Womens_online_clothing_reviews_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "womenc=pd.read_csv(\"Womens Clothing E-Commerce Reviews.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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                    Title  \\\n",
       "0           0          767   33                      NaN   \n",
       "1           1         1080   34                      NaN   \n",
       "2           2         1077   60  Some major design flaws   \n",
       "3           3         1049   50         My favorite buy!   \n",
       "4           4          847   47         Flattering shirt   \n",
       "\n",
       "                                         Review Text  Rating  Recommended IND  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4                1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5                1   \n",
       "2  I had such high hopes for this dress and reall...       3                0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5                1   \n",
       "4  This shirt is very flattering to all due to th...       5                1   \n",
       "\n",
       "   Positive Feedback Count   Division Name Department Name Class Name  \n",
       "0                        0       Initmates        Intimate  Intimates  \n",
       "1                        4         General         Dresses    Dresses  \n",
       "2                        0         General         Dresses    Dresses  \n",
       "3                        0  General Petite         Bottoms      Pants  \n",
       "4                        6         General            Tops    Blouses  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "womenc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1af18c2e4a8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.countplot(y='Rating',hue='Class Name',data=womenc).legend(loc='center left', bbox_to_anchor=(1,0.5))"
   ]
  },
  {
   "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>Clothing ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Positive Feedback Count</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended IND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clothing ID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.017972</td>\n",
       "      <td>0.046356</td>\n",
       "      <td>-0.018879</td>\n",
       "      <td>-0.016206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.017972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.043079</td>\n",
       "      <td>0.026831</td>\n",
       "      <td>0.030622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Positive Feedback Count</th>\n",
       "      <td>0.046356</td>\n",
       "      <td>0.043079</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.064961</td>\n",
       "      <td>-0.069045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rating</th>\n",
       "      <td>-0.018879</td>\n",
       "      <td>0.026831</td>\n",
       "      <td>-0.064961</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recommended IND</th>\n",
       "      <td>-0.016206</td>\n",
       "      <td>0.030622</td>\n",
       "      <td>-0.069045</td>\n",
       "      <td>0.792336</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Clothing ID       Age  Positive Feedback Count  \\\n",
       "Clothing ID                 1.000000  0.017972                 0.046356   \n",
       "Age                         0.017972  1.000000                 0.043079   \n",
       "Positive Feedback Count     0.046356  0.043079                 1.000000   \n",
       "Rating                     -0.018879  0.026831                -0.064961   \n",
       "Recommended IND            -0.016206  0.030622                -0.069045   \n",
       "\n",
       "                           Rating  Recommended IND  \n",
       "Clothing ID             -0.018879        -0.016206  \n",
       "Age                      0.026831         0.030622  \n",
       "Positive Feedback Count -0.064961        -0.069045  \n",
       "Rating                   1.000000         0.792336  \n",
       "Recommended IND          0.792336         1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corRel=womenc[[\"Clothing ID\",\"Age\",\"Positive Feedback Count\",\"Rating\",\"Recommended IND\"]].corr()\n",
    "corRel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1af18ffba58>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corRel,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1af1912a470>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask=np.array(corRel)\n",
    "mask[np.tril_indices_from(mask)]=False\n",
    "sns.heatmap(corRel,mask=mask,vmax=8,square=True,annot=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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                    Title  \\\n",
       "0           0          767   33                      NaN   \n",
       "1           1         1080   34                      NaN   \n",
       "2           2         1077   60  Some major design flaws   \n",
       "3           3         1049   50         My favorite buy!   \n",
       "4           4          847   47         Flattering shirt   \n",
       "\n",
       "                                         Review Text  Rating  Recommended IND  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4                1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5                1   \n",
       "2  I had such high hopes for this dress and reall...       3                0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5                1   \n",
       "4  This shirt is very flattering to all due to th...       5                1   \n",
       "\n",
       "   Positive Feedback Count   Division Name Department Name Class Name  \n",
       "0                        0       Initmates        Intimate  Intimates  \n",
       "1                        4         General         Dresses    Dresses  \n",
       "2                        0         General         Dresses    Dresses  \n",
       "3                        0  General Petite         Bottoms      Pants  \n",
       "4                        6         General            Tops    Blouses  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "womenc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'Cloth_ID', 'Age', 'Title', 'Reviews', 'Rating',\n",
      "       'Recommended', 'pos_feed_count', 'Div_Name', 'Dept_Name', 'Class Name'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "womenc.rename(columns={'Clothing ID':'Cloth_ID','Positive Feedback Count':'pos_feed_count',\n",
    "                       'Review Text':'Reviews','Recommended IND':'Recommended',\n",
    "                       'Department Name':'Dept_Name','Division Name':'Div_Name'},\n",
    "             inplace=True)\n",
    "print(womenc.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "WoW=womenc.drop(womenc.columns[0],axis=1)  #dropping extra index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>Div_Name</th>\n",
       "      <th>Dept_Name</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>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>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>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": [
       "   Cloth_ID  Age                    Title  \\\n",
       "0       767   33                      NaN   \n",
       "1      1080   34                      NaN   \n",
       "2      1077   60  Some major design flaws   \n",
       "3      1049   50         My favorite buy!   \n",
