EDA_fiveClasses with Markdown.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Analysis\n",
    "This is an analysis of different types of **Classfication models** performed on Walmart Trip Type classfication problem.\n",
    "\n",
    "Since the number of triptypes were 38, it has been reduced to 5 triptypes which has almost equal bins.\n",
    "\n",
    "This analysis is as follows. \n",
    "1. Feature Engineering.\n",
    "2. Test Train Split\n",
    "3. K Nearest Neighbours Classifier\n",
    "4. Logistic Regression Classifier\n",
    "5. Naive Bayes Classifier\n",
    "6. Decision Tree Classifier\n",
    "7. SVM Classifier\n",
    "8. Random Forest Classifier\n",
    "9. LDA Classifier\n",
    "\n",
    "Each of the above classifier will have reported \n",
    "- classification accuracy\n",
    "- confusion matrix\n",
    "- classification report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TripType</th>\n",
       "      <th>VisitNumber</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Upc</th>\n",
       "      <th>ScanCount</th>\n",
       "      <th>DepartmentDescription</th>\n",
       "      <th>FinelineNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>572389</td>\n",
       "      <td>38</td>\n",
       "      <td>170528</td>\n",
       "      <td>4</td>\n",
       "      <td>4.900005e+09</td>\n",
       "      <td>2</td>\n",
       "      <td>DSD GROCERY</td>\n",
       "      <td>9570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>185055</td>\n",
       "      <td>37</td>\n",
       "      <td>55398</td>\n",
       "      <td>6</td>\n",
       "      <td>2.015450e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>SERVICE DELI</td>\n",
       "      <td>8001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>474946</td>\n",
       "      <td>37</td>\n",
       "      <td>141533</td>\n",
       "      <td>6</td>\n",
       "      <td>4.300020e+09</td>\n",
       "      <td>2</td>\n",
       "      <td>GROCERY DRY GOODS</td>\n",
       "      <td>4306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>412325</td>\n",
       "      <td>36</td>\n",
       "      <td>122269</td>\n",
       "      <td>3</td>\n",
       "      <td>2.279692e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>PERSONAL CARE</td>\n",
       "      <td>5070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>249953</td>\n",
       "      <td>36</td>\n",
       "      <td>73972</td>\n",
       "      <td>2</td>\n",
       "      <td>7.958200e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>DSD GROCERY</td>\n",
       "      <td>3458.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  TripType  VisitNumber  Weekday           Upc  ScanCount  \\\n",
       "0      572389        38       170528        4  4.900005e+09          2   \n",
       "1      185055        37        55398        6  2.015450e+10          1   \n",
       "2      474946        37       141533        6  4.300020e+09          2   \n",
       "3      412325        36       122269        3  2.279692e+09          1   \n",
       "4      249953        36        73972        2  7.958200e+09          1   \n",
       "\n",
       "  DepartmentDescription  FinelineNumber  \n",
       "0           DSD GROCERY          9570.0  \n",
       "1          SERVICE DELI          8001.0  \n",
       "2     GROCERY DRY GOODS          4306.0  \n",
       "3         PERSONAL CARE          5070.0  \n",
       "4           DSD GROCERY          3458.0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import statements\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re \n",
    "\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier, RadiusNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score,classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB, BernoulliNB\n",
    "from sklearn import tree\n",
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.lda import LDA\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#importing the data into the notebook\n",
    "train_data = pd.read_csv(\"/resources/data/kdd/project/data/fiveclasses_train.csv\")\n",
