Feature Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler, QuantileTransformer\n",
    "from sklearn.feature_selection import SelectKBest, f_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"D:\\\\Analyst\\\\EDA\\\\NYC Bike share\\\\NYC-BikeShare-2015-2017-combined.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(735502, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>End Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>24470</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>739</td>\n",
       "      <td>2015-10-01 00:27:12</td>\n",
       "      <td>2015-10-01 00:39:32</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>24481</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2714</td>\n",
       "      <td>2015-10-01 00:32:46</td>\n",
       "      <td>2015-10-01 01:18:01</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>24628</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>-74.038051</td>\n",
       "      <td>24613</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>-74.055701</td>\n",
       "      <td>24668</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Trip Duration           Start Time            Stop Time  \\\n",
       "0           0            376  2015-10-01 00:16:26  2015-10-01 00:22:42   \n",
       "1           1            739  2015-10-01 00:27:12  2015-10-01 00:39:32   \n",
       "2           2           2714  2015-10-01 00:32:46  2015-10-01 01:18:01   \n",
       "3           3            275  2015-10-01 00:34:31  2015-10-01 00:39:06   \n",
       "4           4            561  2015-10-01 00:40:12  2015-10-01 00:49:33   \n",
       "\n",
       "   Start Station ID Start Station Name  Start Station Latitude  \\\n",
       "0              3212    Christ Hospital               40.734786   \n",
       "1              3207        Oakland Ave               40.737604   \n",
       "2              3193       Lincoln Park               40.724605   \n",
       "3              3199       Newport Pkwy               40.728745   \n",
       "4              3183     Exchange Place               40.716247   \n",
       "\n",
       "   Start Station Longitude  End Station ID    End Station Name  \\\n",
       "0               -74.050444            3207         Oakland Ave   \n",
       "1               -74.052478            3212     Christ Hospital   \n",
       "2               -74.078406            3193        Lincoln Park   \n",
       "3               -74.032108            3187           Warren St   \n",
       "4               -74.033459            3192  Liberty Light Rail   \n",
       "\n",
       "   End Station Latitude  End Station Longitude  Bike ID   User Type  \\\n",
       "0             40.737604             -74.052478    24470  Subscriber   \n",
       "1             40.734786             -74.050444    24481  Subscriber   \n",
       "2             40.724605             -74.078406    24628  Subscriber   \n",
       "3             40.721124             -74.038051    24613  Subscriber   \n",
       "4             40.711242             -74.055701    24668    Customer   \n",
       "\n",
       "   Birth Year  Gender  Trip_Duration_in_min  \n",
       "0      1960.0       1                     6  \n",
       "1      1960.0       1                    12  \n",
       "2      1983.0       1                    45  \n",
       "3      1975.0       1                     5  \n",
       "4      1984.0       0                     9  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 735502 entries, 0 to 735501\n",
      "Data columns (total 17 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   Unnamed: 0               735502 non-null  int64  \n",
      " 1   Trip Duration            735502 non-null  int64  \n",
      " 2   Start Time               735502 non-null  object \n",
      " 3   Stop Time                735502 non-null  object \n",
      " 4   Start Station ID         735502 non-null  int64  \n",
      " 5   Start Station Name       735502 non-null  object \n",
      " 6   Start Station Latitude   735502 non-null  float64\n",
      " 7   Start Station Longitude  735502 non-null  float64\n",
      " 8   End Station ID           735502 non-null  int64  \n",
      " 9   End Station Name         735502 non-null  object \n",
      " 10  End Station Latitude     735502 non-null  float64\n",
      " 11  End Station Longitude    735502 non-null  float64\n",
      " 12  Bike ID                  735502 non-null  int64  \n",
      " 13  User Type                735502 non-null  object \n",
      " 14  Birth Year               735502 non-null  float64\n",
      " 15  Gender                   735502 non-null  int64  \n",
      " 16  Trip_Duration_in_min     735502 non-null  int64  \n",
      "dtypes: float64(5), int64(7), object(5)\n",
      "memory usage: 95.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate Rows :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(395882, 17)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate = df[df.duplicated(subset=None,keep='first')] \n",
    "  \n",
    "print(\"Duplicate Rows :\") \n",
    "  \n",
