original phase 1 with 4 tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline\n",
    "sns.set(rc={'figure.figsize': [10, 10]}, font_scale=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi=pd.read_csv(r'F:\\mohamed\\3rd year\\semester 2\\machine learning\\project\\milestone 1\\taxi\\taxi-rides.csv')\n",
    "weather=pd.read_csv(r'F:\\mohamed\\3rd year\\semester 2\\machine learning\\project\\milestone 1\\taxi\\weather.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## start with Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>cab_type</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.62</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.544770e+12</td>\n",
       "      <td>West End</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1.0</td>\n",
       "      <td>c1b4a572-24b2-484d-aac5-b4f97b790480</td>\n",
       "      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.27</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543400e+12</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>f9e7e7e6-b309-479b-9f90-bf26a1870c81</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.544920e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154e8438-46f5-4e5f-9872-15cc95e85eed</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.98</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543620e+12</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6bdc30a6-9a80-487b-914e-8d9b82ee4acb</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.49</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543580e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0cb12fe9-cd72-4feb-b341-4a566b2e6643</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type    time_stamp         destination  \\\n",
       "0      0.62     Uber  1.544770e+12            West End   \n",
       "1      2.27     Uber  1.543400e+12   Boston University   \n",
       "2      2.00     Lyft  1.544920e+12            Back Bay   \n",
       "3      3.98     Lyft  1.543620e+12  Financial District   \n",
       "4      1.49     Lyft  1.543580e+12            Back Bay   \n",
       "\n",
       "                    source  surge_multiplier  \\\n",
       "0         Haymarket Square               1.0   \n",
       "1              Beacon Hill               1.0   \n",
       "2         Haymarket Square               1.0   \n",
       "3  Northeastern University               1.0   \n",
       "4  Northeastern University               1.0   \n",
       "\n",
       "                                     id                            product_id  \\\n",
       "0  c1b4a572-24b2-484d-aac5-b4f97b790480  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a   \n",
       "1  f9e7e7e6-b309-479b-9f90-bf26a1870c81  997acbb5-e102-41e1-b155-9df7de0a73f2   \n",
       "2  154e8438-46f5-4e5f-9872-15cc95e85eed                                  lyft   \n",
       "3  6bdc30a6-9a80-487b-914e-8d9b82ee4acb                             lyft_plus   \n",
       "4  0cb12fe9-cd72-4feb-b341-4a566b2e6643                                  lyft   \n",
       "\n",
       "       name  price  \n",
       "0      Taxi    NaN  \n",
       "1  UberPool    8.0  \n",
       "2      Lyft   10.5  \n",
       "3   Lyft XL   22.5  \n",
       "4      Lyft    7.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>554456.000000</td>\n",
       "      <td>5.544560e+05</td>\n",
       "      <td>554456.000000</td>\n",
       "      <td>510321.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.189834</td>\n",
       "      <td>1.544046e+12</td>\n",
       "      <td>1.013946</td>\n",
       "      <td>16.545307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.139779</td>\n",
       "      <td>6.892123e+08</td>\n",
       "      <td>0.091999</td>\n",
       "      <td>9.322049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.543200e+12</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.270000</td>\n",
       "      <td>1.543440e+12</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.160000</td>\n",
       "      <td>1.543740e+12</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.930000</td>\n",
       "      <td>1.544830e+12</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.860000</td>\n",
       "      <td>1.545160e+12</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>97.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            distance    time_stamp  surge_multiplier          price\n",
       "count  554456.000000  5.544560e+05     554456.000000  510321.000000\n",
       "mean        2.189834  1.544046e+12          1.013946      16.545307\n",
       "std         1.139779  6.892123e+08          0.091999       9.322049\n",
       "min         0.020000  1.543200e+12          1.000000       2.500000\n",
       "25%         1.270000  1.543440e+12          1.000000       9.000000\n",
       "50%         2.160000  1.543740e+12          1.000000      13.500000\n",
       "75%         2.930000  1.544830e+12          1.000000      22.500000\n",
       "max         7.860000  1.545160e+12          3.000000      97.500000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 554456 entries, 0 to 554455\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   distance          554456 non-null  float64\n",
      " 1   cab_type          554456 non-null  object \n",
      " 2   time_stamp        554456 non-null  float64\n",
      " 3   destination       554456 non-null  object \n",
      " 4   source            554456 non-null  object \n",
      " 5   surge_multiplier  554456 non-null  float64\n",
      " 6   id                554456 non-null  object \n",
      " 7   product_id        554456 non-null  object \n",
      " 8   name              554456 non-null  object \n",
      " 9   price             510321 non-null  float64\n",
      "dtypes: float64(4), object(6)\n",
      "memory usage: 42.3+ MB\n"
     ]
    }
   ],
   "source": [
    "taxi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(554456, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# investigate and find missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distance                0\n",
       "cab_type                0\n",
       "time_stamp              0\n",
       "destination             0\n",
       "source                  0\n",
       "surge_multiplier        0\n",
       "id                      0\n",
       "product_id              0\n",
       "name                    0\n",
       "price               44135\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " percentage of missing price values  = 7.96005453994546\n"
     ]
    }
   ],
   "source": [
    "print(' percentage of missing price values  = {}'.format((taxi['price'].isnull().sum()/len(taxi))*100))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## since `price` is our target so we will drop the rows containing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distance            0\n",
       "cab_type            0\n",
       "time_stamp          0\n",
       "destination         0\n",
       "source              0\n",
       "surge_multiplier    0\n",
       "id                  0\n",
       "product_id          0\n",
       "name                0\n",
       "price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.dropna(inplace=True)\n",
    "taxi.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# investigate and find Duplicated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['distance', 'cab_type', 'time_stamp', 'destination', 'source',\n",
       "       'surge_multiplier', 'id', 'product_id', 'name', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=taxi)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=taxi['price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=taxi['time_stamp'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=taxi['surge_multiplier'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eACA9wQMApCd4AID0BA8AkJ7gAQDSEzwAQHqCBwBIT/AAAOkJHgAgPcEDAKQneACA9AQPAJCe4AEA0hM8AEB6ggcASE/wAADpCR4AID3BAwCkJ3gAgPQEDwCQnuABANITPABAeoIHAEhP8AAA6QkeACA9wQMApCd4AID0BA8AkJ7gAQDSEzwAQHqCBwBIT/AAAOkJHgAgPcEDAKQneACA9CqLHoD91/TexmhYtbDoMTpU03sbIyIO+Of5/vM4sugxADodwXOAO/bYvkWPsN+qqiqisbFpj/u8/fb7X6o9e/Yqw0Qd6cgUrxnAgUbwHODGj59Q9Aj7rXfvQ2P9+neKHgOAxHyGBwBIT/AAAOkJHgAgPcEDAKQneACA9AQPAJCe4AEA0hM8AEB6ggcASE/wAADpCR4AID3BAwCkJ3gAgPQEDwCQnuABANITPABAeoIHAEhP8AAA6QkeACA9wQMApCd4AID0BA8AkJ7gAQDSEzwAQHqCBwBIT/AAAOkJHgAgPcEDAKQneACA9AQPAJCe4AEA0hM8AEB6ggcASE/wAADpCR4AID3BAwCkJ3gAgPQEDwCQnuABANITPABAeoIHAEhP8AAA6QkeACA9wQMApCd4AID0BA8AkJ7gAQDSEzwAQHqCBwBIT/AAAOkJHgAgvcq97dC1a5cOHaCjH/9AYA2sQWd//hHWIMIadPbnH2ENIj78GuztuC6lUqn0oR4ZAOAA4S0tACA9wQMApCd4AID0BA8AkJ7gAQDSEzwAQHqCBwBIT/AAAOkJHgAgvcKCZ968eTF69Og4++yz46GHHipqjEJt3rw5zj333Fi7dm3RoxRi1qxZMWbMmBgzZkzMmDGj6HEKcdddd8Xo0aNjzJgx8cADDxQ9TmGmT58eN954Y9FjFOLSSy+NMWPGxNixY2Ps2LGxZMmSokcqu0WLFsWFF14Yo0aNiu9973tFj1N2jz32WOvrP3bs2DjttNNi6tSpRY9VVk899VTrfw+mT5/eMScpFeAvf/lLafjw4aW33nqr9O6775bOO++80htvvFHEKIX53e9+Vzr33HNLJ598cmnNmjVFj1N2NTU1pS9+8YulrVu3lrZt21aaMGFCaf78+UWPVVYvvfRS6eKLLy41NjaWtmzZUho+fHjpzTffLHqssqutrS2dfvrppRtuuKHoUcquubm5NGTIkFJjY2PRoxRm9erVpSFDhpTq6+tL27ZtK11yySWl5557ruixCvPHP/6xNHLkyNKGDRuKHqVsGhoaSgMGDCht2LCh1NjYWLroootKNTU17X6eQq7w1NbWxhlnnBG9evWK7t27x+c///l45plnihilMHPmzInvfOc7cdRRRxU9SiF69+4dN954Yxx00EFRVVUVJ5xwQvz5z38ueqyy+sxnPhM/+9nPorKyMjZs2BBNTU3RvXv3oscqq40bN8add94ZV155ZdGjFOJPf/pTRERMnDgxvvCFL8Ts2bMLnqj8FixYEKNHj45jjjkmqqqq4s4774xTTjml6LEKc9ttt8XkyZPj8MMPL3qUsmlqaorm5ubYsmVLbN++PbZv3x4HH3xwu59nr38tvSOsW7cuevfu3Xr7qKOOiqVLlxYxSmG+//3vFz1