    "train_data.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15182, 71)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [\"Weekday\", \"NumItems\", \"Return\", 'ACCESSORIES',\n",
    "       'AUTOMOTIVE', 'BAKERY', 'BATH AND SHOWER', 'BEAUTY', 'BEDDING',\n",
    "       'BOOKS AND MAGAZINES', 'BOYS WEAR', 'BRAS & SHAPEWEAR',\n",
    "       'CAMERAS AND SUPPLIES', 'CANDY, TOBACCO, COOKIES', 'CELEBRATION',\n",
    "       'COMM BREAD', 'CONCEPT STORES', 'COOK AND DINE', 'DAIRY', 'DSD GROCERY',\n",
    "       'ELECTRONICS', 'FABRICS AND CRAFTS', 'FINANCIAL SERVICES',\n",
    "       'FROZEN FOODS', 'FURNITURE', 'GIRLS WEAR, 4-6X  AND 7-14',\n",
    "       'GROCERY DRY GOODS', 'HARDWARE', 'HOME DECOR',\n",
    "       'HOME MANAGEMENT', 'HORTICULTURE AND ACCESS',\n",
    "       'HOUSEHOLD CHEMICALS/SUPP', 'HOUSEHOLD PAPER GOODS',\n",
    "       'IMPULSE MERCHANDISE', 'INFANT APPAREL', 'INFANT CONSUMABLE HARDLINES',\n",
    "       'JEWELRY AND SUNGLASSES', 'LADIES SOCKS', 'LADIESWEAR',\n",
    "       'LARGE HOUSEHOLD GOODS', 'LAWN AND GARDEN', 'LIQUOR,WINE,BEER',\n",
    "       'MEAT - FRESH & FROZEN', 'MEDIA AND GAMING',  'MENSWEAR',\n",
    "       'OFFICE SUPPLIES', 'OPTICAL - FRAMES', 'OPTICAL - LENSES',\n",
    "       'OTHER DEPARTMENTS', 'PAINT AND ACCESSORIES', 'PERSONAL CARE',\n",
    "       'PETS AND SUPPLIES', 'PHARMACY OTC', 'PHARMACY RX',\n",
    "       'PLAYERS AND ELECTRONICS', 'PLUS AND MATERNITY', 'PRE PACKED DELI',\n",
    "       'PRODUCE', 'SEAFOOD', 'SEASONAL', 'SERVICE DELI', 'SHEER HOSIERY',\n",
    "       'SHOES', 'SLEEPWEAR/FOUNDATIONS', 'SPORTING GOODS',\n",
    "       'SWIMWEAR/OUTERWEAR', 'TOYS', 'WIRELESS']\n",
    "#Feature Building\n",
    "def transform_my_data(data):\n",
    "    \n",
    "    dummies = pd.get_dummies(data.DepartmentDescription)\n",
    "    data[dummies.columns] = dummies \n",
    "    data_dummies = data.iloc[:,7:]\n",
    "    data_dummies = data_dummies.apply(lambda x: x*data[\"ScanCount\"])\n",
    "    data_dummies = data_dummies.replace(-0,0)\n",
    "\n",
    "    data.loc[data.ScanCount < 0, 'Return'] = 1\n",
    "    data.loc[data.Return != 1, 'Return'] = 0\n",
    "    data = data[[\"TripType\", \"VisitNumber\", \"Weekday\", \"ScanCount\", \"Return\"]]\n",
    "    data = data.rename(columns={\"ScanCount\":\"NumItems\"})\n",
    "    data = pd.concat([data, data_dummies], axis=1)\n",
    "    grouped = data.groupby(\"VisitNumber\")\n",
    "    grouped = grouped.agg({'Weekday': np.max, \"TripType\": np.max, 'NumItems': np.sum, 'Return': np.max, \n",
    "              '1-HR PHOTO': np.sum, 'ACCESSORIES': np.sum,\n",
    "       'AUTOMOTIVE': np.sum, 'BAKERY': np.sum, 'BATH AND SHOWER': np.sum, 'BEAUTY': np.sum, 'BEDDING': np.sum,\n",
    "       'BOOKS AND MAGAZINES': np.sum, 'BOYS WEAR': np.sum, 'BRAS & SHAPEWEAR': np.sum,\n",
    "       'CAMERAS AND SUPPLIES': np.sum, 'CANDY, TOBACCO, COOKIES': np.sum, 'CELEBRATION': np.sum,\n",
    "       'COMM BREAD': np.sum, 'CONCEPT STORES': np.sum, 'COOK AND DINE': np.sum, 'DAIRY': np.sum, 'DSD GROCERY': np.sum,\n",
    "       'ELECTRONICS': np.sum, 'FABRICS AND CRAFTS': np.sum, 'FINANCIAL SERVICES': np.sum,\n",
    "       'FROZEN FOODS': np.sum, 'FURNITURE': np.sum, 'GIRLS WEAR, 4-6X  AND 7-14': np.sum,\n",
    "       'GROCERY DRY GOODS': np.sum, 'HARDWARE': np.sum, 'HEALTH AND BEAUTY AIDS': np.sum, 'HOME DECOR': np.sum,\n",
    "       'HOME MANAGEMENT': np.sum, 'HORTICULTURE AND ACCESS': np.sum,\n",
    "       'HOUSEHOLD CHEMICALS/SUPP': np.sum, 'HOUSEHOLD PAPER GOODS': np.sum,\n",
    "       'IMPULSE MERCHANDISE': np.sum, 'INFANT APPAREL': np.sum, 'INFANT CONSUMABLE HARDLINES': np.sum,\n",
    "       'JEWELRY AND SUNGLASSES': np.sum, 'LADIES SOCKS': np.sum, 'LADIESWEAR': np.sum,\n",
    "       'LARGE HOUSEHOLD GOODS': np.sum, 'LAWN AND GARDEN': np.sum, 'LIQUOR,WINE,BEER': np.sum,\n",
    "       'MEAT - FRESH & FROZEN': np.sum, 'MEDIA AND GAMING': np.sum,'MENSWEAR': np.sum,\n",
    "       'OFFICE SUPPLIES': np.sum, 'OPTICAL - FRAMES': np.sum, 'OPTICAL - LENSES': np.sum,\n",
    "       'OTHER DEPARTMENTS': np.sum, 'PAINT AND ACCESSORIES': np.sum, 'PERSONAL CARE': np.sum,\n",