       "4       847   47         Flattering shirt   \n",
       "\n",
       "                                             Reviews  Rating  Recommended  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4            1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5            1   \n",
       "2  I had such high hopes for this dress and reall...       3            0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5            1   \n",
       "4  This shirt is very flattering to all due to th...       5            1   \n",
       "\n",
       "   pos_feed_count        Div_Name Dept_Name Class Name  \n",
       "0               0       Initmates  Intimate  Intimates  \n",
       "1               4         General   Dresses    Dresses  \n",
       "2               0         General   Dresses    Dresses  \n",
       "3               0  General Petite   Bottoms      Pants  \n",
       "4               6         General      Tops    Blouses  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>pos_feed_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cloth_ID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.017972</td>\n",
       "      <td>-0.018879</td>\n",
       "      <td>-0.016206</td>\n",
       "      <td>0.046356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.017972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026831</td>\n",
       "      <td>0.030622</td>\n",
       "      <td>0.043079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rating</th>\n",
       "      <td>-0.018879</td>\n",
       "      <td>0.026831</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792336</td>\n",
       "      <td>-0.064961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recommended</th>\n",
       "      <td>-0.016206</td>\n",
       "      <td>0.030622</td>\n",
       "      <td>0.792336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.069045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pos_feed_count</th>\n",
       "      <td>0.046356</td>\n",
       "      <td>0.043079</td>\n",
       "      <td>-0.064961</td>\n",
       "      <td>-0.069045</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Cloth_ID       Age    Rating  Recommended  pos_feed_count\n",
       "Cloth_ID        1.000000  0.017972 -0.018879    -0.016206        0.046356\n",
       "Age             0.017972  1.000000  0.026831     0.030622        0.043079\n",
       "Rating         -0.018879  0.026831  1.000000     0.792336       -0.064961\n",
       "Recommended    -0.016206  0.030622  0.792336     1.000000       -0.069045\n",
       "pos_feed_count  0.046356  0.043079 -0.064961    -0.069045        1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering to convert strings into numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>Div_Name</th>\n",
       "      <th>Dept_Name</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>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>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>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": [
       "   Cloth_ID  Age                    Title  \\\n",
       "0       767   33                      NaN   \n",
       "1      1080   34                      NaN   \n",
       "2      1077   60  Some major design flaws   \n",
       "3      1049   50         My favorite buy!   \n",
       "4       847   47         Flattering shirt   \n",
       "\n",
       "                                             Reviews  Rating  Recommended  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4            1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5            1   \n",
       "2  I had such high hopes for this dress and reall...       3            0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5            1   \n",
       "4  This shirt is very flattering to all due to th...       5            1   \n",
       "\n",
       "   pos_feed_count        Div_Name Dept_Name Class Name  \n",
       "0               0       Initmates  Intimate  Intimates  \n",
       "1               4         General   Dresses    Dresses  \n",
       "2               0         General   Dresses    Dresses  \n",
       "3               0  General Petite   Bottoms      Pants  \n",
       "4               6         General      Tops    Blouses  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>Div_Name</th>\n",
       "      <th>Dept_Name</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cloth_ID    Age  Title  Reviews  Rating  Recommended  pos_feed_count  \\\n",
       "0     False  False   True    False   False        False           False   \n",
       "1     False  False   True    False   False        False           False   \n",
       "2     False  False  False    False   False        False           False   \n",
       "3     False  False  False    False   False        False           False   \n",
       "4     False  False  False    False   False        False           False   \n",
       "\n",
       "   Div_Name  Dept_Name  Class Name  \n",
       "0     False      False       False  \n",
       "1     False      False       False  \n",
       "2     False      False       False  \n",
       "3     False      False       False  \n",
       "4     False      False       False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW.isnull().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning(replacing null values with default values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "WoW[\"Title\"].fillna(\"Basic\", inplace=True)\n",
    "WoW[\"Reviews\"].fillna(\"Satisfactory\", inplace=True)\n",
    "WoW[\"Div_Name\"].fillna(\"General\", inplace=True)\n",
    "WoW[\"Dept_Name\"].fillna(\"Clothing\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cloth_ID           0\n",
       "Age                0\n",
       "Title              0\n",
       "Reviews            0\n",
       "Rating             0\n",
       "Recommended        0\n",
       "pos_feed_count     0\n",
       "Div_Name           0\n",
       "Dept_Name          0\n",
       "Class Name        14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>Div_Name</th>\n",
       "      <th>Dept_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>33</td>\n",
       "      <td>Basic</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1080</td>\n",
       "      <td>34</td>\n",
       "      <td>Basic</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cloth_ID  Age                    Title  \\\n",
       "0       767   33                    Basic   \n",
       "1      1080   34                    Basic   \n",
       "2      1077   60  Some major design flaws   \n",
       "3      1049   50         My favorite buy!   \n",
       "4       847   47         Flattering shirt   \n",
       "\n",
       "                                             Reviews  Rating  Recommended  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4            1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5            1   \n",
       "2  I had such high hopes for this dress and reall...       3            0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5            1   \n",
       "4  This shirt is very flattering to all due to th...       5            1   \n",
       "\n",
       "   pos_feed_count        Div_Name Dept_Name  \n",
       "0               0       Initmates  Intimate  \n",
       "1               4         General   Dresses  \n",
       "2               0         General   Dresses  \n",
       "3               0  General Petite   Bottoms  \n",