CoU488cTWf69cuTJ+/etfx8MPP1zgRMWoqqqKmTNnxv333x/nnHNOHH300UWPVFa33nprTJ48Oerr64sepRCbNm2KgQMHxi233BKNjY0xYcKE+MQnPhGDBw8uerSyWbVqVVRVVcWVV14Z9fX1ceaZZ8Y3vvGNoscqRG1tbbz33nsxatSookcpqx49esR1110Xo0aNim7dusWAAQPi05/+dLufp5ArPM3NzdGly9/+jHupVGpzm87jjTfeiIkTJ8aUKVPi+OOPL3qcQlx77bVRV1cX9fX1MWfOnKLHKZvHHnssPv7xj8fAgQOLHqUwp556asyYMSMOPfTQOPzww+Oiiy6K559/vuixyqqpqSnq6upi2rRp8eijj8bSpUvjySefLHqsQjzyyCPx5S9/uegxyu7111+Pxx9/PH7zm9/ECy+8EF27do377ruv3c9TSPAcc8wxsX79+tbb69ev77Rv7XRmv/3tb+Pyyy+Pb33rW3HBBRcUPU7Zvfnmm/Haa69FRES3bt3i7LPPjuXLlxc8Vfn86le/ipqamhg7dmzMnDkzFi1aFNOmTSt6rLJ69dVXo66urvV2qVSKyspCLrwX5sgjj4yBAwfG4YcfHoccckh87nOf63RX/CMitm3bFq+88kqMGDGi6FHKbvHixTFw4MA44ogj4qCDDooLL7wwXn755XY/TyHBM2jQoKirq4u//vWvsWXLlpg/f34MHTq0iFEoSH19fVx99dXxgx/8IMaMGVP0OIVYu3Zt3HzzzbFt27bYtm1bLFy4ME477bSixyqbBx54IJ5++ul46qmn4tprr40RI0bETTfdVPRYZfXOO+/EjBkzYuvWrbF58+Z48sknY+TIkUWPVVbDhw+PxYsXx6ZNm6KpqSleeOGFOPnkk4seq+yWL18exx9/fKf7HF9ERHV1ddTW1kZDQ0OUSqVYtGhR9O/fv93PU8j/lTj66KNj8uTJMWHChGhsbIyLLrooPvWpTxUxCgW57777YuvWrXHHHXe03nfxxRfHJZdcUuBU5TVs2LBYunRpnH/++VFRURFnn312p42/zmr48OGxZMmSOP/886O5uTnGjx8fp556atFjldUpp5wSV1xxRYwfPz4aGxtj8ODBMW7cuKLHKrs1a9bEMcccU/QYhRgyZEj84Q9/iAsvvDCqqqqif//+MWnSpHY/T5dSqVRq90cFAPgI8ZuWAYD0BA8AkJ7gAQDSEzwAQHqCBwBIT/AAH9gzzzwTl156adx1110xd+7cPe47a9asePbZZ8szGMBedK5f6Qm0i+uuu26v+7z00kvRr1+/MkwDsHeCB9iju+66K+bNmxe9evWKvn37RkTEjTfeGCeeeGJ85StfiZkzZ8aCBQuiqqoqDjvssLj99ttjwYIF8fvf/z5mzJgRFRUV0a9fv5g6dWq8++67sX79+qiuro4f/ehHcfDBB7f+krGamppYt25d6y+hi4i455574sknn4zKysro27dv3HHHHXHooYfGY489Fg8//HA0NzdHr1694pZbbokTTjihyGUCPuK8pQXs1rPPPhvz58+PuXPnxiOPPBKbN29us72+vj4efPDBePzxx+OJJ56IwYMHx9KlS+NLX/pSfPKTn4wpU6bEyJEjY86cOXH++efHnDlzYv78+bF27dp47rnnIuL9vyF02GGHxSOPPBIzZ86M22+/PbZu3RoLFy6MJ554Ih599NF4+umno0+fPjF79ux4+eWXY+7cufHQQw/F3Llz44orrohrrrmmgNUBDiSu8AC7VVdXFyNHjowePXpERMS4cePi5z//eev2o48+Oqqrq+OCCy6IoUOHxtChQ3f518+vv/76qKmpiZ/85CexcuXKWLduXTQ0NLRuP+ussyIi4uSTT45t27ZFQ0ND1NXVxTnnnBM9e/aMiIhvf/vbERExY8aMWLVqVVx88cWtx2/atCk2btwYvXr1avc1AHIQPMAe7fjXZyoqKtps69q1a8yePTuWLVsWdXV1MW3atPjsZz8bU6ZMabPfN7/5zWhqaopRo0bFmWeeGfX19W0e9+CDD46IiC5durSes6KiovV2xPtRs2nTpmhubo6xY8fG9ddfHxERzc3NsW7dutYwAtgVb2kBuzV06NB45plnWkPjqaeearP99ddfj3PPPTdOOOGE+NrXvhaXX355LFu2LCLej6Pt27dHRMTixYvj6quvjtGjR0dExJIlS6KpqWmP5x40aFAsWLCg9W20H//4x/HTn/40hgwZEr/85S9j3bp1ERHx8MMPx2WXXdauzxvIxxUeYLeGDRsWy5cvj3HjxsXHPvaxqK6ujrfeeqt1e3V1dYwaNSrGjRsX3bt3j0MOOSRuvvnmiIgYMWJE/PCHP4zGxsaYPHlyXH311dG9e/fo0aNHDBgwIFavXr3Xc69YsSIuueSSiIjo169ffPe7340ePXrEV7/61Zg4cWJ06dIlevToEbNmzWpzNQhgZ/5aOgCQnre0AID0BA8AkJ7gAQDSEzwAQHqCBwBIT/AAAOkJHgAgPcEDAKT3/wF0OflnupbJpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=taxi['distance'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# remove_outliers using `IQR` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(df,columns):\n",
    "    for col in columns:\n",
    "        Q1=df[col].quantile(0.25)\n",
    "        Q3=df[col].quantile(0.75)\n",
    "        IQR=Q3-Q1\n",
    "        upper_limit=Q3+1.5*IQR\n",
    "        lower_limit=Q1-1.5*IQR\n",
    "        # outlires are below lower limit and above upper limit\n",
    "        outliers = ( ( df[col] < lower_limit) | (df[col] > upper_limit) )\n",
    "        indecies=df[outliers].index\n",
    "        print('number of outliers in {} is : {}'.format(col,len(indecies)))\n",
    "        df.drop(indecies,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of outliers in price is : 4477\n",
      "number of outliers in surge_multiplier is : 14622\n",
      "number of outliers in distance is : 5586\n"
     ]
    }
   ],
   "source": [
    "columns={'distance','surge_multiplier','price'}\n",
    "remove_outliers(taxi,columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=taxi['price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(485636, 10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>cab_type</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.27</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543400e+12</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>f9e7e7e6-b309-479b-9f90-bf26a1870c81</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.544920e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154e8438-46f5-4e5f-9872-15cc95e85eed</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.98</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543620e+12</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6bdc30a6-9a80-487b-914e-8d9b82ee4acb</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.49</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543580e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0cb12fe9-cd72-4feb-b341-4a566b2e6643</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.97</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543460e+12</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8ca92e07-e4b9-4191-a212-9098c5fed50a</td>\n",
       "      <td>6d318bcc-22a3-4af6-bddd-b409bfce1546</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type    time_stamp              destination  \\\n",
       "1      2.27     Uber  1.543400e+12        Boston University   \n",
       "2      2.00     Lyft  1.544920e+12                 Back Bay   \n",
       "3      3.98     Lyft  1.543620e+12       Financial District   \n",
       "4      1.49     Lyft  1.543580e+12                 Back Bay   \n",
       "5      1.97     Uber  1.543460e+12  Northeastern University   \n",
       "\n",
       "                    source  surge_multiplier  \\\n",
       "1              Beacon Hill               1.0   \n",
       "2         Haymarket Square               1.0   \n",
       "3  Northeastern University               1.0   \n",
       "4  Northeastern University               1.0   \n",
       "5              Beacon Hill               1.0   \n",
       "\n",
       "                                     id                            product_id  \\\n",
       "1  f9e7e7e6-b309-479b-9f90-bf26a1870c81  997acbb5-e102-41e1-b155-9df7de0a73f2   \n",
       "2  154e8438-46f5-4e5f-9872-15cc95e85eed                                  lyft   \n",
       "3  6bdc30a6-9a80-487b-914e-8d9b82ee4acb                             lyft_plus   \n",
       "4  0cb12fe9-cd72-4feb-b341-4a566b2e6643                                  lyft   \n",
       "5  8ca92e07-e4b9-4191-a212-9098c5fed50a  6d318bcc-22a3-4af6-bddd-b409bfce1546   \n",
       "\n",
       "        name  price  \n",
       "1   UberPool    8.0  \n",
       "2       Lyft   10.5  \n",
       "3    Lyft XL   22.5  \n",
       "4       Lyft    7.0  \n",
       "5  Black SUV   29.5  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4b8e9cb6-4cbf-46e2-b1fd-173a703fc7ff    1\n",
       "05093432-2037-4c12-844d-03ef597bbc8b    1\n",
       "3ff43946-38a4-4e23-9875-e7956cd94f0e    1\n",
       "40f77be2-55f8-4730-b6fa-01fb9cce9b52    1\n",
       "3302cc23-71f2-4aa3-a77f-079cdc8a66f3    1\n",
       "                                       ..\n",
       "eb45cc7a-3eed-40b5-b0e7-004c544b7436    1\n",
       "3309a612-5943-499e-9faf-d0b21a5f64b9    1\n",
       "b4b9bb06-352f-4c5a-b89f-1d3d85e38810    1\n",
       "761b25cf-cf9b-4f27-8575-db3dbffb685e    1\n",
       "35df394d-99f1-4cdb-baa2-dbb2c4300135    1\n",
       "Name: id, Length: 485636, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# we will drop this `id` feature due it doesn't provide any important information\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>cab_type</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.27</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543400e+12</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.544920e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.98</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543620e+12</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.49</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543580e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.97</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543460e+12</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6d318bcc-22a3-4af6-bddd-b409bfce1546</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type    time_stamp              destination  \\\n",
       "1      2.27     Uber  1.543400e+12        Boston University   \n",
       "2      2.00     Lyft  1.544920e+12                 Back Bay   \n",
       "3      3.98     Lyft  1.543620e+12       Financial District   \n",
       "4      1.49     Lyft  1.543580e+12                 Back Bay   \n",
       "5      1.97     Uber  1.543460e+12  Northeastern University   \n",
       "\n",
       "                    source  surge_multiplier  \\\n",
       "1              Beacon Hill               1.0   \n",
       "2         Haymarket Square               1.0   \n",
       "3  Northeastern University               1.0   \n",
       "4  Northeastern University               1.0   \n",
       "5              Beacon Hill               1.0   \n",
       "\n",
       "                             product_id       name  price  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2   UberPool    8.0  \n",
       "2                                  lyft       Lyft   10.5  \n",
       "3                             lyft_plus    Lyft XL   22.5  \n",
       "4                                  lyft       Lyft    7.0  \n",
       "5  6d318bcc-22a3-4af6-bddd-b409bfce1546  Black SUV   29.5  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.drop(['id'],axis=1,inplace=True)\n",
    "taxi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Back Bay                   41177\n",