    "       'PETS AND SUPPLIES': np.sum, 'PHARMACY OTC': np.sum, 'PHARMACY RX': np.sum,\n",
    "       'PLAYERS AND ELECTRONICS': np.sum, 'PLUS AND MATERNITY': np.sum, 'PRE PACKED DELI': np.sum,\n",
    "       'PRODUCE': np.sum, 'SEAFOOD': np.sum, 'SEASONAL': np.sum, 'SERVICE DELI': np.sum, 'SHEER HOSIERY': np.sum,\n",
    "       'SHOES': np.sum, 'SLEEPWEAR/FOUNDATIONS': np.sum, 'SPORTING GOODS': np.sum,\n",
    "       'SWIMWEAR/OUTERWEAR': np.sum, 'TOYS': np.sum, 'WIRELESS': np.sum})\n",
    "    data = grouped[[\"TripType\", \"Weekday\", \"NumItems\", \"Return\",'1-HR PHOTO', 'ACCESSORIES',\n",
    "           'AUTOMOTIVE', 'BAKERY', 'BATH AND SHOWER', 'BEAUTY', 'BEDDING',\n",
    "           'BOOKS AND MAGAZINES', 'BOYS WEAR', 'BRAS & SHAPEWEAR',\n",
    "           'CAMERAS AND SUPPLIES', 'CANDY, TOBACCO, COOKIES', 'CELEBRATION',\n",
    "           'COMM BREAD', 'CONCEPT STORES', 'COOK AND DINE', 'DAIRY', 'DSD GROCERY',\n",
    "           'ELECTRONICS', 'FABRICS AND CRAFTS', 'FINANCIAL SERVICES',\n",
    "           'FROZEN FOODS', 'FURNITURE', 'GIRLS WEAR, 4-6X  AND 7-14',\n",
    "           'GROCERY DRY GOODS', 'HARDWARE', 'HEALTH AND BEAUTY AIDS', 'HOME DECOR',\n",
    "           'HOME MANAGEMENT', 'HORTICULTURE AND ACCESS',\n",
    "           'HOUSEHOLD CHEMICALS/SUPP', 'HOUSEHOLD PAPER GOODS',\n",
    "           'IMPULSE MERCHANDISE', 'INFANT APPAREL', 'INFANT CONSUMABLE HARDLINES',\n",
    "           'JEWELRY AND SUNGLASSES', 'LADIES SOCKS', 'LADIESWEAR',\n",
    "           'LARGE HOUSEHOLD GOODS', 'LAWN AND GARDEN', 'LIQUOR,WINE,BEER',\n",
    "           'MEAT - FRESH & FROZEN', 'MEDIA AND GAMING', 'MENSWEAR',\n",
    "           'OFFICE SUPPLIES', 'OPTICAL - FRAMES', 'OPTICAL - LENSES',\n",
    "           'OTHER DEPARTMENTS', 'PAINT AND ACCESSORIES', 'PERSONAL CARE',\n",
    "           'PETS AND SUPPLIES', 'PHARMACY OTC', 'PHARMACY RX',\n",
    "           'PLAYERS AND ELECTRONICS', 'PLUS AND MATERNITY', 'PRE PACKED DELI',\n",
    "           'PRODUCE', 'SEAFOOD', 'SEASONAL', 'SERVICE DELI', 'SHEER HOSIERY',\n",
    "           'SHOES', 'SLEEPWEAR/FOUNDATIONS', 'SPORTING GOODS',\n",
    "           'SWIMWEAR/OUTERWEAR', 'TOYS', 'WIRELESS']]\n",
    "    \n",
    "    \n",
    "    #data.head()\n",
    "    return data\n",
    "\n",
    "train_data=transform_my_data(train_data)\n",
    "train_data.shape\n",
    "#train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Test and Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10627, 71)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data preprocesssing dropping na and splitting data into test and train\n",
    "train, test = train_test_split(train_data, test_size = 0.3)\n",
    "test=test.dropna()\n",
    "train=train.dropna()\n",
    "train_data = train\n",
    "train_data.describe()\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Data preprocesssing\n",
    "#Transforming qualitative variables into quantitative\n",
    "def transform(day,no):\n",
    "    global train_data\n",
    "    train_data=train_data.replace(day,no)\n",
    "    return;\n",
    "transform(\"Monday\", 1)\n",
    "transform(\"Tuesday\", 2)\n",
    "transform(\"Wednesday\", 3)\n",
    "transform(\"Thursday\", 4)\n",
    "transform(\"Friday\", 5)\n",
    "transform(\"Saturday\", 6)\n",
    "transform(\"Sunday\", 7)\n",
    "\n",
    "#Removing redundency in department description column\n",
    "train_data=train_data.replace(\"MENS WEAR\",\"MENSWEAR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f36695114e0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f366950e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#ploting the histogram for the trip type \n",
    "#data_tripty = train_data.drop_duplicates(\"VisitNumber\")\n",
    "plt.figure(figsize=(12,5))\n",
    "train_data['TripType'].value_counts().plot('bar',title=\"Trip Type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f36695f7278>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sJG85W/svyq3d+0T/56b/89H7een/vLZ2/9kzyy0AAOAeEJIBAGAgJAMAwEBIBgCAgZAM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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f366950e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting histogram for daywise visits\n",
