79 strong positive correlation rate - meaning we can look deeper into how does rating/recommended column effect the other\n",
        "#We can also see some such as Rating & Clothing ID and Clothing ID & Recommended have a negative correlation - although there is a low correlation rate, that does not meang no correlations exist."
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QA2gBTF3dRguZ9S5HRPQBZn3d9SRJkiStvVKss2MhnYCPq9yfwoqZu5e7CHg6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\nVf4fKGUYIPqOrTsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1224x1008 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}