    "# Print the resultant Dataframe \n",
    "duplicate.shape \n",
    "#duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = duplicate.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(332952, 17)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                 0\n",
       "Trip Duration              0\n",
       "Start Time                 0\n",
       "Stop Time                  0\n",
       "Start Station ID           0\n",
       "Start Station Name         0\n",
       "Start Station Latitude     0\n",
       "Start Station Longitude    0\n",
       "End Station ID             0\n",
       "End Station Name           0\n",
       "End Station Latitude       0\n",
       "End Station Longitude      0\n",
       "Bike ID                    0\n",
       "User Type                  0\n",
       "Birth Year                 0\n",
       "Gender                     0\n",
       "Trip_Duration_in_min       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_11832\\1376793381.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.drop(['Unnamed: 0'], axis=1, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df.drop(['Unnamed: 0'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "332952\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_11832\\3781205836.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  adf.loc[:,('Birth Year')] = adf['Birth Year'].astype(int)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19264</th>\n",
       "      <td>6</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>24470</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19265</th>\n",
       "      <td>12</td>\n",
       "      <td>2015-10-01 00:27:12</td>\n",
       "      <td>2015-10-01 00:39:32</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>24481</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19266</th>\n",
       "      <td>45</td>\n",
       "      <td>2015-10-01 00:32:46</td>\n",
       "      <td>2015-10-01 01:18:01</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>24628</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19267</th>\n",
       "      <td>5</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>24613</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19268</th>\n",
       "      <td>9</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>24668</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip_Duration_in_min           Start Time            Stop Time  \\\n",
       "19264                     6  2015-10-01 00:16:26  2015-10-01 00:22:42   \n",
       "19265                    12  2015-10-01 00:27:12  2015-10-01 00:39:32   \n",
       "19266                    45  2015-10-01 00:32:46  2015-10-01 01:18:01   \n",
       "19267                     5  2015-10-01 00:34:31  2015-10-01 00:39:06   \n",
       "19268                     9  2015-10-01 00:40:12  2015-10-01 00:49:33   \n",
       "\n",
       "      Start Station Name    End Station Name  Bike ID   User Type  Birth Year  \\\n",
       "19264    Christ Hospital         Oakland Ave    24470  Subscriber        1960   \n",
       "19265        Oakland Ave     Christ Hospital    24481  Subscriber        1960   \n",
       "19266       Lincoln Park        Lincoln Park    24628  Subscriber        1983   \n",
       "19267       Newport Pkwy           Warren St    24613  Subscriber        1975   \n",
       "19268     Exchange Place  Liberty Light Rail    24668    Customer        1984   \n",
       "\n",
       "       Gender  \n",
       "19264       1  \n",
       "19265       1  \n",
       "19266       1  \n",
       "19267       1  \n",
       "19268       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks for length of dataframe - df\n",
    "print(len(df))\n",
    "adf = df[['Trip_Duration_in_min', 'Start Time', 'Stop Time',\n",
    "       'Start Station Name', 'End Station Name',\n",
    "      'Bike ID', 'User Type','Birth Year', 'Gender']]\n",
    "adf.loc[:,('Birth Year')] = adf['Birth Year'].astype(int)\n",
    "adf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>332952.000000</td>\n",
       "      <td>332952.000000</td>\n",
       "      <td>332952.000000</td>\n",
       "      <td>332952.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.929870</td>\n",
       "      <td>24919.277091</td>\n",
       "      <td>1979.558942</td>\n",
       "      <td>1.125285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>573.591031</td>\n",
       "      <td>748.555620</td>\n",
       "      <td>9.376521</td>\n",
       "      <td>0.516336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>14552.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>24486.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>24604.000000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>24713.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>272163.000000</td>\n",
       "      <td>29296.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip_Duration_in_min        Bike ID     Birth Year         Gender\n",
       "count         332952.000000  332952.000000  332952.000000  332952.000000\n",