    "#data_tripwd = train_data.drop_duplicates(\"VisitNumber\")\n",
    "plt.figure(figsize=(12,5))\n",
    "train_data['Weekday'].value_counts().plot('bar',title=\"Daywise visits in a week \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TripType                       5\n",
       "Weekday                        7\n",
       "NumItems                      67\n",
       "Return                         2\n",
       "1-HR PHOTO                     5\n",
       "ACCESSORIES                    4\n",
       "AUTOMOTIVE                     7\n",
       "BAKERY                         7\n",
       "BATH AND SHOWER                8\n",
       "BEAUTY                        20\n",
       "BEDDING                        4\n",
       "BOOKS AND MAGAZINES            4\n",
       "BOYS WEAR                      4\n",
       "BRAS & SHAPEWEAR               4\n",
       "CAMERAS AND SUPPLIES           2\n",
       "CANDY, TOBACCO, COOKIES        8\n",
       "CELEBRATION                   11\n",
       "COMM BREAD                    12\n",
       "CONCEPT STORES                 2\n",
       "COOK AND DINE                 10\n",
       "DAIRY                         20\n",
       "DSD GROCERY                   18\n",
       "ELECTRONICS                    6\n",
       "FABRICS AND CRAFTS             7\n",
       "FINANCIAL SERVICES            14\n",
       "FROZEN FOODS                  14\n",
       "FURNITURE                      3\n",
       "GIRLS WEAR, 4-6X  AND 7-14     5\n",
       "GROCERY DRY GOODS             33\n",
       "HARDWARE                       4\n",
       "                              ..\n",
       "LADIESWEAR                     6\n",
       "LARGE HOUSEHOLD GOODS          2\n",
       "LAWN AND GARDEN                7\n",
       "LIQUOR,WINE,BEER               6\n",
       "MEAT - FRESH & FROZEN         14\n",
       "MEDIA AND GAMING               4\n",
       "MENSWEAR                       9\n",
       "OFFICE SUPPLIES                8\n",
       "OPTICAL - FRAMES               6\n",
       "OPTICAL - LENSES               3\n",
       "OTHER DEPARTMENTS              2\n",
       "PAINT AND ACCESSORIES          4\n",
       "PERSONAL CARE                 27\n",
       "PETS AND SUPPLIES             12\n",
       "PHARMACY OTC                  20\n",
       "PHARMACY RX                    6\n",
       "PLAYERS AND ELECTRONICS        2\n",
       "PLUS AND MATERNITY             3\n",
       "PRE PACKED DELI               10\n",
       "PRODUCE                       25\n",
       "SEAFOOD                        6\n",
       "SEASONAL                       2\n",
       "SERVICE DELI                   8\n",
       "SHEER HOSIERY                  4\n",
       "SHOES                          6\n",
       "SLEEPWEAR/FOUNDATIONS          5\n",
       "SPORTING GOODS                 7\n",
       "SWIMWEAR/OUTERWEAR             3\n",
       "TOYS                           6\n",
       "WIRELESS                       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.T.apply(lambda x: x.nunique(),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weekday  TripType\n",
       "1        3           260\n",
       "         5           269\n",
       "         36          250\n",
       "         37          268\n",
       "         38          287\n",
       "2        3           306\n",
       "         5           283\n",
       "         36          248\n",
       "         37          226\n",
       "         38          266\n",
       "3        3           312\n",
       "         5           299\n",
       "         36          251\n",
       "         37          219\n",
       "         38          268\n",
       "4        3           254\n",
       "         5           313\n",
       "         36          258\n",
       "         37          220\n",
       "         38          241\n",
       "5        3           527\n",
       "         5           382\n",
       "         36          351\n",
       "         37          290\n",
       "         38          313\n",