       "4               6         General      Tops  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW=WoW.drop(WoW.columns[9],axis=1)\n",
    "WoW.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1af191ea780>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,10))\n",
    "sns.countplot(y='Dept_Name',data=WoW, hue='Div_Name').legend(loc='center left', bbox_to_anchor=(1,0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23486 entries, 0 to 23485\n",
      "Data columns (total 9 columns):\n",
      "Cloth_ID          23486 non-null int64\n",
      "Age               23486 non-null int64\n",
      "Title             23486 non-null object\n",
      "Reviews           23486 non-null object\n",
      "Rating            23486 non-null int64\n",
      "Recommended       23486 non-null int64\n",
      "pos_feed_count    23486 non-null int64\n",
      "Div_Name          23486 non-null object\n",
      "Dept_Name         23486 non-null object\n",
      "dtypes: int64(5), object(4)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "WoW.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tops        10468\n",
       "Dresses      6319\n",
       "Bottoms      3799\n",
       "Intimate     1735\n",
       "Jackets      1032\n",
       "Trend         119\n",
       "Clothing       14\n",
       "Name: Dept_Name, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW['Dept_Name'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Intimate', 'Dresses', 'Bottoms', 'Tops', 'Jackets', 'Trend',\n",
       "       'Clothing'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dep_name=pd.unique(WoW['Dept_Name'])\n",
    "dep_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "leo=LabelEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TypeError: '<' not supported between instances of 'str' and 'float'\n",
    "this error occurs when we have missing values in our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 2, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dept_labels=leo.fit_transform(WoW['Dept_Name'])\n",
    "dept_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Bottoms',\n",
       " 1: 'Clothing',\n",
       " 2: 'Dresses',\n",
       " 3: 'Intimate',\n",
       " 4: 'Jackets',\n",
       " 5: 'Tops',\n",
       " 6: 'Trend'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mapping of values in dictionaries using dictionary comprehension\n",
    "dept_map={index:label for index, label in enumerate(leo.classes_)}\n",
    "dept_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Cloth_ID</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>Dept_Name</th>\n",
       "      <th>DeptLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>1</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1080</td>\n",
       "      <td>1</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077</td>\n",
       "      <td>0</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1049</td>\n",
       "      <td>1</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>847</td>\n",
       "      <td>1</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cloth_ID  Recommended Dept_Name  DeptLabel\n",
       "0       767            1  Intimate          3\n",
       "1      1080            1   Dresses          2\n",
       "2      1077            0   Dresses          2\n",
       "3      1049            1   Bottoms          0\n",
       "4       847            1      Tops          5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW['DeptLabel']=dept_labels   # adding the column dept_map in the dataframe\n",
    "WoW[['Cloth_ID','Recommended','Dept_Name','DeptLabel']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>Div_Name</th>\n",
       "      <th>Dept_Name</th>\n",
       "      <th>DeptLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13737</th>\n",
       "      <td>1078</td>\n",
       "      <td>50</td>\n",
       "      <td>Love this dress!!</td>\n",
       "      <td>This dress is wonderful. fits perfect. very co...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12670</th>\n",
       "      <td>1110</td>\n",
       "      <td>48</td>\n",
       "      <td>10 lbs. no thanks!</td>\n",
       "      <td>I was very excited to receive this dress becau...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21523</th>\n",
       "      <td>975</td>\n",
       "      <td>37</td>\n",
       "      <td>So close</td>\n",
       "      <td>I really, really wanted to love this jacket. u...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Jackets</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10787</th>\n",
       "      <td>1078</td>\n",
       "      <td>35</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>132</td>\n",
       "      <td>45</td>\n",
       "      <td>Amazing!</td>\n",
       "      <td>I went out on a limb ordering this romper. it'...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8175</th>\n",
       "      <td>831</td>\n",
       "      <td>24</td>\n",
       "      <td>Easy breezy :)</td>\n",
       "      <td>Gorgeous top! very beautiful and easy. can be ...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12513</th>\n",
       "      <td>936</td>\n",
       "      <td>47</td>\n",
       "      <td>Gorgeous sweater!</td>\n",
       "      <td>I purchased all three colors! this sweater is ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8853</th>\n",
       "      <td>283</td>\n",
       "      <td>37</td>\n",
       "      <td>Very flattering shirt</td>\n",
       "      <td>I was apprehensive because of the long tail at...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5183</th>\n",
       "      <td>927</td>\n",
       "      <td>39</td>\n",
       "      <td>A tad scratchier than most</td>\n",
       "      <td>Also, does run big, though i felt te xs seemed...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10014</th>\n",
       "      <td>1110</td>\n",
       "      <td>46</td>\n",
       "      <td>Violetta dress</td>\n",
       "      <td>Thin for this late in the season, but the colo...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10129</th>\n",
       "      <td>872</td>\n",
       "      <td>53</td>\n",
       "      <td>Great casual top</td>\n",
       "      <td>Love this top for casual days. fits perfectly,...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22641</th>\n",
       "      <td>895</td>\n",
       "      <td>49</td>\n",
       "      <td>Basic</td>\n",
       "      <td>I loved the style so i ordered on line. it run...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12141</th>\n",
       "      <td>865</td>\n",
       "      <td>26</td>\n",
       "      <td>Material too flimsy</td>\n",
       "      <td>I wanted to love this as i love bright colored...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17346</th>\n",
       "      <td>1022</td>\n",
       "      <td>42</td>\n",
       "      <td>Super comfy , great fit</td>\n",
       "      <td>I like these pants a lot. got one pair in blac...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>General</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22217</th>\n",
       "      <td>717</td>\n",
       "      <td>25</td>\n",
       "      <td>Comfort and style</td>\n",
       "      <td>The color of these shorts is even more beautif...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5221</th>\n",
       "      <td>1099</td>\n",
       "      <td>62</td>\n",
       "      <td>I don't get it...</td>\n",
       "      <td>I know this is supposed to be a loose, flowy f...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6558</th>\n",
       "      <td>1078</td>\n",
