original phase2 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 datetime import datetime\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.feature_selection import chi2 \n",
    "from sklearn.preprocessing import LabelEncoder\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 2\\Milestone 2 Datasets\\taxi\\taxi-rides-classification.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>RideCategory</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>unknown</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>cheap</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>moderate</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>expensive</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>cheap</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 RideCategory  \n",
       "0      Taxi      unknown  \n",
       "1  UberPool        cheap  \n",
       "2      Lyft     moderate  \n",
       "3   Lyft XL    expensive  \n",
       "4      Lyft        cheap  "
      ]
     },
     "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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.189834</td>\n",
       "      <td>1.544046e+12</td>\n",
       "      <td>1.013946</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.543200e+12</td>\n",
       "      <td>1.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.160000</td>\n",
       "      <td>1.543740e+12</td>\n",
       "      <td>1.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.860000</td>\n",
       "      <td>1.545160e+12</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            distance    time_stamp  surge_multiplier\n",
       "count  554456.000000  5.544560e+05     554456.000000\n",
       "mean        2.189834  1.544046e+12          1.013946\n",
       "std         1.139779  6.892123e+08          0.091999\n",
       "min         0.020000  1.543200e+12          1.000000\n",
       "25%         1.270000  1.543440e+12          1.000000\n",
       "50%         2.160000  1.543740e+12          1.000000\n",
       "75%         2.930000  1.544830e+12          1.000000\n",
       "max         7.860000  1.545160e+12          3.000000"
      ]
     },
     "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   RideCategory      554456 non-null  object \n",
      "dtypes: float64(3), object(7)\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": {
    "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",