       "6        3           400\n",
       "         5           351\n",
       "         36          381\n",
       "         37          321\n",
       "         38          288\n",
       "7        3           293\n",
       "         5           303\n",
       "         36          352\n",
       "         37          400\n",
       "         38          377\n",
       "Name: TripType, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train_data_dup = train_data.drop_duplicates(\"VisitNumber\")\n",
    "def get_count(values):\n",
    "    return len(values)\n",
    "grouped_count = train_data.groupby(['Weekday',\"TripType\"]).TripType.agg(get_count)\n",
    "grouped_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#KNN classifier\n",
    "\n",
    "knc = KNeighborsClassifier(n_neighbors=5)\n",
    "knc = knc.fit(np.asarray(train_data[features]), \n",
    "                    np.asarray(train_data.TripType))\n",
    "\n",
    "predictions = knc.predict(np.asarray(test[features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86937431394072451"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy of KNN\n",
    "\n",
    "accuracy_score(test.TripType,predictions )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          3       0.96      0.98      0.97      1027\n",
      "          5       0.83      0.86      0.85       934\n",
      "         36       0.87      0.83      0.85       894\n",
      "         37       0.86      0.84      0.85       838\n",
      "         38       0.81      0.82      0.81       862\n",
      "\n",
      "avg / total       0.87      0.87      0.87      4555\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pd.crosstab(test.TripType,predictions , rownames=['True'], colnames=['Predicted'], margins=True)\n",
    "print(classification_report(test[\"TripType\"], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Logistic Regression\n",
    "\n",
    "train_data.TripType = train_data.TripType.astype(float)\n",
    "train_data.Weekday = train_data.Weekday.astype(float)  \n",
    "                         \n",
    "test.TripType = test.TripType.astype(float)\n",
    "test.Weekday = test.Weekday.astype(float)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(X = np.asarray(train_data[features]), y = np.asarray(train_data.TripType))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = lr.predict(np.asarray(test[features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>3.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>36.0</th>\n",
       "      <th>37.0</th>\n",
       "      <th>38.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>1011</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>15</td>\n",
       "      <td>824</td>\n",
       "      <td>56</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36.0</th>\n",
       "      <td>11</td>\n",
       "      <td>71</td>\n",
       "      <td>783</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.0</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>730</td>\n",
       "      <td>84</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38.0</th>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>66</td>\n",
       "      <td>741</td>\n",
       "      <td>862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1045</td>\n",
       "      <td>943</td>\n",
       "      <td>876</td>\n",
       "      <td>816</td>\n",
       "      <td>875</td>\n",
       "      <td>4555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted   3.0  5.0  36.0  37.0  38.0   All\n",
       "True                                        \n",
       "3.0        1011   13     1     1     1  1027\n",
       "5.0          15  824    56    11    28   934\n",
       "36.0         11   71   783     8    21   894\n",
       "37.0          2    8    14   730    84   838\n",
       "38.0          6   27    22    66   741   862\n",
       "All        1045  943   876   816   875  4555"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(test.TripType,predictions , rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89769484083424811"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy of logistic regression\n",