       "      <td>69</td>\n",
       "      <td>Unique style</td>\n",
       "      <td>I love the feel of the fabric and design of th...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13470</th>\n",
       "      <td>1024</td>\n",
       "      <td>63</td>\n",
       "      <td>Cute but tight</td>\n",
       "      <td>These are pretty good high-rise jeans--probabl...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17900</th>\n",
       "      <td>861</td>\n",
       "      <td>47</td>\n",
       "      <td>Complimentary color</td>\n",
       "      <td>After seeing this top in the catalog i also sp...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5120</th>\n",
       "      <td>678</td>\n",
       "      <td>40</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Very pretty and comfortable. it was not obviou...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3771</th>\n",
       "      <td>1025</td>\n",
       "      <td>37</td>\n",
       "      <td>Worth every penny!</td>\n",
       "      <td>These jeans are amazing! i just got them from ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>General</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19215</th>\n",
       "      <td>1020</td>\n",
       "      <td>38</td>\n",
       "      <td>Fun skirt with a little twist</td>\n",
       "      <td>I bought this skirt for an end of summer \"cari...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15834</th>\n",
       "      <td>1028</td>\n",
       "      <td>48</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21758</th>\n",
       "      <td>863</td>\n",
       "      <td>40</td>\n",
       "      <td>Not sure...</td>\n",
       "      <td>I was expecting to love this based on the revi...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12618</th>\n",
       "      <td>820</td>\n",
       "      <td>51</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18812</th>\n",
       "      <td>862</td>\n",
       "      <td>35</td>\n",
       "      <td>Classy cowlneck</td>\n",
       "      <td>Absolutely love this sweater! it is a tad bit ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2590</th>\n",
       "      <td>975</td>\n",
       "      <td>37</td>\n",
       "      <td>Basic</td>\n",
       "      <td>This jacket fits just a tad big, but it's quit...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Jackets</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15380</th>\n",
       "      <td>1083</td>\n",
       "      <td>40</td>\n",
       "      <td>Cute casual dress.</td>\n",
       "      <td>This is a cute casual dress that could be worn...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15096</th>\n",
       "      <td>881</td>\n",
       "      <td>21</td>\n",
       "      <td>Pretty but the doesn't fit right.</td>\n",
       "      <td>Alright, so i really love this shirt. but i am...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3485</th>\n",
       "      <td>1056</td>\n",
       "      <td>46</td>\n",
       "      <td>Pilcro stet chino capris</td>\n",
       "      <td>I'm not much of a chino girl, but these fit li...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2131</th>\n",
       "      <td>862</td>\n",
       "      <td>54</td>\n",
       "      <td>Basic</td>\n",
       "      <td>I love everything about this top! the fabric, ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3256</th>\n",
       "      <td>512</td>\n",
       "      <td>58</td>\n",
       "      <td>Cute and sweet</td>\n",
       "      <td>Ordered my regular size s early this week and ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>1020</td>\n",
       "      <td>41</td>\n",
       "      <td>Runs very small</td>\n",
       "      <td>My waist measures 28\" and the size 4 is a snug...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8731</th>\n",
       "      <td>836</td>\n",
       "      <td>65</td>\n",
       "      <td>Beautiful quality and fit! summertime!</td>\n",
       "      <td>I held my breath as i slid this on for the fir...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13021</th>\n",
       "      <td>347</td>\n",
       "      <td>29</td>\n",
       "      <td>Worth the splurge</td>\n",
       "      <td>This is different from any bra i've had before...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19077</th>\n",
       "      <td>452</td>\n",
       "      <td>62</td>\n",
       "      <td>Terrace robe</td>\n",
       "      <td>Absolutely perfect for summer. great weight, l...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4858</th>\n",
       "      <td>829</td>\n",
       "      <td>28</td>\n",
       "      <td>Ski jumpers</td>\n",
       "      <td>I love the design on this top- so playful! i w...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15550</th>\n",
       "      <td>926</td>\n",
       "      <td>37</td>\n",
       "      <td>Thick and warm</td>\n",
       "      <td>Really nice thick fabric, yet lays really nice...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20663</th>\n",
       "      <td>726</td>\n",
       "      <td>29</td>\n",
       "      <td>Love!</td>\n",
       "      <td>So great! soft and a great sweater but still g...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>1033</td>\n",
       "      <td>30</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Love these jeans however they get a little sag...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13822</th>\n",
       "      <td>974</td>\n",
       "      <td>32</td>\n",
       "      <td>Goes with everything</td>\n",
       "      <td>This is a beautifully made jacket. i ended up ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Jackets</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6327</th>\n",
       "      <td>831</td>\n",
       "      <td>38</td>\n",
       "      <td>Basic</td>\n",
       "      <td>I love this shirt. the colors are just as depi...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9926</th>\n",
       "      <td>1115</td>\n",
       "      <td>65</td>\n",
       "      <td>Fab coat!</td>\n",
       "      <td>This is a beautiful coat and the fit and weigh...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Jackets</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12899</th>\n",
       "      <td>1078</td>\n",
       "      <td>36</td>\n",
       "      <td>Cute dress but terrible design flaw</td>\n",
       "      <td>I purchased this dress yesterday and it's cute...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5010</th>\n",
       "      <td>675</td>\n",
       "      <td>49</td>\n",
       "      <td>Basic</td>\n",
       "      <td>I was not going to purchase this jacket thinki...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11197</th>\n",
       "      <td>375</td>\n",
       "      <td>29</td>\n",
       "      <td>Wardrobe staple for me!</td>\n",
       "      <td>Super soft and stylish. it's a major step up f...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18246</th>\n",
       "      <td>862</td>\n",
       "      <td>53</td>\n",
       "      <td>Pretty...</td>\n",
       "      <td>Probably should have returned this but it's ju...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>999</td>\n",
       "      <td>36</td>\n",
       "      <td>Better in person</td>\n",
       "      <td>This skirt has a beautiful, flattering feminin...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>General</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16343</th>\n",
       "      <td>1081</td>\n",