       "West End                   41151\n",
       "Haymarket Square           40984\n",
       "Financial District         40983\n",
       "Beacon Hill                40976\n",
       "North End                  40949\n",
       "North Station              40893\n",
       "Theatre District           40858\n",
       "Fenway                     40761\n",
       "South Station              40280\n",
       "Northeastern University    39994\n",
       "Boston University          36630\n",
       "Name: destination, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North End                  42349\n",
       "Haymarket Square           42041\n",
       "North Station              41587\n",
       "West End                   41464\n",
       "South Station              41238\n",
       "Beacon Hill                40474\n",
       "Theatre District           40380\n",
       "Boston University          40249\n",
       "Fenway                     40104\n",
       "Northeastern University    40057\n",
       "Back Bay                   39039\n",
       "Financial District         36654\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UberX           43373\n",
       "UberPool        43266\n",
       "WAV             43176\n",
       "Black           43141\n",
       "UberXL          43121\n",
       "Black SUV       42736\n",
       "Shared          40570\n",
       "Lyft XL         37511\n",
       "Lux Black       37453\n",
       "Lyft            37391\n",
       "Lux             37368\n",
       "Lux Black XL    36530\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55c66225-fbe7-4fd5-9072-eab1ece5e23e    43373\n",
       "997acbb5-e102-41e1-b155-9df7de0a73f2    43266\n",
       "9a0e7b09-b92b-4c41-9779-2ad22b4d779d    43176\n",
       "6c84fd89-3f11-4782-9b50-97c468b19529    43141\n",
       "6f72dfc5-27f1-42e8-84db-ccc7a75f6969    43121\n",
       "6d318bcc-22a3-4af6-bddd-b409bfce1546    42736\n",
       "lyft_line                               40570\n",
       "lyft_plus                               37511\n",
       "lyft_lux                                37453\n",
       "lyft                                    37391\n",
       "lyft_premier                            37368\n",
       "lyft_luxsuv                             36530\n",
       "Name: product_id, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['product_id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "taxi=pd.get_dummies(data=taxi,columns=['destination','source','name','product_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>cab_type</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.27</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543400e+12</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.544920e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.98</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543620e+12</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.49</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543580e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.97</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543460e+12</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6d318bcc-22a3-4af6-bddd-b409bfce1546</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type    time_stamp              destination  \\\n",
       "1      2.27     Uber  1.543400e+12        Boston University   \n",
       "2      2.00     Lyft  1.544920e+12                 Back Bay   \n",
       "3      3.98     Lyft  1.543620e+12       Financial District   \n",
       "4      1.49     Lyft  1.543580e+12                 Back Bay   \n",
       "5      1.97     Uber  1.543460e+12  Northeastern University   \n",
       "\n",
       "                    source  surge_multiplier  \\\n",
       "1              Beacon Hill               1.0   \n",
       "2         Haymarket Square               1.0   \n",
       "3  Northeastern University               1.0   \n",
       "4  Northeastern University               1.0   \n",
       "5              Beacon Hill               1.0   \n",
       "\n",
       "                             product_id       name  price  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2   UberPool    8.0  \n",
       "2                                  lyft       Lyft   10.5  \n",
       "3                             lyft_plus    Lyft XL   22.5  \n",
       "4                                  lyft       Lyft    7.0  \n",
       "5  6d318bcc-22a3-4af6-bddd-b409bfce1546  Black SUV   29.5  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## we will continue working on the left nominal features only after mergeing the 2 data sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# now let's move to work on weather data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>location</th>\n",
       "      <th>clouds</th>\n",
       "      <th>pressure</th>\n",
       "      <th>rain</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.42</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.14</td>\n",
       "      <td>0.1228</td>\n",
       "      <td>1545003901</td>\n",
       "      <td>0.77</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.43</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.15</td>\n",
       "      <td>0.1846</td>\n",
       "      <td>1545003901</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.50</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.15</td>\n",
       "      <td>0.1089</td>\n",
       "      <td>1545003901</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.11</td>\n",
       "      <td>Fenway</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.13</td>\n",
       "      <td>0.0969</td>\n",
       "      <td>1545003901</td>\n",
       "      <td>0.77</td>\n",
       "      <td>11.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.13</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.14</td>\n",
       "      <td>0.1786</td>\n",
       "      <td>1545003901</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp            location  clouds  pressure    rain  time_stamp  humidity  \\\n",
       "0  42.42            Back Bay     1.0   1012.14  0.1228  1545003901      0.77   \n",
       "1  42.43         Beacon Hill     1.0   1012.15  0.1846  1545003901      0.76   \n",
       "2  42.50   Boston University     1.0   1012.15  0.1089  1545003901      0.76   \n",
       "3  42.11              Fenway     1.0   1012.13  0.0969  1545003901      0.77   \n",
       "4  43.13  Financial District     1.0   1012.14  0.1786  1545003901      0.75   \n",
       "\n",
       "    wind  \n",
       "0  11.25  \n",
       "1  11.32  \n",
       "2  11.07  \n",
       "3  11.09  \n",
       "4  11.49  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>clouds</th>\n",
       "      <th>pressure</th>\n",
       "      <th>rain</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6276.000000</td>\n",
       "      <td>6276.000000</td>\n",
       "      <td>6276.000000</td>\n",
       "      <td>894.000000</td>\n",
       "      <td>6.276000e+03</td>\n",
       "      <td>6276.000000</td>\n",
       "      <td>6276.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.090475</td>\n",
       "      <td>0.677777</td>\n",
       "      <td>1008.445209</td>\n",
       "      <td>0.057652</td>\n",
       "      <td>1.543857e+09</td>\n",
       "      <td>0.763985</td>\n",
       "      <td>6.802812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.022055</td>\n",
       "      <td>0.314284</td>\n",
       "      <td>12.870775</td>\n",
       "      <td>0.100758</td>\n",
       "      <td>6.659340e+05</td>\n",
       "      <td>0.127340</td>\n",
       "      <td>3.633466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.620000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>988.250000</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>1.543204e+09</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.077500</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>997.747500</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>1.543387e+09</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>3.517500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.130000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>1007.660000</td>\n",
       "      <td>0.014850</td>\n",
       "      <td>1.543514e+09</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>6.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.832500</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>1018.480000</td>\n",
       "      <td>0.060925</td>\n",
       "      <td>1.544691e+09</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>9.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>55.410000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1035.120000</td>\n",
       "      <td>0.780700</td>\n",
       "      <td>1.545159e+09</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>18.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              temp       clouds     pressure        rain    time_stamp  \\\n",
       "count  6276.000000  6276.000000  6276.000000  894.000000  6.276000e+03   \n",
       "mean     39.090475     0.677777  1008.445209    0.057652  1.543857e+09   \n",
       "std       6.022055     0.314284    12.870775    0.100758  6.659340e+05   \n",
       "min      19.620000     0.000000   988.250000    0.000200  1.543204e+09   \n",
       "25%      36.077500     0.440000   997.747500    0.004900  1.543387e+09   \n",