    "accuracy_score(test.TripType,predictions )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        3.0       0.97      0.98      0.98      1027\n",
      "        5.0       0.87      0.88      0.88       934\n",
      "       36.0       0.89      0.88      0.88       894\n",
      "       37.0       0.89      0.87      0.88       838\n",
      "       38.0       0.85      0.86      0.85       862\n",
      "\n",
      "avg / total       0.90      0.90      0.90      4555\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pd.crosstab(test.TripType,predictions , rownames=['True'], colnames=['Predicted'], margins=True)\n",
    "print(classification_report(test[\"TripType\"], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Naive Bayes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "\n",
    "gnb = gnb.fit(np.asarray(train_data[features]), \n",
    "                    np.asarray(train_data.TripType))\n",
    "prediction = gnb.predict(np.asarray(test[features]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.59187705817782654"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy of naive Bayes\n",
    "accuracy_score(test.TripType,prediction )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>3.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>36.0</th>\n",
       "      <th>37.0</th>\n",
       "      <th>38.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>973</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>1027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>305</td>\n",
       "      <td>374</td>\n",
       "      <td>73</td>\n",
       "      <td>14</td>\n",
       "      <td>168</td>\n",
       "      <td>934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36.0</th>\n",
       "      <td>44</td>\n",
       "      <td>279</td>\n",
       "      <td>291</td>\n",
       "      <td>16</td>\n",
       "      <td>264</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>284</td>\n",
       "      <td>532</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38.0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>50</td>\n",
       "      <td>774</td>\n",
       "      <td>862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1326</td>\n",
       "      <td>661</td>\n",
       "      <td>428</td>\n",
       "      <td>368</td>\n",
       "      <td>1772</td>\n",
       "      <td>4555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted   3.0  5.0  36.0  37.0  38.0   All\n",
       "True                                        \n",
       "3.0         973    1    15     4    34  1027\n",
       "5.0         305  374    73    14   168   934\n",
       "36.0         44  279   291    16   264   894\n",
       "37.0          1    3    18   284   532   838\n",
       "38.0          3    4    31    50   774   862\n",
       "All        1326  661   428   368  1772  4555"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(test.TripType,prediction , rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.59187705817782654"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(test.TripType,prediction )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Decision Tree\n",
    "\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(np.asarray(train_data[features]), \n",
    "                    np.asarray(train_data.TripType))\n",
    "predictions = clf.predict(np.asarray(test[features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85971459934138306"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy of Decision Tree\n",
    "accuracy_score(test.TripType,predictions )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        3.0       0.98      0.98      0.98      1027\n",
      "        5.0       0.83      0.85      0.84       934\n",
      "       36.0       0.85      0.82      0.84       894\n",
      "       37.0       0.83      0.83      0.83       838\n",
      "       38.0       0.78      0.80      0.79       862\n",
      "\n",
      "avg / total       0.86      0.86      0.86      4555\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pd.crosstab(test.TripType,predictions , rownames=['True'], colnames=['Predicted'], margins=True)\n",