       "mean              14.929870   24919.277091    1979.558942       1.125285\n",
       "std              573.591031     748.555620       9.376521       0.516336\n",
       "min                1.000000   14552.000000    1900.000000       0.000000\n",
       "25%                4.000000   24486.000000    1975.000000       1.000000\n",
       "50%                6.000000   24604.000000    1982.000000       1.000000\n",
       "75%               11.000000   24713.000000    1986.000000       1.000000\n",
       "max           272163.000000   29296.000000    2000.000000       2.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Gender'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(3,3), dpi=200)\n",
    "ax = plt.subplot(111)\n",
    "df['Gender'].value_counts().plot(kind='pie', ax=ax, autopct='%1.1f%%', startangle=270, fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0 = Unknown\n",
    "### 1 = Male\n",
    "### 2 = Female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_11832\\3891836313.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  adf['Start Date'] = pd.to_datetime(adf['Start Time'])\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_11832\\3891836313.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  adf['Stop Date'] = pd.to_datetime(adf['Stop Time'])\n"
     ]
    }
   ],
   "source": [
    "adf['Start Date'] = pd.to_datetime(adf['Start Time'])\n",
    "adf['Stop Date'] = pd.to_datetime(adf['Stop Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_11832\\2810143333.py:6: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead.\n",
      "  adf['Start Week'] = adf['Start Date'].dt.week\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_11832\\2810143333.py:7: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead.\n",
      "  adf['Start Weekofyear'] = adf['Start Date'].dt.weekofyear\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_11832\\2810143333.py:17: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead.\n",
      "  adf['Stop Week'] = adf['Stop Date'].dt.week\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_11832\\2810143333.py:18: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead.\n",
      "  adf['Stop Weekofyear'] = adf['Stop Date'].dt.weekofyear\n"
     ]
    },
    {
     "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>Trip_Duration_in_min</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>...</th>\n",
       "      <th>Stop Month</th>\n",
       "      <th>Stop Day</th>\n",
       "      <th>Stop Minute</th>\n",
       "      <th>Stop Week</th>\n",
       "      <th>Stop Weekday</th>\n",
       "      <th>Stop Weekofyear</th>\n",
       "      <th>Stop weekday</th>\n",
       "      <th>Stop dayofyear</th>\n",
       "      <th>Stop weekday_name</th>\n",
       "      <th>Stop quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19264</th>\n",
       "      <td>6</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>24470</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19265</th>\n",
       "      <td>12</td>\n",
       "      <td>2015-10-01 00:27:12</td>\n",
       "      <td>2015-10-01 00:39:32</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>24481</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-01 00:27:12</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19266</th>\n",
       "      <td>45</td>\n",
       "      <td>2015-10-01 00:32:46</td>\n",
       "      <td>2015-10-01 01:18:01</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>24628</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-01 00:32:46</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19267</th>\n",
       "      <td>5</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>24613</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19268</th>\n",
       "      <td>9</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>24668</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>40</td>\n",
       "      <td>49</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip_Duration_in_min           Start Time            Stop Time  \\\n",
       "19264                     6  2015-10-01 00:16:26  2015-10-01 00:22:42   \n",
       "19265                    12  2015-10-01 00:27:12  2015-10-01 00:39:32   \n",
       "19266                    45  2015-10-01 00:32:46  2015-10-01 01:18:01   \n",
       "19267                     5  2015-10-01 00:34:31  2015-10-01 00:39:06   \n",
       "19268                     9  2015-10-01 00:40:12  2015-10-01 00:49:33   \n",
       "\n",
       "      Start Station Name    End Station Name  Bike ID   User Type  Birth Year  \\\n",
       "19264    Christ Hospital         Oakland Ave    24470  Subscriber        1960   \n",
       "19265        Oakland Ave     Christ Hospital    24481  Subscriber        1960   \n",
       "19266       Lincoln Park        Lincoln Park    24628  Subscriber        1983   \n",
       "19267       Newport Pkwy           Warren St    24613  Subscriber        1975   \n",
       "19268     Exchange Place  Liberty Light Rail    24668    Customer        1984   \n",
       "\n",
       "       Gender          Start Date  ... Stop Month  Stop Day  Stop Minute  \\\n",