       "50%      40.130000     0.780000  1007.660000    0.014850  1.543514e+09   \n",
       "75%      42.832500     0.970000  1018.480000    0.060925  1.544691e+09   \n",
       "max      55.410000     1.000000  1035.120000    0.780700  1.545159e+09   \n",
       "\n",
       "          humidity         wind  \n",
       "count  6276.000000  6276.000000  \n",
       "mean      0.763985     6.802812  \n",
       "std       0.127340     3.633466  \n",
       "min       0.450000     0.290000  \n",
       "25%       0.670000     3.517500  \n",
       "50%       0.760000     6.570000  \n",
       "75%       0.890000     9.920000  \n",
       "max       0.990000    18.180000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6276 entries, 0 to 6275\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   temp        6276 non-null   float64\n",
      " 1   location    6276 non-null   object \n",
      " 2   clouds      6276 non-null   float64\n",
      " 3   pressure    6276 non-null   float64\n",
      " 4   rain        894 non-null    float64\n",
      " 5   time_stamp  6276 non-null   int64  \n",
      " 6   humidity    6276 non-null   float64\n",
      " 7   wind        6276 non-null   float64\n",
      "dtypes: float64(6), int64(1), object(1)\n",
      "memory usage: 392.4+ KB\n"
     ]
    }
   ],
   "source": [
    "weather.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6276, 8)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp             0\n",
       "location         0\n",
       "clouds           0\n",
       "pressure         0\n",
       "rain          5382\n",
       "time_stamp       0\n",
       "humidity         0\n",
       "wind             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " percentage of missing rain values  = 85.75525812619503\n"
     ]
    }
   ],
   "source": [
    "print(' percentage of missing rain values  = {}'.format((weather['rain'].isnull().sum()/len(weather))*100))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## percentage of missing rain values is about 85% so we will drop rain column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather.drop(['rain'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp          0\n",
       "location      0\n",
       "clouds        0\n",
       "pressure      0\n",
       "time_stamp    0\n",
       "humidity      0\n",
       "wind          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# find duplicate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['temp', 'location', 'clouds', 'pressure', 'time_stamp', 'humidity',\n",
       "       'wind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=weather)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=weather['temp'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=weather['clouds'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=weather['pressure'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=weather['time_stamp'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=weather['humidity'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=weather['wind'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## there are clear outliers on temp column so let's remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=weather['temp'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## - After I have removed these outliers using `IQR` method ,the boxplot still contains other outliers\n",
    "- That's mean that we have a `non-normal` distribution data, so we will use `median absolute deviation` method that can treat with such problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of outliers in temp is : 401\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def MAD(df,col,thresh_hold):\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.show()\n",
    "    from scipy import stats\n",
    "    med = float(np.median(df[col], axis = 0))\n",
    "    mad = (stats.median_absolute_deviation(df[col]))\n",
    "    outlier = []\n",
    "    for i, v in enumerate(df[col]):\n",
    "        t = (v-med)/mad\n",
    "        if t > thresh_hold or t<-thresh_hold:\n",
    "            outlier.append(i)\n",
    "        else:\n",
    "            continue\n",
    "    print('number of outliers in {} is : {}'.format( col,len(outlier)))\n",
    "\n",
    "    d=df.drop(outlier)\n",
    "    sns.boxplot(x=d[col])\n",
    "    plt.show()\n",
    "    return d\n",
    "\n",
    "weath=MAD(weather,'temp',thresh_hold=2.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>location</th>\n",
       "      <th>clouds</th>\n",
       "      <th>pressure</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.42</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.14</td>\n",
       "      <td>1545003901</td>\n",
       "      <td>0.77</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.43</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.15</td>\n",
       "      <td>1545003901</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.50</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.15</td>\n",
       "      <td>1545003901</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.11</td>\n",
       "      <td>Fenway</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.13</td>\n",
       "      <td>1545003901</td>\n",
       "      <td>0.77</td>\n",
       "      <td>11.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.13</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.14</td>\n",
       "      <td>1545003901</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp            location  clouds  pressure  time_stamp  humidity   wind\n",
       "0  42.42            Back Bay     1.0   1012.14  1545003901      0.77  11.25\n",
       "1  42.43         Beacon Hill     1.0   1012.15  1545003901      0.76  11.32\n",
       "2  42.50   Boston University     1.0   1012.15  1545003901      0.76  11.07\n",
       "3  42.11              Fenway     1.0   1012.13  1545003901      0.77  11.09\n",
       "4  43.13  Financial District     1.0   1012.14  1545003901      0.75  11.49"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weath.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5875, 7)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weath.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['destination'].unique().sort()==weath['location'].unique().sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Haymarket Square           492\n",
       "North End                  491\n",
       "Back Bay                   491\n",
       "Beacon Hill                490\n",
       "Boston University          490\n",
       "North Station              490\n",
       "West End                   489\n",
       "Fenway                     489\n",
       "Northeastern University    489\n",
       "South Station              488\n",
       "Theatre District           488\n",
       "Financial District         488\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weath['location'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "weath=pd.get_dummies(data=weath,columns=['location'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's merge our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weather shape:(5875, 7)\n",
      "Taxi shape:(485636, 9)\n"
     ]
    }
   ],
   "source": [
    "print('weather shape:{}'.format(weath.shape))\n",
    "print('Taxi shape:{}'.format(taxi.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=taxi.copy()\n",
    "data2=weath.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>cab_type</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.27</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1543400</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1544920</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.98</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1543620</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.49</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1543580</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.97</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1543460</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6d318bcc-22a3-4af6-bddd-b409bfce1546</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type time_stamp              destination  \\\n",
       "1      2.27     Uber    1543400        Boston University   \n",
       "2      2.00     Lyft    1544920                 Back Bay   \n",
       "3      3.98     Lyft    1543620       Financial District   \n",
       "4      1.49     Lyft    1543580                 Back Bay   \n",
       "5      1.97     Uber    1543460  Northeastern University   \n",
       "\n",
       "                    source  surge_multiplier  \\\n",
       "1              Beacon Hill               1.0   \n",
       "2         Haymarket Square               1.0   \n",
       "3  Northeastern University               1.0   \n",
       "4  Northeastern University               1.0   \n",
       "5              Beacon Hill               1.0   \n",
       "\n",
       "                             product_id       name  price  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2   UberPool    8.0  \n",
       "2                                  lyft       Lyft   10.5  \n",
       "3                             lyft_plus    Lyft XL   22.5  \n",
       "4                                  lyft       Lyft    7.0  \n",
       "5  6d318bcc-22a3-4af6-bddd-b409bfce1546  Black SUV   29.5  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "data['time_stamp'] = data['time_stamp'].astype(str)\n",