       "      <td>41</td>\n",
       "      <td>Love this dress</td>\n",
       "      <td>I've had my eye on this dress ever since i saw...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22216</th>\n",
       "      <td>784</td>\n",
       "      <td>33</td>\n",
       "      <td>Best tights!</td>\n",
       "      <td>These are the best tights! last year i bought ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>936</td>\n",
       "      <td>58</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Absolutely beautiful. as soon as i saw the col...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18255</th>\n",
       "      <td>361</td>\n",
       "      <td>22</td>\n",
       "      <td>Cannot get enough of this!</td>\n",
       "      <td>This is beautiful and fits perfect. it's even ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10674</th>\n",
       "      <td>1002</td>\n",
       "      <td>58</td>\n",
       "      <td>Versital and comfortable</td>\n",
       "      <td>This skirt is a great addition to my wardrobe....</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18969</th>\n",
       "      <td>1084</td>\n",
       "      <td>46</td>\n",
       "      <td>Needs pockets! runs large</td>\n",
       "      <td>Even though this is an online exclusive, i was...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4882</th>\n",
       "      <td>463</td>\n",
       "      <td>67</td>\n",
       "      <td>Great aqua print</td>\n",
       "      <td>Love the aqua print, which looks vaguely japan...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17736</th>\n",
       "      <td>1077</td>\n",
       "      <td>44</td>\n",
       "      <td>Lovely, perfect</td>\n",
       "      <td>I am a shorter woman (5'3\") and typically a si...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21106</th>\n",
       "      <td>863</td>\n",
       "      <td>31</td>\n",
       "      <td>Basic</td>\n",
       "      <td>I love this shirt. i have it in red. i found t...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21451</th>\n",
       "      <td>576</td>\n",
       "      <td>36</td>\n",
       "      <td>So cute but not a great fit</td>\n",
       "      <td>I wear an 8-10 and these are a little big on m...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21404</th>\n",
       "      <td>1033</td>\n",
       "      <td>42</td>\n",
       "      <td>Cute ankle jean!</td>\n",
       "      <td>These are great, but unfortunately ran small f...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12334</th>\n",
       "      <td>1022</td>\n",
       "      <td>47</td>\n",
       "      <td>Flattering!</td>\n",
       "      <td>I was returning some things when i saw these j...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>General</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1174 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cloth_ID  Age                                   Title  \\\n",
       "13737      1078   50                       Love this dress!!   \n",
       "12670      1110   48                      10 lbs. no thanks!   \n",
       "21523       975   37                                So close   \n",
       "10787      1078   35                                   Basic   \n",
       "2347        132   45                                Amazing!   \n",
       "8175        831   24                          Easy breezy :)   \n",
       "12513       936   47                       Gorgeous sweater!   \n",
       "8853        283   37                   Very flattering shirt   \n",
       "5183        927   39              A tad scratchier than most   \n",
       "10014      1110   46                          Violetta dress   \n",
       "10129       872   53                        Great casual top   \n",
       "22641       895   49                                   Basic   \n",
       "12141       865   26                     Material too flimsy   \n",
       "17346      1022   42                 Super comfy , great fit   \n",
       "22217       717   25                       Comfort and style   \n",
       "5221       1099   62                       I don't get it...   \n",
       "6558       1078   69                            Unique style   \n",
       "13470      1024   63                          Cute but tight   \n",
       "17900       861   47                     Complimentary color   \n",
       "5120        678   40                                   Basic   \n",
       "3771       1025   37                      Worth every penny!   \n",
       "19215      1020   38           Fun skirt with a little twist   \n",
       "15834      1028   48                                   Basic   \n",
       "21758       863   40                             Not sure...   \n",
       "12618       820   51                                   Basic   \n",
       "18812       862   35                         Classy cowlneck   \n",
       "2590        975   37                                   Basic   \n",
       "15380      1083   40                      Cute casual dress.   \n",
       "15096       881   21       Pretty but the doesn't fit right.   \n",
       "3485       1056   46                Pilcro stet chino capris   \n",
       "...         ...  ...                                     ...   \n",
       "2131        862   54                                   Basic   \n",
       "3256        512   58                          Cute and sweet   \n",
       "189        1020   41                         Runs very small   \n",
       "8731        836   65  Beautiful quality and fit! summertime!   \n",
       "13021       347   29                       Worth the splurge   \n",
       "19077       452   62                            Terrace robe   \n",
       "4858        829   28                             Ski jumpers   \n",
       "15550       926   37                          Thick and warm   \n",
       "20663       726   29                                   Love!   \n",
       "3258       1033   30                                   Basic   \n",
       "13822       974   32                    Goes with everything   \n",
       "6327        831   38                                   Basic   \n",
       "9926       1115   65                               Fab coat!   \n",
       "12899      1078   36     Cute dress but terrible design flaw   \n",
       "5010        675   49                                   Basic   \n",
       "11197       375   29                 Wardrobe staple for me!   \n",
       "18246       862   53                               Pretty...   \n",
       "3019        999   36                        Better in person   \n",
       "16343      1081   41                         Love this dress   \n",
       "22216       784   33                            Best tights!   \n",
       "971         936   58                                   Basic   \n",
       "18255       361   22              Cannot get enough of this!   \n",
       "10674      1002   58                Versital and comfortable   \n",
       "18969      1084   46               Needs pockets! runs large   \n",
       "4882        463   67                        Great aqua print   \n",
       "17736      1077   44                         Lovely, perfect   \n",
       "21106       863   31                                   Basic   \n",
       "21451       576   36             So cute but not a great fit   \n",
       "21404      1033   42                        Cute ankle jean!   \n",
       "12334      1022   47                             Flattering!   \n",