       "19264       1 2015-10-01 00:16:26  ...         10         1           22   \n",
       "19265       1 2015-10-01 00:27:12  ...         10         1           39   \n",
       "19266       1 2015-10-01 00:32:46  ...         10         1           18   \n",
       "19267       1 2015-10-01 00:34:31  ...         10         1           39   \n",
       "19268       0 2015-10-01 00:40:12  ...         10         1           49   \n",
       "\n",
       "       Stop Week  Stop Weekday  Stop Weekofyear  Stop weekday  Stop dayofyear  \\\n",
       "19264         40            22               40             3             274   \n",
       "19265         40            39               40             3             274   \n",
       "19266         40            18               40             3             274   \n",
       "19267         40            39               40             3             274   \n",
       "19268         40            49               40             3             274   \n",
       "\n",
       "       Stop weekday_name  Stop quarter  \n",
       "19264                  3             4  \n",
       "19265                  3             4  \n",
       "19266                  3             4  \n",
       "19267                  3             4  \n",
       "19268                  3             4  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf['Start Month'] = adf['Start Date'].dt.month\n",
    "adf['Start Day'] = adf['Start Date'].dt.day\n",
    "adf['Start Minute'] = adf['Start Date'].dt.minute\n",
    "adf['Start Week'] = adf['Start Date'].dt.minute\n",
    "adf['Start Weekday'] = adf['Start Date'].dt.minute\n",
    "adf['Start Week'] = adf['Start Date'].dt.week\n",
    "adf['Start Weekofyear'] = adf['Start Date'].dt.weekofyear\n",
    "adf['Start weekday'] = adf['Start Date'].dt.weekday\n",
    "adf['Start dayofyear'] = adf['Start Date'].dt.dayofyear\n",
    "adf['Start weekday_name'] = adf['Start Date'].dt.weekday\n",
    "adf['Start quarter'] = adf['Start Date'].dt.quarter\n",
    "adf['Stop Month'] = adf['Stop Date'].dt.month\n",
    "adf['Stop Day'] = adf['Start Date'].dt.day\n",
    "adf['Stop Minute'] = adf['Stop Date'].dt.minute\n",
    "adf['Stop Week'] = adf['Stop Date'].dt.minute\n",
    "adf['Stop Weekday'] = adf['Stop Date'].dt.minute\n",
    "adf['Stop Week'] = adf['Stop Date'].dt.week\n",
    "adf['Stop Weekofyear'] = adf['Stop Date'].dt.weekofyear\n",
    "adf['Stop weekday'] = adf['Stop Date'].dt.weekday\n",
    "adf['Stop dayofyear'] = adf['Stop Date'].dt.dayofyear\n",
    "adf['Stop weekday_name'] = adf['Stop Date'].dt.weekday\n",
    "adf['Stop quarter'] = adf['Stop Date'].dt.quarter\n",
    "adf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 332952 entries, 19264 to 711099\n",
      "Data columns (total 31 columns):\n",
      " #   Column                Non-Null Count   Dtype         \n",
      "---  ------                --------------   -----         \n",
      " 0   Trip_Duration_in_min  332952 non-null  int64         \n",
      " 1   Start Time            332952 non-null  object        \n",
      " 2   Stop Time             332952 non-null  object        \n",
      " 3   Start Station Name    332952 non-null  object        \n",
      " 4   End Station Name      332952 non-null  object        \n",
      " 5   Bike ID               332952 non-null  int64         \n",
      " 6   User Type             332952 non-null  object        \n",
      " 7   Birth Year            332952 non-null  int32         \n",
      " 8   Gender                332952 non-null  int64         \n",
      " 9   Start Date            332952 non-null  datetime64[ns]\n",
      " 10  Stop Date             332952 non-null  datetime64[ns]\n",
      " 11  Start Month           332952 non-null  int64         \n",
      " 12  Start Day             332952 non-null  int64         \n",
      " 13  Start Minute          332952 non-null  int64         \n",
      " 14  Start Week            332952 non-null  int64         \n",
      " 15  Start Weekday         332952 non-null  int64         \n",
      " 16  Start Weekofyear      332952 non-null  int64         \n",
      " 17  Start weekday         332952 non-null  int64         \n",
      " 18  Start dayofyear       332952 non-null  int64         \n",
      " 19  Start weekday_name    332952 non-null  int64         \n",
      " 20  Start quarter         332952 non-null  int64         \n",
      " 21  Stop Month            332952 non-null  int64         \n",
      " 22  Stop Day              332952 non-null  int64         \n",
      " 23  Stop Minute           332952 non-null  int64         \n",
      " 24  Stop Week             332952 non-null  int64         \n",
      " 25  Stop Weekday          332952 non-null  int64         \n",
      " 26  Stop Weekofyear       332952 non-null  int64         \n",
      " 27  Stop weekday          332952 non-null  int64         \n",
      " 28  Stop dayofyear        332952 non-null  int64         \n",
      " 29  Stop weekday_name     332952 non-null  int64         \n",
      " 30  Stop quarter          332952 non-null  int64         \n",