    "data2['time_stamp'] = data2['time_stamp'].astype(str)\n",
    "data['time_stamp'] = data['time_stamp'].str[:7]\n",
    "data2['time_stamp'] = data2['time_stamp'].str[:7]\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>destination</th>\n",
       "      <th>clouds</th>\n",
       "      <th>pressure</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.42</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.14</td>\n",
       "      <td>1545003</td>\n",
       "      <td>0.77</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.43</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.15</td>\n",
       "      <td>1545003</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.50</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.15</td>\n",
       "      <td>1545003</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.11</td>\n",
       "      <td>Fenway</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.13</td>\n",
       "      <td>1545003</td>\n",
       "      <td>0.77</td>\n",
       "      <td>11.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.13</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.14</td>\n",
       "      <td>1545003</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp         destination  clouds  pressure time_stamp  humidity   wind\n",
       "0  42.42            Back Bay     1.0   1012.14    1545003      0.77  11.25\n",
       "1  42.43         Beacon Hill     1.0   1012.15    1545003      0.76  11.32\n",
       "2  42.50   Boston University     1.0   1012.15    1545003      0.76  11.07\n",
       "3  42.11              Fenway     1.0   1012.13    1545003      0.77  11.09\n",
       "4  43.13  Financial District     1.0   1012.14    1545003      0.75  11.49"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.rename(columns={'location':'destination'},inplace=True)\n",
    "data2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(185349, 14)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedf = data2.merge(data, how='inner')\n",
    "mergedf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "mergedf.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149717, 14)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedf.duplicated().sum() == 0\n",
    "mergedf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data=mergedf.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>destination</th>\n",
       "      <th>clouds</th>\n",
       "      <th>pressure</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "      <th>distance</th>\n",
       "      <th>cab_type</th>\n",
       "      <th>source</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45.52</td>\n",
       "      <td>South Station</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1000.08</td>\n",
       "      <td>1543830</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.39</td>\n",
       "      <td>2.39</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45.52</td>\n",
       "      <td>South Station</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1000.08</td>\n",
       "      <td>1543830</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.55</td>\n",
       "      <td>Uber</td>\n",
       "      <td>Theatre District</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6c84fd89-3f11-4782-9b50-97c468b19529</td>\n",
       "      <td>Black</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.52</td>\n",
       "      <td>South Station</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1000.08</td>\n",
       "      <td>1543830</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.44</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft_lux</td>\n",
       "      <td>Lux Black</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.52</td>\n",
       "      <td>South Station</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1000.08</td>\n",
       "      <td>1543830</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.39</td>\n",
       "      <td>2.17</td>\n",
       "      <td>Uber</td>\n",
       "      <td>North Station</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6f72dfc5-27f1-42e8-84db-ccc7a75f6969</td>\n",
       "      <td>UberXL</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45.52</td>\n",
       "      <td>South Station</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1000.08</td>\n",
       "      <td>1543830</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.39</td>\n",
       "      <td>2.14</td>\n",
       "      <td>Uber</td>\n",
       "      <td>West End</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55c66225-fbe7-4fd5-9072-eab1ece5e23e</td>\n",
       "      <td>UberX</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp    destination  clouds  pressure time_stamp  humidity  wind  \\\n",
       "0  45.52  South Station    0.89   1000.08    1543830      0.95  1.39   \n",
       "1  45.52  South Station    0.89   1000.08    1543830      0.95  1.39   \n",
       "2  45.52  South Station    0.89   1000.08    1543830      0.95  1.39   \n",
       "3  45.52  South Station    0.89   1000.08    1543830      0.95  1.39   \n",
       "4  45.52  South Station    0.89   1000.08    1543830      0.95  1.39   \n",
       "\n",
       "   distance cab_type              source  surge_multiplier  \\\n",
       "0      2.39     Lyft         Beacon Hill               1.0   \n",
       "1      0.55     Uber    Theatre District               1.0   \n",
       "2      0.44     Lyft  Financial District               1.0   \n",
       "3      2.17     Uber       North Station               1.0   \n",
       "4      2.14     Uber            West End               1.0   \n",
       "\n",
       "                             product_id       name  price  \n",
       "0                             lyft_plus    Lyft XL   16.5  \n",
       "1  6c84fd89-3f11-4782-9b50-97c468b19529      Black   15.5  \n",
       "2                              lyft_lux  Lux Black   16.5  \n",
       "3  6f72dfc5-27f1-42e8-84db-ccc7a75f6969     UberXL   14.0  \n",
       "4  55c66225-fbe7-4fd5-9072-eab1ece5e23e      UberX    8.5  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying some Exploratory Data Analysis and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distances\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=data['distance'],kde=False,bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## number of cab_type with respect to sources and destinations of the travels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cab_type,source'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(['cab_type'])['source'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cab_type,destination'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(['cab_type'])['destination'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## overall outcome for each cab_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cab_type\n",
       "Lyft    1259085.08\n",
       "Uber    1177619.00\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('cab_type')['price'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### overall surge_multiplier for each cab_type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cab_type\n",
       "Lyft    76206.0\n",
       "Uber    73511.0\n",
       "Name: surge_multiplier, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('cab_type')['surge_multiplier'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## know overall distances to specific destinations \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('destination')['distance'].sum().plot.pie(autopct=\"%.1f%%\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(data=data,kind='reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This plot can help us in feature selection , as we can conclude and choose  good features upon the tendency and `correlaton` between each `feature` and the `target` and the other `features` and that's clear in the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='distance', ylabel='price'>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='distance',y='price',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## there is a clear correlation \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# let's apply feature selection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(),annot=True,cmap='RdBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we will drop  `surge_multiplier` since it has no correlation with any other feature\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['surge_multiplier'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- we will drop `temp` and keep `humidity` ,since one of them is redundunt due to high correlation between.\n",
    "- and because `humidity` has higher coorrelation with the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['temp'],axis=1,inplace=True)#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ans so\n",
    "- we will drop `wind` and keep `pressure` ,since one of them is redundunt due to high correlation between.\n",
    "- and because `pressure` has higher coorrelation with the target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['wind'],axis=1,inplace=True)#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ans so\n",
    "- we will drop `pressure` and keep `humidity` ,since one of them is redundunt due to high correlation between.\n",
    "- and because `humidity` has higher coorrelation with the target\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data.drop(['humidity'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ans so\n",