       "\n",
       "                                                 Reviews  Rating  Recommended  \\\n",
       "13737  This dress is wonderful. fits perfect. very co...       5            1   \n",
       "12670  I was very excited to receive this dress becau...       1            0   \n",
       "21523  I really, really wanted to love this jacket. u...       3            0   \n",
       "10787                                       Satisfactory       5            1   \n",
       "2347   I went out on a limb ordering this romper. it'...       5            1   \n",
       "8175   Gorgeous top! very beautiful and easy. can be ...       4            1   \n",
       "12513  I purchased all three colors! this sweater is ...       5            1   \n",
       "8853   I was apprehensive because of the long tail at...       5            1   \n",
       "5183   Also, does run big, though i felt te xs seemed...       4            0   \n",
       "10014  Thin for this late in the season, but the colo...       5            1   \n",
       "10129  Love this top for casual days. fits perfectly,...       5            1   \n",
       "22641  I loved the style so i ordered on line. it run...       2            0   \n",
       "12141  I wanted to love this as i love bright colored...       2            0   \n",
       "17346  I like these pants a lot. got one pair in blac...       5            1   \n",
       "22217  The color of these shorts is even more beautif...       5            1   \n",
       "5221   I know this is supposed to be a loose, flowy f...       3            0   \n",
       "6558   I love the feel of the fabric and design of th...       4            1   \n",
       "13470  These are pretty good high-rise jeans--probabl...       4            1   \n",
       "17900  After seeing this top in the catalog i also sp...       5            1   \n",
       "5120   Very pretty and comfortable. it was not obviou...       5            1   \n",
       "3771   These jeans are amazing! i just got them from ...       5            1   \n",
       "19215  I bought this skirt for an end of summer \"cari...       5            1   \n",
       "15834                                       Satisfactory       5            1   \n",
       "21758  I was expecting to love this based on the revi...       4            1   \n",
       "12618                                       Satisfactory       5            1   \n",
       "18812  Absolutely love this sweater! it is a tad bit ...       5            1   \n",
       "2590   This jacket fits just a tad big, but it's quit...       4            1   \n",
       "15380  This is a cute casual dress that could be worn...       4            1   \n",
       "15096  Alright, so i really love this shirt. but i am...       4            1   \n",
       "3485   I'm not much of a chino girl, but these fit li...       5            1   \n",
       "...                                                  ...     ...          ...   \n",
       "2131   I love everything about this top! the fabric, ...       5            1   \n",
       "3256   Ordered my regular size s early this week and ...       5            1   \n",
       "189    My waist measures 28\" and the size 4 is a snug...       4            1   \n",
       "8731   I held my breath as i slid this on for the fir...       5            1   \n",
       "13021  This is different from any bra i've had before...       5            1   \n",
       "19077  Absolutely perfect for summer. great weight, l...       5            1   \n",
       "4858   I love the design on this top- so playful! i w...       4            1   \n",
       "15550  Really nice thick fabric, yet lays really nice...       5            1   \n",
       "20663  So great! soft and a great sweater but still g...       5            1   \n",
       "3258   Love these jeans however they get a little sag...       4            1   \n",
       "13822  This is a beautifully made jacket. i ended up ...       5            1   \n",
       "6327   I love this shirt. the colors are just as depi...       5            1   \n",
       "9926   This is a beautiful coat and the fit and weigh...       4            1   \n",
       "12899  I purchased this dress yesterday and it's cute...       3            0   \n",
       "5010   I was not going to purchase this jacket thinki...       5            1   \n",
       "11197  Super soft and stylish. it's a major step up f...       5            1   \n",
       "18246  Probably should have returned this but it's ju...       3            1   \n",
       "3019   This skirt has a beautiful, flattering feminin...       5            1   \n",
       "16343  I've had my eye on this dress ever since i saw...       5            1   \n",
       "22216  These are the best tights! last year i bought ...       5            1   \n",
       "971    Absolutely beautiful. as soon as i saw the col...       5            1   \n",
       "18255  This is beautiful and fits perfect. it's even ...       5            1   \n",
       "10674  This skirt is a great addition to my wardrobe....       5            1   \n",
       "18969  Even though this is an online exclusive, i was...       4            1   \n",
       "4882   Love the aqua print, which looks vaguely japan...       5            1   \n",
       "17736  I am a shorter woman (5'3\") and typically a si...       5            1   \n",
       "21106  I love this shirt. i have it in red. i found t...       5            1   \n",
       "21451  I wear an 8-10 and these are a little big on m...       3            0   \n",
       "21404  These are great, but unfortunately ran small f...       5            1   \n",
       "12334  I was returning some things when i saw these j...       5            1   \n",
       "\n",
       "       pos_feed_count        Div_Name Dept_Name  DeptLabel  \n",
       "13737               0  General Petite   Dresses          2  \n",
       "12670              25         General   Dresses          2  \n",
       "21523               1  General Petite   Jackets          4  \n",
       "10787               0         General   Dresses          2  \n",
       "2347                0         General   Bottoms          0  \n",
       "8175                0         General      Tops          5  \n",
       "12513               1  General Petite      Tops          5  \n",
       "8853                0       Initmates  Intimate          3  \n",
       "5183                0         General      Tops          5  \n",
       "10014               0         General   Dresses          2  \n",
       "10129               0  General Petite      Tops          5  \n",
       "22641               0         General      Tops          5  \n",
       "12141               1         General      Tops          5  \n",
       "17346               6         General   Bottoms          0  \n",
       "22217               0         General   Bottoms          0  \n",
       "5221                1         General   Dresses          2  \n",
       "6558                0  General Petite   Dresses          2  \n",
       "13470               0         General   Bottoms          0  \n",
       "17900               1         General      Tops          5  \n",
       "5120                2       Initmates  Intimate          3  \n",
       "3771                1         General   Bottoms          0  \n",
       "19215               0         General   Bottoms          0  \n",
       "15834               0  General Petite   Bottoms          0  \n",