    "print(classification_report(test[\"TripType\"], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#SVM\n",
    "\n",
    "clf = svm.SVC(decision_function_shape='ovr')\n",
    "clf = clf.fit(np.asarray(train_data[features]), \n",
    "                    np.asarray(train_data.TripType))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = clf.predict(np.asarray(test[features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89352360043907797"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy of SVM\n",
    "accuracy_score(test.TripType,predictions )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>3.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>36.0</th>\n",
       "      <th>37.0</th>\n",
       "      <th>38.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>1006</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>10</td>\n",
       "      <td>825</td>\n",
       "      <td>48</td>\n",
       "      <td>14</td>\n",
       "      <td>37</td>\n",
       "      <td>934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36.0</th>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>767</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.0</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>731</td>\n",
       "      <td>82</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38.0</th>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>66</td>\n",
       "      <td>741</td>\n",
       "      <td>862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1022</td>\n",
       "      <td>965</td>\n",
       "      <td>852</td>\n",
       "      <td>829</td>\n",
       "      <td>887</td>\n",
       "      <td>4555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted   3.0  5.0  36.0  37.0  38.0   All\n",
       "True                                        \n",
       "3.0        1006   16     3     1     1  1027\n",
       "5.0          10  825    48    14    37   934\n",
       "36.0          4   80   767    17    26   894\n",
       "37.0          0   13    12   731    82   838\n",
       "38.0          2   31    22    66   741   862\n",
       "All        1022  965   852   829   887  4555"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(test.TripType,predictions , rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Random Forest\n",
    "\n",
    "walmart_rf = RandomForestClassifier(n_estimators=100)\n",
    "walmart_rf = walmart_rf.fit(np.asarray(train_data[features]), np.asarray(train_data.TripType))\n",
    "predictions = walmart_rf.predict(np.asarray(test[features]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89330406147091113"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy of Random Forest\n",
    "accuracy_score(test.TripType,predictions )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        3.0       0.98      0.98      0.98      1027\n",
      "        5.0       0.87      0.87      0.87       934\n",
      "       36.0       0.88      0.87      0.87       894\n",
      "       37.0       0.87      0.90      0.88       838\n",
      "       38.0       0.86      0.84      0.85       862\n",
      "\n",
      "avg / total       0.89      0.89      0.89      4555\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pd.crosstab(test.TripType,predictions , rownames=['True'], colnames=['Predicted'], margins=True)\n",
    "print(classification_report(test[\"TripType\"], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#LDA\n",
    "\n",
    "clf = LDA()\n",
    "clf = clf.fit(np.asarray(train_data[features]), np.asarray(train_data.TripType))\n",
    "predictions = clf.predict(np.asarray(test[features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86717892425905596"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy of LDA\n",
    "accuracy_score(test.TripType,predictions )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        3.0       0.96      0.92      0.94      1027\n",
      "        5.0       0.77      0.91      0.84       934\n",
      "       36.0       0.87      0.84      0.86       894\n",
      "       37.0       0.95      0.77      0.85       838\n",