    "- we will drop `clouds` and keep `humidity` ,since one of them is redundunt due to high correlation between.\n",
    "- and because `humidity` has higher coorrelation with the target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['clouds'],axis=1,inplace=True) #\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['time_stamp'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['pressure','humidity'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# After we have tried merging our taxi data with weather,we tried to apply feature selection to find which variables of weather \n",
    "\n",
    "- data we are going to use ,finally we can conclude that we will no longer use weather data and we will use the whole records \n",
    "\n",
    "- of the Taxi data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "data=taxi.copy()\n",
    "data['time_stamp'] = data['time_stamp'].apply(lambda x: int(x / 1000))\n",
    "data['time_stamp'] = data['time_stamp'].apply(lambda ts: datetime.utcfromtimestamp(int(ts)).strftime('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.get_dummies(data,columns=['name','cab_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([ 'product_id','time_stamp','destination','source','surge_multiplier'],axis=1,inplace=True)\n",
    "x=data.drop('price',axis=1)\n",
    "y=data[['price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>name_Black</th>\n",
       "      <th>name_Black SUV</th>\n",
       "      <th>name_Lux</th>\n",
       "      <th>name_Lux Black</th>\n",
       "      <th>name_Lux Black XL</th>\n",
       "      <th>name_Lyft</th>\n",
       "      <th>name_Lyft XL</th>\n",
       "      <th>name_Shared</th>\n",
       "      <th>name_UberPool</th>\n",
       "      <th>name_UberX</th>\n",
       "      <th>name_UberXL</th>\n",
       "      <th>name_WAV</th>\n",
       "      <th>cab_type_Lyft</th>\n",
       "      <th>cab_type_Uber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>554452</th>\n",
       "      <td>0.92</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>485636 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance  name_Black  name_Black SUV  name_Lux  name_Lux Black  \\\n",
       "1           2.27           0               0         0               0   \n",
       "2           2.00           0               0         0               0   \n",
       "3           3.98           0               0         0               0   \n",
       "4           1.49           0               0         0               0   \n",
       "5           1.97           0               1         0               0   \n",
       "...          ...         ...             ...       ...             ...   \n",
       "554451      1.64           0               0         0               0   \n",
       "554452      0.92           0               1         0               0   \n",
       "554453      2.66           0               0         0               0   \n",
       "554454      2.98           0               0         0               0   \n",
       "554455      1.91           0               0         0               0   \n",
       "\n",
       "        name_Lux Black XL  name_Lyft  name_Lyft XL  name_Shared  \\\n",
       "1                       0          0             0            0   \n",
       "2                       0          1             0            0   \n",
       "3                       0          0             1            0   \n",
       "4                       0          1             0            0   \n",
       "5                       0          0             0            0   \n",
       "...                   ...        ...           ...          ...   \n",
       "554451                  0          0             0            0   \n",
       "554452                  0          0             0            0   \n",
       "554453                  0          0             0            0   \n",
       "554454                  0          0             0            0   \n",
       "554455                  0          0             0            0   \n",
       "\n",
       "        name_UberPool  name_UberX  name_UberXL  name_WAV  cab_type_Lyft  \\\n",
       "1                   1           0            0         0              0   \n",
       "2                   0           0            0         0              1   \n",
       "3                   0           0            0         0              1   \n",
       "4                   0           0            0         0              1   \n",
       "5                   0           0            0         0              0   \n",
       "...               ...         ...          ...       ...            ...   \n",
       "554451              0           0            1         0              0   \n",
       "554452              0           0            0         0              0   \n",
       "554453              0           0            1         0              0   \n",
       "554454              0           1            0         0              0   \n",
       "554455              0           0            1         0              0   \n",
       "\n",
       "        cab_type_Uber  \n",
       "1                   1  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  \n",
       "5                   1  \n",
       "...               ...  \n",
       "554451              1  \n",
       "554452              1  \n",
       "554453              1  \n",
       "554454              1  \n",
       "554455              1  \n",
       "\n",
       "[485636 rows x 15 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554451</th>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>554452</th>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554453</th>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554454</th>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554455</th>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>485636 rows × 1 columns</p>\n",
       "</div>"
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      "text/plain": [
       "        price\n",
       "1         8.0\n",
       "2        10.5\n",
       "3        22.5\n",
       "4         7.0\n",
       "5        29.5\n",
       "...       ...\n",
       "554451   13.0\n",
       "554452   27.5\n",
       "554453   16.0\n",
       "554454   11.5\n",
       "554455   13.5\n",
       "\n",
       "[485636 rows x 1 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: (388508, 15)\n",
      "test: (97128, 15)\n"
     ]
    }
   ],
   "source": [
    "print('train:',X_train.shape)\n",
    "print('test:',X_test.shape)\n",
    "import time\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ploynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed time=0:00:03.545392\n",
      "train error / Root_mean_squared_error :1.7649877980712445\n",
      "train error / r2_score :0.9579845135605488\n",
      "test error / Root_mean_squared_error :1.7630273247393922\n",
      "test error / r2_score :0.9580203236219828\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "pf = PolynomialFeatures(2)\n",
    "X_poly=pf.fit_transform(x)\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_poly,y,test_size=0.25,shuffle=True,random_state=46)#46\n",
    "model=LinearRegression()\n",
    "\n",
    "start=datetime.now()\n",
    "model.fit(X_train,y_train)\n",
    "end=datetime.now()\n",
    "print('elapsed time={}'.format(end-start))\n",
    "\n",
    "# Saving the model to ploy.pkl\n",
    "joblib.dump(model, 'model_poly.pkl')\n",
    "#model_polys=joblib.load('model_poly.pkl')\n",
    "\n",
    "\n",
    "y_pred_train= model.predict(X_train)\n",
    "y_pred_test= model.predict(X_test)\n",
    "\n",
    "\n",
    "print(\"train error / Root_mean_squared_error :{}\".format(mean_squared_error(y_train,y_pred_train,squared=False)))\n",
    "print(\"train error / r2_score :{}\".format(r2_score(y_train,y_pred_train)))\n",
    "\n",
    "print(\"test error / Root_mean_squared_error :{}\".format(mean_squared_error(y_test,y_pred_test,squared=False)))\n",
    "print(\"test error / r2_score :{}\".format(r2_score(y_test,y_pred_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L2 Regularization: Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train error / Root_mean_squared_error :1.8686930938976944\n",
      "train error / r2_score :0.9547317306195743\n",
      "test error / Root_mean_squared_error :1.8440148895744963\n",
      "test error / r2_score :0.9558746844549015\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "alphas = [0.0005, 0.005,0.001, 0.1,0.5, 1, 3, 5, 10, 15, 30, 80]\n",
    "\n",
    "start=time.time()\n",
    "ridgeCV = RidgeCV(alphas=alphas).fit(X_train,y_train)\n",
    "end=time.time()\n",
    "Total_training_time=end- start\n",
    "\n",
    "# Saving the model to ploy.pkl\n",
    "joblib.dump(ridgeCV, 'model_ridge.pkl')\n",
    "#model_ridges=joblib.load('model_ridge.pkl')\n",
    "\n",
    "y_pred_train= ridgeCV.predict(X_train)\n",
    "y_pred_test= ridgeCV.predict(X_test)\n",
    "\n",
    "\n",
    "print(\"train error / Root_mean_squared_error :{}\".format(mean_squared_error(y_train,y_pred_train,squared=False)))\n",
    "print(\"train error / r2_score :{}\".format(r2_score(y_train,y_pred_train)))\n",
    "\n",
    "print(\"test error / Root_mean_squared_error :{}\".format(mean_squared_error(y_test,y_pred_test,squared=False)))\n",