       "21758               0  General Petite      Tops          5  \n",
       "12618               0         General      Tops          5  \n",
       "18812               0  General Petite      Tops          5  \n",
       "2590                0         General   Jackets          4  \n",
       "15380               0         General   Dresses          2  \n",
       "15096               4         General      Tops          5  \n",
       "3485                2  General Petite   Bottoms          0  \n",
       "...               ...             ...       ...        ...  \n",
       "2131                0         General      Tops          5  \n",
       "3256                2       Initmates  Intimate          3  \n",
       "189                 4  General Petite   Bottoms          0  \n",
       "8731                7         General      Tops          5  \n",
       "13021               0       Initmates  Intimate          3  \n",
       "19077               1       Initmates  Intimate          3  \n",
       "4858                0  General Petite      Tops          5  \n",
       "15550               0         General      Tops          5  \n",
       "20663               0       Initmates  Intimate          3  \n",
       "3258                1  General Petite   Bottoms          0  \n",
       "13822               0         General   Jackets          4  \n",
       "6327                0         General      Tops          5  \n",
       "9926                0         General   Jackets          4  \n",
       "12899               0  General Petite   Dresses          2  \n",
       "5010                2       Initmates  Intimate          3  \n",
       "11197               3       Initmates  Intimate          3  \n",
       "18246               0         General      Tops          5  \n",
       "3019                5         General   Bottoms          0  \n",
       "16343               0  General Petite   Dresses          2  \n",
       "22216               1       Initmates  Intimate          3  \n",
       "971                 0         General      Tops          5  \n",
       "18255               0       Initmates  Intimate          3  \n",
       "10674               0         General   Bottoms          0  \n",
       "18969               1         General   Dresses          2  \n",
       "4882                2       Initmates  Intimate          3  \n",
       "17736               0  General Petite   Dresses          2  \n",
       "21106               0         General      Tops          5  \n",
       "21451               0         General   Bottoms          0  \n",
       "21404               0  General Petite   Bottoms          0  \n",
       "12334               4         General   Bottoms          0  \n",
       "\n",
       "[1174 rows x 10 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW.sample(frac=0.05,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dilip\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:2262: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1af1925a5c0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x324 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.jointplot(data=WoW, x='Age',y='DeptLabel',kind='reg',ylim=(0,2),color='red',size=4.5,ratio=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Modelling through linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=WoW[['Rating']]\n",
    "x=WoW[['DeptLabel']]\n",
    "regmodel=linear_model.LinearRegression()\n",
    "regmodel.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.16674983]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#providing DeptLabel 4 and checking if it provides the rating or not\n",
    "newp=5\n",
    "newp=np.array(newp)\n",
    "newp=newp.reshape(-1,1)\n",
    "pred=regmodel.predict(newp)\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We conclude that DeptLabel=4 i.e. Jackets have mostly the rating of 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>DeptLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23486.000000</td>\n",
       "      <td>23486.000000</td>\n",
       "      <td>23486.000000</td>\n",
       "      <td>23486.000000</td>\n",
       "      <td>23486.000000</td>\n",
       "      <td>23486.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>918.118709</td>\n",
       "      <td>43.198544</td>\n",
       "      <td>4.196032</td>\n",
       "      <td>0.822362</td>\n",
       "      <td>2.535936</td>\n",
       "      <td>3.195052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>203.298980</td>\n",
       "      <td>12.279544</td>\n",
       "      <td>1.110031</td>\n",
       "      <td>0.382216</td>\n",
       "      <td>5.702202</td>\n",
       "      <td>1.887257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>861.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>936.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1078.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1205.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Cloth_ID           Age        Rating   Recommended  pos_feed_count  \\\n",
       "count  23486.000000  23486.000000  23486.000000  23486.000000    23486.000000   \n",
       "mean     918.118709     43.198544      4.196032      0.822362        2.535936   \n",
       "std      203.298980     12.279544      1.110031      0.382216        5.702202   \n",
       "min        0.000000     18.000000      1.000000      0.000000        0.000000   \n",
       "25%      861.000000     34.000000      4.000000      1.000000        0.000000   \n",
       "50%      936.000000     41.000000      5.000000      1.000000        1.000000   \n",
       "75%     1078.000000     52.000000      5.000000      1.000000        3.000000   \n",
       "max     1205.000000     99.000000      5.000000      1.000000      122.000000   \n",
       "\n",
       "          DeptLabel  \n",
       "count  23486.000000  \n",
       "mean       3.195052  \n",
       "std        1.887257  \n",
       "min        0.000000  \n",
       "25%        2.000000  \n",
       "50%        3.000000  \n",
       "75%        5.000000  \n",
       "max        6.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Through test_train_split model \n",
    "### Training 20% of our entire data and testing on the rest of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>Div_Name</th>\n",
       "      <th>Dept_Name</th>\n",
       "      <th>DeptLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>33</td>\n",
       "      <td>Basic</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1080</td>\n",
       "      <td>34</td>\n",
       "      <td>Basic</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cloth_ID  Age                    Title  \\\n",
       "0       767   33                    Basic   \n",
       "1      1080   34                    Basic   \n",
       "2      1077   60  Some major design flaws   \n",
       "3      1049   50         My favorite buy!   \n",
       "4       847   47         Flattering shirt   \n",
       "\n",
       "                                             Reviews  Rating  Recommended  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4            1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5            1   \n",
       "2  I had such high hopes for this dress and reall...       3            0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5            1   \n",
       "4  This shirt is very flattering to all due to th...       5            1   \n",
       "\n",
       "   pos_feed_count        Div_Name Dept_Name  DeptLabel  \n",