      "dtypes: datetime64[ns](2), int32(1), int64(23), object(5)\n",
      "memory usage: 80.0+ MB\n"
     ]
    }
   ],
   "source": [
    "adf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Start Month</th>\n",
       "      <th>Start Day</th>\n",
       "      <th>Start Minute</th>\n",
       "      <th>Start Week</th>\n",
       "      <th>Start Weekofyear</th>\n",
       "      <th>Start dayofyear</th>\n",
       "      <th>Start quarter</th>\n",
       "      <th>Stop Month</th>\n",
       "      <th>Stop Day</th>\n",
       "      <th>Stop Minute</th>\n",
       "      <th>Stop Weekofyear</th>\n",
       "      <th>Stop weekday</th>\n",
       "      <th>Stop dayofyear</th>\n",
       "      <th>Stop quarter</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19264</th>\n",
       "      <td>6</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
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       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19265</th>\n",
       "      <td>12</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19266</th>\n",
       "      <td>45</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19267</th>\n",
       "      <td>5</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19268</th>\n",
       "      <td>9</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip_Duration_in_min Start Station Name    End Station Name  \\\n",
       "19264                     6    Christ Hospital         Oakland Ave   \n",
       "19265                    12        Oakland Ave     Christ Hospital   \n",
       "19266                    45       Lincoln Park        Lincoln Park   \n",
       "19267                     5       Newport Pkwy           Warren St   \n",
       "19268                     9     Exchange Place  Liberty Light Rail   \n",
       "\n",
       "        User Type  Birth Year  Start Month  Start Day  Start Minute  \\\n",
       "19264  Subscriber        1960           10          1            16   \n",
       "19265  Subscriber        1960           10          1            27   \n",
       "19266  Subscriber        1983           10          1            32   \n",
       "19267  Subscriber        1975           10          1            34   \n",
       "19268    Customer        1984           10          1            40   \n",
       "\n",
       "       Start Week  Start Weekofyear  Start dayofyear  Start quarter  \\\n",
       "19264          40                40              274              4   \n",
       "19265          40                40              274              4   \n",
       "19266          40                40              274              4   \n",
       "19267          40                40              274              4   \n",
       "19268          40                40              274              4   \n",
       "\n",
       "       Stop Month  Stop Day  Stop Minute  Stop Weekofyear  Stop weekday  \\\n",
       "19264          10         1           22               40             3   \n",
       "19265          10         1           39               40             3   \n",
       "19266          10         1           18               40             3   \n",
       "19267          10         1           39               40             3   \n",
       "19268          10         1           49               40             3   \n",
       "\n",
       "       Stop dayofyear  Stop quarter  Gender  \n",
       "19264             274             4       1  \n",
       "19265             274             4       1  \n",
       "19266             274             4       1  \n",
       "19267             274             4       1  \n",
       "19268             274             4       0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = adf.columns\n",
    "dafa = adf[['Trip_Duration_in_min', 'Start Station Name',\n",
    "       'End Station Name', 'User Type', 'Birth Year', \n",
    "      'Start Month', 'Start Day', 'Start Minute',\n",
    "       'Start Week', 'Start Weekofyear', \n",
    "       'Start dayofyear', 'Start quarter', 'Stop Month',\n",
    "       'Stop Day', 'Stop Minute',\n",
    "       'Stop Weekofyear', 'Stop weekday', 'Stop dayofyear',\n",
    "        'Stop quarter', 'Gender']]\n",
    "dafa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 332952 entries, 19264 to 711099\n",
      "Data columns (total 20 columns):\n",
      " #   Column                Non-Null Count   Dtype \n",
      "---  ------                --------------   ----- \n",
      " 0   Trip_Duration_in_min  332952 non-null  int64 \n",
      " 1   Start Station Name    332952 non-null  object\n",
      " 2   End Station Name      332952 non-null  object\n",
      " 3   User Type             332952 non-null  object\n",
      " 4   Birth Year            332952 non-null  int32 \n",
      " 5   Start Month           332952 non-null  int64 \n",
      " 6   Start Day             332952 non-null  int64 \n",
      " 7   Start Minute          332952 non-null  int64 \n",
      " 8   Start Week            332952 non-null  int64 \n",