    "print(\"test error / r2_score :{}\".format(r2_score(y_test,y_pred_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "model = xgb.XGBRegressor(objective=\"reg:linear\",n_estimators=50, max_depth=5,random_state=10, eta=0.1, subsample=0.7, colsample_bytree=0.7)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train= model.predict(X_train)\n",
    "y_pred_test= model.predict(X_test)\n",
    "\n",
    "print(\"train error / Root_mean_squared_error :{}\".format(mean_squared_error(y_train,y_pred_train,squared=False)))\n",
    "print(\"train error / r2_score :{}\".format(r2_score(y_train,y_pred_train)))\n",
    "\n",
    "print(\"test error / Root_mean_squared_error :{}\".format(mean_squared_error(y_test,y_pred_test,squared=False)))\n",
    "print(\"test error / r2_score :{}\".format(r2_score(y_test,y_pred_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>cab_type</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.14</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543230e+12</td>\n",
       "      <td>South Station</td>\n",
       "      <td>North Station</td>\n",
       "      <td>1</td>\n",
       "      <td>d17c8f8f-5018-4dc1-b86b-0e67849e5f57</td>\n",
       "      <td>lyft_luxsuv</td>\n",
       "      <td>Lux Black XL</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.08</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543280e+12</td>\n",
       "      <td>North End</td>\n",
       "      <td>North Station</td>\n",
       "      <td>1</td>\n",
       "      <td>4258d0c6-e1e5-45fb-bcee-d2a02aa5e6b0</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.57</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543630e+12</td>\n",
       "      <td>North Station</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1</td>\n",
       "      <td>a0a9b20e-d5ed-4eab-b2a5-d6ebd5eb3214</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type    time_stamp    destination            source  \\\n",
       "0      2.14     Lyft  1.543230e+12  South Station     North Station   \n",
       "1      1.08     Uber  1.543280e+12      North End     North Station   \n",
       "2      0.57     Lyft  1.543630e+12  North Station  Haymarket Square   \n",
       "\n",
       "   surge_multiplier                                    id  \\\n",
       "0                 1  d17c8f8f-5018-4dc1-b86b-0e67849e5f57   \n",
       "1                 1  4258d0c6-e1e5-45fb-bcee-d2a02aa5e6b0   \n",
       "2                 1  a0a9b20e-d5ed-4eab-b2a5-d6ebd5eb3214   \n",
       "\n",
       "                             product_id          name  price  \n",
       "0                           lyft_luxsuv  Lux Black XL   27.5  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2      UberPool    6.5  \n",
       "2                             lyft_plus       Lyft XL    9.0  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_sample=pd.read_csv(r'F:\\mohamed\\3rd year\\semester 2\\machine learning\\project\\ProjectTestSamples\\Milestone 1\\taxi-test-samples.csv')\n",
    "taxi_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_sample['time_stamp'] = taxi_sample['time_stamp'].apply(lambda x: int(x / 1000))\n",
    "taxi_sample['time_stamp'] = taxi_sample['time_stamp'].apply(lambda ts: datetime.utcfromtimestamp(int(ts)).strftime('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>cab_type</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.27</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543400e+12</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.544920e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.98</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543620e+12</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.49</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1.543580e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.97</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543460e+12</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6d318bcc-22a3-4af6-bddd-b409bfce1546</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554451</th>\n",
       "      <td>1.64</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543620e+12</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6f72dfc5-27f1-42e8-84db-ccc7a75f6969</td>\n",
       "      <td>UberXL</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554452</th>\n",
       "      <td>0.92</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543550e+12</td>\n",
       "      <td>North Station</td>\n",
       "      <td>North End</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6d318bcc-22a3-4af6-bddd-b409bfce1546</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554453</th>\n",
       "      <td>2.66</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543530e+12</td>\n",
       "      <td>Theatre District</td>\n",
       "      <td>Fenway</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6f72dfc5-27f1-42e8-84db-ccc7a75f6969</td>\n",
       "      <td>UberXL</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554454</th>\n",
       "      <td>2.98</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543650e+12</td>\n",
       "      <td>Theatre District</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55c66225-fbe7-4fd5-9072-eab1ece5e23e</td>\n",
       "      <td>UberX</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554455</th>\n",
       "      <td>1.91</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.543620e+12</td>\n",
       "      <td>Theatre District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6f72dfc5-27f1-42e8-84db-ccc7a75f6969</td>\n",
       "      <td>UberXL</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>485636 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance cab_type    time_stamp              destination  \\\n",
       "1           2.27     Uber  1.543400e+12        Boston University   \n",
       "2           2.00     Lyft  1.544920e+12                 Back Bay   \n",
       "3           3.98     Lyft  1.543620e+12       Financial District   \n",
       "4           1.49     Lyft  1.543580e+12                 Back Bay   \n",
       "5           1.97     Uber  1.543460e+12  Northeastern University   \n",
       "...          ...      ...           ...                      ...   \n",
       "554451      1.64     Uber  1.543620e+12                 Back Bay   \n",
       "554452      0.92     Uber  1.543550e+12            North Station   \n",
       "554453      2.66     Uber  1.543530e+12         Theatre District   \n",
       "554454      2.98     Uber  1.543650e+12         Theatre District   \n",
       "554455      1.91     Uber  1.543620e+12         Theatre District   \n",
       "\n",
       "                         source  surge_multiplier  \\\n",
       "1                   Beacon Hill               1.0   \n",
       "2              Haymarket Square               1.0   \n",
       "3       Northeastern University               1.0   \n",
       "4       Northeastern University               1.0   \n",
       "5                   Beacon Hill               1.0   \n",
       "...                         ...               ...   \n",
       "554451  Northeastern University               1.0   \n",
       "554452                North End               1.0   \n",
       "554453                   Fenway               1.0   \n",
       "554454        Boston University               1.0   \n",
       "554455  Northeastern University               1.0   \n",
       "\n",
       "                                  product_id       name  price  \n",
       "1       997acbb5-e102-41e1-b155-9df7de0a73f2   UberPool    8.0  \n",
       "2                                       lyft       Lyft   10.5  \n",
       "3                                  lyft_plus    Lyft XL   22.5  \n",
       "4                                       lyft       Lyft    7.0  \n",
       "5       6d318bcc-22a3-4af6-bddd-b409bfce1546  Black SUV   29.5  \n",
       "...                                      ...        ...    ...  \n",
       "554451  6f72dfc5-27f1-42e8-84db-ccc7a75f6969     UberXL   13.0  \n",
       "554452  6d318bcc-22a3-4af6-bddd-b409bfce1546  Black SUV   27.5  \n",
       "554453  6f72dfc5-27f1-42e8-84db-ccc7a75f6969     UberXL   16.0  \n",
       "554454  55c66225-fbe7-4fd5-9072-eab1ece5e23e      UberX   11.5  \n",
       "554455  6f72dfc5-27f1-42e8-84db-ccc7a75f6969     UberXL   13.5  \n",
       "\n",
       "[485636 rows x 9 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   distance          3 non-null      float64\n",
      " 1   cab_type          3 non-null      object \n",
      " 2   time_stamp        3 non-null      object \n",
      " 3   destination       3 non-null      object \n",
      " 4   source            3 non-null      object \n",
      " 5   surge_multiplier  3 non-null      int64  \n",
      " 6   id                3 non-null      object \n",
      " 7   product_id        3 non-null      object \n",
      " 8   name              3 non-null      object \n",
      " 9   price             3 non-null      float64\n",
      "dtypes: float64(2), int64(1), object(7)\n",
      "memory usage: 368.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "taxi_sample.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distance            0\n",
       "cab_type            0\n",
       "time_stamp          0\n",
       "destination         0\n",
       "source              0\n",
       "surge_multiplier    0\n",
       "id                  0\n",
       "product_id          0\n",
       "name                0\n",