      "       38.0       0.81      0.88      0.85       862\n",
      "\n",
      "avg / total       0.88      0.87      0.87      4555\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pd.crosstab(test.TripType,predictions , rownames=['True'], colnames=['Predicted'], margins=True)\n",
    "print(classification_report(test[\"TripType\"], predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TripType</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>NumItems</th>\n",
       "      <th>Return</th>\n",
       "      <th>1-HR PHOTO</th>\n",
       "      <th>ACCESSORIES</th>\n",
       "      <th>AUTOMOTIVE</th>\n",
       "      <th>BAKERY</th>\n",
       "      <th>BATH AND SHOWER</th>\n",
       "      <th>BEAUTY</th>\n",
       "      <th>...</th>\n",
       "      <th>SEAFOOD</th>\n",
       "      <th>SEASONAL</th>\n",
       "      <th>SERVICE DELI</th>\n",
       "      <th>SHEER HOSIERY</th>\n",
       "      <th>SHOES</th>\n",
       "      <th>SLEEPWEAR/FOUNDATIONS</th>\n",
       "      <th>SPORTING GOODS</th>\n",
       "      <th>SWIMWEAR/OUTERWEAR</th>\n",
       "      <th>TOYS</th>\n",
       "      <th>WIRELESS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VisitNumber</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TripType  Weekday  NumItems  Return  1-HR PHOTO  ACCESSORIES  \\\n",
       "VisitNumber                                                                 \n",
       "153940           36.0      1.0         2     0.0         0.0          0.0   \n",
       "77578            38.0      3.0         4     0.0         0.0          0.0   \n",
       "138370           36.0      6.0         3     0.0         0.0          0.0   \n",
       "35057             3.0      3.0         1     0.0         0.0          0.0   \n",
       "132089           36.0      5.0         3     0.0         0.0          0.0   \n",
       "\n",
       "             AUTOMOTIVE  BAKERY  BATH AND SHOWER  BEAUTY    ...     SEAFOOD  \\\n",
       "VisitNumber                                                 ...               \n",
       "153940              0.0     0.0              0.0     2.0    ...         0.0   \n",
       "77578               0.0     0.0              0.0     0.0    ...         0.0   \n",
       "138370              0.0     0.0              0.0     1.0    ...         0.0   \n",
       "35057               0.0     0.0              0.0     0.0    ...         0.0   \n",
       "132089              0.0     0.0              0.0     1.0    ...         0.0   \n",
       "\n",
       "             SEASONAL  SERVICE DELI  SHEER HOSIERY  SHOES  \\\n",
       "VisitNumber                                                 \n",
       "153940            0.0           0.0            0.0    0.0   \n",
       "77578             0.0           0.0            0.0    0.0   \n",
       "138370            0.0           0.0            0.0    0.0   \n",
       "35057             0.0           0.0            0.0    0.0   \n",
       "132089            0.0           0.0            0.0    0.0   \n",
       "\n",
       "             SLEEPWEAR/FOUNDATIONS  SPORTING GOODS  SWIMWEAR/OUTERWEAR  TOYS  \\\n",
       "VisitNumber                                                                    \n",
       "153940                         0.0             0.0                 0.0   0.0   \n",
       "77578                          0.0             0.0                 0.0   0.0   \n",
       "138370                         0.0             0.0                 0.0   2.0   \n",
       "35057                          0.0             0.0                 0.0   0.0   \n",
       "132089                         0.0             0.0                 0.0   0.0   \n",
       "\n",
       "             WIRELESS  \n",
       "VisitNumber            \n",
       "153940            0.0  \n",
       "77578             0.0  \n",
       "138370            0.0  \n",
       "35057             0.0  \n",
       "132089            0.0  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.to_csv(\"featureData.csv\", sep=',')"
   ]
  }
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