      " 9   Start Weekofyear      332952 non-null  int64 \n",
      " 10  Start dayofyear       332952 non-null  int64 \n",
      " 11  Start quarter         332952 non-null  int64 \n",
      " 12  Stop Month            332952 non-null  int64 \n",
      " 13  Stop Day              332952 non-null  int64 \n",
      " 14  Stop Minute           332952 non-null  int64 \n",
      " 15  Stop Weekofyear       332952 non-null  int64 \n",
      " 16  Stop weekday          332952 non-null  int64 \n",
      " 17  Stop dayofyear        332952 non-null  int64 \n",
      " 18  Stop quarter          332952 non-null  int64 \n",
      " 19  Gender                332952 non-null  int64 \n",
      "dtypes: int32(1), int64(16), object(3)\n",
      "memory usage: 52.1+ MB\n"
     ]
    }
   ],
   "source": [
    "dafa.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Start Month</th>\n",
       "      <th>Start Day</th>\n",
       "      <th>Start Minute</th>\n",
       "      <th>Start Week</th>\n",
       "      <th>Start Weekofyear</th>\n",
       "      <th>Start dayofyear</th>\n",
       "      <th>Start quarter</th>\n",
       "      <th>Stop Month</th>\n",
       "      <th>Stop Day</th>\n",
       "      <th>Stop Minute</th>\n",
       "      <th>Stop Weekofyear</th>\n",
       "      <th>Stop weekday</th>\n",
       "      <th>Stop dayofyear</th>\n",
       "      <th>Stop quarter</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19264</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19265</th>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19266</th>\n",
       "      <td>44</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19267</th>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19268</th>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip_Duration_in_min  Start Station Name  End Station Name  User Type  \\\n",
       "19264                     5                   8                75          1   \n",
       "19265                    11                  42                19          1   \n",
       "19266                    44                  30                60          1   \n",
       "19267                     4                  40               103          1   \n",
       "19268                     8                  17                59          0   \n",
       "\n",
       "       Birth Year  Start Month  Start Day  Start Minute  Start Week  \\\n",
       "19264          24            9          0            16          39   \n",
       "19265          24            9          0            27          39   \n",
       "19266          47            9          0            32          39   \n",
       "19267          39            9          0            34          39   \n",
       "19268          48            9          0            40          39   \n",
       "\n",
       "       Start Weekofyear  Start dayofyear  Start quarter  Stop Month  Stop Day  \\\n",
       "19264                39              273              3           9         0   \n",
       "19265                39              273              3           9         0   \n",
       "19266                39              273              3           9         0   \n",
       "19267                39              273              3           9         0   \n",
       "19268                39              273              3           9         0   \n",
       "\n",
       "       Stop Minute  Stop Weekofyear  Stop weekday  Stop dayofyear  \\\n",
       "19264           22               39             3             273   \n",
       "19265           39               39             3             273   \n",
       "19266           18               39             3             273   \n",
       "19267           39               39             3             273   \n",
       "19268           49               39             3             273   \n",
       "\n",
       "       Stop quarter  Gender  \n",
       "19264             3       1  \n",
       "19265             3       1  \n",
       "19266             3       1  \n",
       "19267             3       1  \n",
       "19268             3       0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "data = dafa.apply(le.fit_transform)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "322952 10000\n",
      "322952 10000\n"
     ]
    }
   ],
   "source": [
    "X = data.iloc[:-10000,:-1]\n",
    "test_X = data.iloc[-10000:,:-1]\n",
    "print(len(X), len(test_X))\n",
    "\n",
    "y = data.iloc[:-10000,-1:]\n",
    "test_y = data.iloc[-10000:,-1:]\n",
    "print(len(y),len(test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(242214, 19) (80738, 19) (242214, 1) (80738, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "clf = DecisionTreeClassifier()\n",
    "kf = KFold(n_splits=3, random_state=None, shuffle=True)\n",
    "model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80.57853845362963\n"
     ]
    }
   ],
   "source": [
    "results = cross_val_score(model, X, y, cv=kf)\n",
    "print(results.mean()*100)"
   ]
  }
 ],
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