       "price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_sample.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Handling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "#incase of null values #categorical\n",
    "#taxi_sample.fillna(taxi_sample['destination'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "#incase of null values #continuous\n",
    "#taxi_sample.fillna(taxi_sample['distance'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_sample.duplicated().sum()==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_sample.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "taxi_sample=taxi_sample.drop(['destination','source','product_id','id','surge_multiplier','time_stamp'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_sample = taxi_sample.reindex(sorted(taxi_sample.columns), axis=1)\n",
    "data = data.reindex(sorted(data.columns), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>cab_type</th>\n",
       "      <th>distance</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>2.14</td>\n",
       "      <td>Lux Black XL</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uber</td>\n",
       "      <td>1.08</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>0.57</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cab_type  distance          name  price\n",
       "0     Lyft      2.14  Lux Black XL   27.5\n",
       "1     Uber      1.08      UberPool    6.5\n",
       "2     Lyft      0.57       Lyft XL    9.0"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>cab_type_Lyft</th>\n",
       "      <th>cab_type_Uber</th>\n",
       "      <th>distance</th>\n",
       "      <th>name_Black</th>\n",
       "      <th>name_Black SUV</th>\n",
       "      <th>name_Lux</th>\n",
       "      <th>name_Lux Black</th>\n",
       "      <th>name_Lux Black XL</th>\n",
       "      <th>name_Lyft</th>\n",
       "      <th>name_Lyft XL</th>\n",
       "      <th>name_Shared</th>\n",
       "      <th>name_UberPool</th>\n",
       "      <th>name_UberX</th>\n",
       "      <th>name_UberXL</th>\n",
       "      <th>name_WAV</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554451</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554452</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554453</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554454</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554455</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>485636 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cab_type_Lyft  cab_type_Uber  distance  name_Black  name_Black SUV  \\\n",
       "1                   0              1      2.27           0               0   \n",
       "2                   1              0      2.00           0               0   \n",
       "3                   1              0      3.98           0               0   \n",
       "4                   1              0      1.49           0               0   \n",
       "5                   0              1      1.97           0               1   \n",
       "...               ...            ...       ...         ...             ...   \n",
       "554451              0              1      1.64           0               0   \n",
       "554452              0              1      0.92           0               1   \n",
       "554453              0              1      2.66           0               0   \n",
       "554454              0              1      2.98           0               0   \n",
       "554455              0              1      1.91           0               0   \n",
       "\n",
       "        name_Lux  name_Lux Black  name_Lux Black XL  name_Lyft  name_Lyft XL  \\\n",
       "1              0               0                  0          0             0   \n",
       "2              0               0                  0          1             0   \n",
       "3              0               0                  0          0             1   \n",
       "4              0               0                  0          1             0   \n",
       "5              0               0                  0          0             0   \n",
       "...          ...             ...                ...        ...           ...   \n",
       "554451         0               0                  0          0             0   \n",
       "554452         0               0                  0          0             0   \n",
       "554453         0               0                  0          0             0   \n",
       "554454         0               0                  0          0             0   \n",
       "554455         0               0                  0          0             0   \n",
       "\n",
       "        name_Shared  name_UberPool  name_UberX  name_UberXL  name_WAV  price  \n",
       "1                 0              1           0            0         0    8.0  \n",
       "2                 0              0           0            0         0   10.5  \n",
       "3                 0              0           0            0         0   22.5  \n",
       "4                 0              0           0            0         0    7.0  \n",
       "5                 0              0           0            0         0   29.5  \n",
       "...             ...            ...         ...          ...       ...    ...  \n",
       "554451            0              0           0            1         0   13.0  \n",
       "554452            0              0           0            0         0   27.5  \n",
       "554453            0              0           0            1         0   16.0  \n",
       "554454            0              0           1            0         0   11.5  \n",
       "554455            0              0           0            1         0   13.5  \n",
       "\n",
       "[485636 rows x 16 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "testT=taxi_sample.copy()\n",
    "alld=taxi.copy()\n",
    "all = pd.concat([alld , testT],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "all.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "all =pd.get_dummies(all ,columns=['name','cab_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "all.drop(['product_id','time_stamp','destination','source','surge_multiplier'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=len(testT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "tests=all.iloc[-l:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=tests.drop('price',axis=1)\n",
    "y=tests[['price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>name_Black</th>\n",
       "      <th>name_Black SUV</th>\n",
       "      <th>name_Lux</th>\n",
       "      <th>name_Lux Black</th>\n",
       "      <th>name_Lux Black XL</th>\n",
       "      <th>name_Lyft</th>\n",
       "      <th>name_Lyft XL</th>\n",
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       "      <th>name_UberXL</th>\n",
       "      <th>name_WAV</th>\n",
       "      <th>cab_type_Lyft</th>\n",
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       "      <th>554455</th>\n",
       "      <td>1.91</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance  name_Black  name_Black SUV  name_Lux  name_Lux Black  \\\n",
       "554453      2.66           0               0         0               0   \n",
       "554454      2.98           0               0         0               0   \n",
       "554455      1.91           0               0         0               0   \n",
       "\n",
       "        name_Lux Black XL  name_Lyft  name_Lyft XL  name_Shared  \\\n",
       "554453                  0          0             0            0   \n",
       "554454                  0          0             0            0   \n",
       "554455                  0          0             0            0   \n",
       "\n",
       "        name_UberPool  name_UberX  name_UberXL  name_WAV  cab_type_Lyft  \\\n",
       "554453              0           0            1         0              0   \n",
       "554454              0           1            0         0              0   \n",
       "554455              0           0            1         0              0   \n",
       "\n",
       "        cab_type_Uber  \n",
       "554453              1  \n",
       "554454              1  \n",
       "554455              1  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_poly=pf.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 136)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_poly.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_polys=joblib.load('model_poly.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train error / Root_mean_squared_error :0.9822407233743526\n",
      "train error / r2_score :0.7153058508886969\n"
     ]
    }
   ],
   "source": [
    "y_test_d=model_polys.predict(x_poly)\n",
    "#\n",
    "print(\"train error / Root_mean_squared_error :{}\".format(mean_squared_error(y,y_test_d,squared=False)))\n",
    "print(\"train error / r2_score :{}\".format(r2_score(y,y_test_d)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17.05280113],\n",
       "       [10.94490623],\n",
       "       [14.71567726]])"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>554453</th>\n",
       "      <td>16.0</td>\n",
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       "      <td>11.5</td>\n",
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       "        price\n",
       "554453   16.0\n",
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