       "0               0       Initmates  Intimate          3  \n",
       "1               4         General   Dresses          2  \n",
       "2               0         General   Dresses          2  \n",
       "3               0  General Petite   Bottoms          0  \n",
       "4               6         General      Tops          5  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=WoW[['Cloth_ID','Age','Rating','pos_feed_count','DeptLabel']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=WoW[['Recommended']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#WoW2=pd.DataFrame(data=WoW)  #creating a new dataframe so that original dataframe not affected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.20,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>DeptLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3211</th>\n",
       "      <td>862</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8841</th>\n",
       "      <td>1025</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>984</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5857</th>\n",
       "      <td>1033</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8693</th>\n",
       "      <td>774</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cloth_ID  Age  Rating  pos_feed_count  DeptLabel\n",
       "3211       862   41       5               5          5\n",
       "8841      1025   46       5               0          0\n",
       "291        984   71       5               0          4\n",
       "5857      1033   45       5               0          0\n",
       "8693       774   47       1              20          3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "LRModel=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dilip\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Dilip\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LRModel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing out model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_cl=LRModel.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.82      0.80       823\n",
      "           1       0.96      0.95      0.96      3875\n",
      "\n",
      "   micro avg       0.93      0.93      0.93      4698\n",
      "   macro avg       0.87      0.89      0.88      4698\n",
      "weighted avg       0.93      0.93      0.93      4698\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction_cl))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### creating a new row in dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>DeptLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5463</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cloth_ID  Age  Rating  pos_feed_count  DeptLabel\n",
       "0      5463   23       3               6          7"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=['Cloth_ID','Age','Rating','pos_feed_count','DeptLabel']\n",
    "data=[5463,23,3,6,7]\n",
    "recom=pd.DataFrame([data],columns=x)\n",
    "recom.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>Div_Name</th>\n",
       "      <th>Dept_Name</th>\n",
       "      <th>DeptLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>33</td>\n",
       "      <td>Basic</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1080</td>\n",
       "      <td>34</td>\n",
       "      <td>Basic</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cloth_ID  Age                    Title  \\\n",
       "0       767   33                    Basic   \n",
       "1      1080   34                    Basic   \n",
       "2      1077   60  Some major design flaws   \n",
       "3      1049   50         My favorite buy!   \n",
       "4       847   47         Flattering shirt   \n",
       "\n",
       "                                             Reviews  Rating  Recommended  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4            1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5            1   \n",
       "2  I had such high hopes for this dress and reall...       3            0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5            1   \n",
       "4  This shirt is very flattering to all due to th...       5            1   \n",
       "\n",
       "   pos_feed_count        Div_Name Dept_Name  DeptLabel  \n",
       "0               0       Initmates  Intimate          3  \n",
       "1               4         General   Dresses          2  \n",
       "2               0         General   Dresses          2  \n",
       "3               0  General Petite   Bottoms          0  \n",
       "4               6         General      Tops          5  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_cl=LRModel.predict(recom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(prediction_cl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This shows that given the details the product is not recommended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1af1acb1630>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "WoW['Rating'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1af1ad00ef0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "deptnumber=WoW['Dept_Name'].value_counts()\n",
    "deptnumber[:10].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "WoW2=WoW[WoW['Rating']==5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Cloth_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>pos_feed_count</th>\n",
       "      <th>Div_Name</th>\n",
       "      <th>Dept_Name</th>\n",
       "      <th>DeptLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1080</td>\n",
       "      <td>34</td>\n",
       "      <td>Basic</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\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>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1077</td>\n",
       "      <td>24</td>\n",
       "      <td>Flattering</td>\n",
       "      <td>I love this dress. i usually get an xs but it ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cloth_ID  Age                 Title  \\\n",
       "1      1080   34                 Basic   \n",
       "3      1049   50      My favorite buy!   \n",
       "4       847   47      Flattering shirt   \n",
       "6       858   39  Cagrcoal shimmer fun   \n",
       "8      1077   24            Flattering   \n",
       "\n",
       "                                             Reviews  Rating  Recommended  \\\n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5            1   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5            1   \n",
       "4  This shirt is very flattering to all due to th...       5            1   \n",
       "6  I aded this in my basket at hte last mintue to...       5            1   \n",
       "8  I love this dress. i usually get an xs but it ...       5            1   \n",
       "\n",
       "   pos_feed_count        Div_Name Dept_Name  DeptLabel  \n",
       "1               4         General   Dresses          2  \n",
       "3               0  General Petite   Bottoms          0  \n",
       "4               6         General      Tops          5  \n",
       "6               1  General Petite      Tops          5  \n",
       "8               0         General   Dresses          2  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WoW2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1af1ad66e10>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "WoW2['Dept_Name'].unique()\n",
    "deptnumber=WoW2['Dept_Name'].value_counts()\n",
    "deptnumber[:6].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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