       "RideCategory        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# investigate and find Duplicated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['distance', 'cab_type', 'time_stamp', 'destination', 'source',\n",
       "       'surge_multiplier', 'id', 'product_id', 'name', 'RideCategory'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "surge_multiplier    8.312215\n",
       "distance            0.836062\n",
       "time_stamp          0.431423\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.skew().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# convert data to be normal distribution one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taxi['distance']=taxi[\"distance\"].apply(lambda x:np.log(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12abb914430>"
      ]
     },
     "execution_count": 12,
     "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": [
    "taxi[[\"distance\",\"surge_multiplier\"]]=taxi[[\"distance\",\"surge_multiplier\"]].apply(lambda i: np.sqrt(i))\n",
    "\n",
    "s=sns.distplot(taxi['distance'],label=\"skewness :%.2f\"%(taxi['distance'].skew()))\n",
    "s.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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"
    }
   ],
   "source": [
    "sns.boxplot(x=taxi['distance'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk4AAAJPCAYAAACKBVtQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAa+ElEQVR4nO3de5DVZf3A8Q8CQhmBd51Sp8byUk3e0rRaFFNxuazgjbyBgAiZU+rgbSwvPy20m844Uio5appRCWZZ1sDIL9draImXEfACKiIGsiK4XHaf3x/+dgdkgQ+2N3Zfrxln3LPn+z3P8/j1OW/OnuV0KaWUAABgk7Zq6wEAAGwphBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCApG6busM77yyP+vr291c9bb/9J2Lx4vfaehhtprPPP8IaRFiDzj7/CGsQYQ06+/wjPliDd95ZHttuu02LP9Ymw6m+vrTLcIqIdjuu1tLZ5x9hDSKsQWeff4Q1iLAGnX3+Ea23Bn5UBwCQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJK6tfUAOqu7774jXntt3kc+vnv3rrF6dV0zjqj9q6lZGhERvXv3iYjOuQYf1tnXoLPPP8IaRDT/Guy22x5xyilnNNv56FiEUxt57bV58eKcudG1Z5+2HsoWo652aUREvP3umrYdCNBhNewzsCHCqQ117dknPr7HkW09jC3GinnTIiKsGdBiGvYZ2BDvcQIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAASW0eTtXV/xvV1f/b1sMAAFpIR3qu79bWA3j44RkREfG1r1W08UgAgJbQkZ7r2/wVJwCALYVwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASOrW1gOoqVkaNTU1ce21/7NZx3Xv3jVWr65roVG1vPnz50V9Xde2HgYAa6lfUxvz58/b7OektrQlPB/Onz8vevfu3dbDaBZecQIASGrzV5x69+4TvXv3iYsu+v5mHbfjjr3i7beXtdCoWt611/5PzH3tP209DADWslW3nrH7bjts9nNSW9oSng+3pFfwNsUrTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACR1a+sBfP3rfdt6CABAC+pIz/VtHk5f+1pFWw8BAGhBHem53o/qAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAEBSt7YeQGdWV7s0Vsyb1tbD2GLU1S6NiLBmQIv5YJ/Zoa2HQTsmnNrIbrvt8V8d371711i9uq6ZRrNlqKn54HLt3btPRHTONfiwzr4GnX3+EdYgornXYIf/en+mYxNObeSUU874r47fccde8fbby5ppNFsma2ANOvv8I6xBhDWgdXmPEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJAknAIAk4QQAkCScAACShBMAQJJwAgBIEk4AAEnCCQAgSTgBACQJJwCAJOEEAJAknAAAkoQTAECScAIASBJOAABJwgkAIEk4AQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAASd02dYetturSGuP4SNrz2FpDZ59/hDWIsAadff4R1iDCGnT2+Ue03hp0KaWUVnkkAIAtnB/VAQAkCScAgCThBACQJJwAAJKEEwBAknACAEgSTgAAScIJACBJOAEAJLV6OL333nsxcODAeP3119f73o033hhHHHFEVFVVRVVVVdx1113rfP/555+PL37xi41fz507N4YNGxaDBw+O008/Pd54442IiFi0aFGMGjUqqqqqYsiQIfHoo49GRMTq1avjgAMOaDx/VVVV1NXVteBs19ca83/jjTdi//33bzzPqFGjIiKilBLXXntt9O/fPyorK2PmzJktONMNa401GDt2bOM5Bg0aFHvttVfMmjWrXVwDEc27Bg0WLlwYBx98cOM5V61aFePHj49jjz02hgwZEi+99FJEdMzroMGH16Cz7AUNPjz/zrQXNPjwGnSmveCJJ56IQw45pPH+l1xySUS0772gNebf7PtAaUX/+te/ysCBA8sXvvCF8tprr633/bPPPrs89dRTTR67YsWKMmzYsPL5z3++8bbTTjutzJgxo5RSyt13313OP//8UkopF1xwQfn1r39dSinlpZdeKocddlhZs2ZNmTVrVhk5cmRzTyutteb/17/+tXz/+99f7xx/+ctfyllnnVXq6urKyy+/XI466qiyevXq5phaWmutwdquv/76ctlll5VSSptfA6U0/xqUUkpdXV0ZOXJk2W+//RrPeeuttzZeB0888UQ58cQTSykd8zoopek16Cx7QSlNz78z7QWlNL0Ga+voe8GkSZPKL37xi/Xu2173gtaaf3PvA636itPkyZPj8ssvj5122qnJ7z/77LPxy1/+MgYNGhRXXXVVrFy5svF7EyZMiOHDh69z/9tuuy0qKiqivr4+FixYEJ/85CcjIuKoo46KgQMHRkTEHnvsEStXrowVK1bErFmzYsmSJTF06NA46aST4oknnmihmTatteY/a9asmD17dlRVVcUZZ5wRL774YkREzJgxIyorK2OrrbaKz3zmM7HrrrvG008/3UKzbVprrUGDl19+OaZOnRoXXXRRRESbXwMRzb8GERG33nprHHbYYbHttts23vbQQw/F4MGDIyLiK1/5SixZsiQWLFjQIa+DiKbXoLPsBRFNz78z7QURTa9Bg86wF8yaNSsefvjhGDRoUIwdOzbefPPNiGi/e0Frzb+594FWDadrrrkmDjrooCa/t3z58thnn31i/PjxMWXKlHj33XfjpptuioiIadOmRW1tbfTv33+dY7p16xbvvvtuVFRUxG9+85s46aSTIiLimGOOid69e0dExKRJk2KfffaJXr16RZcuXeLII4+M3/72t3HFFVfEeeedF0uWLGnBGa+rtebfo0ePGDx4cEyZMiVGjRoV55xzTqxatSoWLVq0zgW64447xsKFC1totk1rrTVocNNNN8WoUaPiE5/4REREm18DEc2/Bs8++2w89thjceaZZ65z+6JFi2LHHXds/Lrhv3dHvA42tAadZS/Y0Pw7016woTVo0Bn2gl69esXpp58e999/f/Tt2zfOO++8iGi/e0Frzb/Z94HNfo2qGRxxxBFNviy3tueee65UVVWVRYsWleOPP74sW7aslFLWe2m2wYwZM0rfvn3LmjVrGm+77bbbSr9+/cqCBQuaPGbs2LHl73//+0ecxUfXWvNvMGjQoPLCCy+UESNGlEceeaTx9vPPP7/86U9/+i9m8tG1xhosXbq0fPWrXy21tbUbfIy2ugZKaZ41WLFiRTnhhBPKG2+8sd45jzrqqDJv3rzGc5188snl6aef7nDXwcbWoEFH3gsy82/QUfeCTa1BZ9gLmnLggQeWd999t93vBS09/wbNtQ+0m9+qW7BgQfz+979v/LqUEt26dYuHHnooli5dGqeeempUVVVFRERVVVW899578cADD0QpJSIiKioqora2NmpqaiIi4rrrrovf/e53cdddd8Wuu+4aERFTp06N+fPnr/MY3bt3b60pblRzzv/OO++Md955Z71z7bLLLrFo0aLG2//zn/9s8CXSttDc18CMGTOioqIievTo0XjO9nwNRGz+GlRXV8fixYtj3LhxUVVVFYsWLYoxY8bEyy+/HDvvvHOT/7072nWwsTWI6Ph7wcbm31n2gk1dA51hL1i2bFlMnDhxvTc3d+3adYvcC5pz/hHNvA9sMq1aQFN1uXjx4nLwwQeX+fPnl/r6+nLJJZc0+Savtevy+OOPLw8++GAppZRHH3209O/fv5TyQVUOHTq01NTUrHPshAkTyuWXX15K+eANYhUVFY3V2ppaev7nnntuufnmm0sppTz++OOlsrKy1NXVlQceeKCMGjWqrFmzprz66quloqKivP/++y01zY1q6TUopZQrr7yyTJ48eZ1j28s1UErzrcGGznnLLbc0vhH2ySefLJWVlaWU0iGvgw2ds7PsBRs6Z2faCzZ2zs6yFwwZMqT8+c9/LqWUMmXKlMY3Prf3vaCl59/c+0Cbh9Po0aPLM888U0r54DdABgwYUI4++uhy8cUXl5UrV6537NqLNGfOnDJs2LAyePDgcuqpp5bZs2eX+vr6ctBBB5XDDz+8DB48uPGfhQsXlmXLlpVzzz23DBgwoAwcOLA8+uijrTPhD2nJ+ZdSysKFC8uIESPKgAEDytChQ8sLL7xQSimlvr6+TJgwoVRWVpbKysryj3/8o6WnukEtvQYN5234jbsG7eUaKKX51mBD56ytrS0XXnhhqaysLMcdd1x59tlnSykd8zpo6pydaS/Y0Dk7016woXM2nLcz7AWzZ88uJ598cqmsrCynnXZa44+k2vte0JLzb4l9oEsp//9zDgAANqrdvMcJAKC9E04AAEnCCQAgSTgBACQJJwCgVW3sw32bcuGFF8a9997b+PXMmTPjhBNOiKqqqhg+fHjjB7y3BuEEndjIkSNjyZIlcdZZZ8XcuXPbbBwNfzEd0PH9+9//jm9961vx6quvbvK+b731VowdOzYefPDBdW4fP358XH311XHffffFoEGD4uqrr26h0a5POEEnVl1dHRERt9xyS+y5555tNo6ZM2dGbW1tmz0+0Hqa+nDfqVOnxpAhQ6KqqiouvfTSxg/0vf/+++PII4+MY489tvG+q1atiu9+97ux9957R0TEXnvt1fiBvq2hW6s9EtCuXHLJJRERMXz48Jg7d25Mnjw5VqxYET/72c9i1113jVdeeSU+9rGPxZgxY+LOO++MV155JY4++ui49NJLIyJi+vTpMXHixFi9enX07NkzLrrooth///03+ph333133HPPPdG9e/fo0aNHXHXVVfHKK6/E9OnTo7q6Onr27BnHHHNM/OAHP4jFixfH22+/HZ/61Kfi+uuvj+233z769esXAwcOjMceeyxqampi9OjR8dRTT8Vzzz0X3bp1i4kTJ8bOO+8c/fr1iwEDBkR1dXUsW7YszjzzzDjllFNafE2BTbvmmmvW+XrOnDkxefLkuOeee6JHjx7x05/+NCZNmhTf/va3Y/To0RHxwR+uGmy99daNH7dSX18fN954Y3zzm99stfELJ+ikfvSjH8W9994bt99+e5xwwgmNt8+aNSsuv/zy2HfffWP06NFx8803xx133BHvvfdeVFRUxKhRo+L999+Pn//853HHHXfEtttuG3PmzIkzzzwz/va3v8XHP/7xJh+vrq4ufvjDH8b06dNjp512iqlTp8bMmTPj5JNPjmnTpsXnPve5OPXUU+P222+P/fbbL8aMGROllBgzZkzcd999MXLkyIiIWLlyZUyePDkeeOCBuOCCC2LKlCmx9957xznnnBNTpkyJsWPHRkRETU1N/OEPf4i33norjjvuuDjwwANjr732avmFBTbL448/HvPmzYuTTjopIiJWr14d++677yaPW7VqVVx88cWxZs2aOPvss1t6mI2EE7COT3/6042b1u677x69evWKrbfeOrbbbrvYZpttoqamJp588slYtGhRjBgxovG4Ll26xPz58xtfPv+wrl27Rv/+/WPYsGFx+OGHx9e//vXo27fvevcbPnx4/POf/4zbbrstXn311ZgzZ058+ctfbvz+0UcfHRERu+22W+ywww6Nj7f77rs3fsBzRMQpp5wSXbp0iV122SW+8Y1vRHV1tXCCdqiuri6OPfbYuOyyyyIiYvny5et9WO+HLV++PMaNGxd9+vSJiRMntuoHNAsnYB1bb731Ol9367b+NlFfXx+HHnpoXH/99Y23vfnmm5v8ZPWf/OQnMXv27HjkkUfi5ptvjvvuuy9uuOGGde7z4x//OJ555pk4/vjj45BDDok1a9bE2p8Mtfb4NrZZrj3u+vr62Gorb+mE9uiQQw6JX/3qVzFu3LjYbrvt4oorrojdd989zj333A0eM378+Nhjjz3iyiuvbPX/t+0k0Il17do11qxZs9nHHXrooVFdXR0vvfRSRETMmDEjBg8evNE3eC9ZsiT69u0bffr0iREjRsT3vve9mDVr1nrjePjhh2P48OFx3HHHxfbbbx+PPPLIJv/02ZSpU6dGRMSCBQuiuro6KioqNvscQMvbe++94zvf+U4MHz48BgwYEPX19TFmzJgN3v/555+PadOmxVNPPdX4hvKzzjqr1cbrFSfoxPr37x+nn356LF++fLOO23PPPeOqq66K888/P0opjW/M3mabbTZ4zHbbbRfjxo2LESNGRM+ePaNr166Nv0JcUVEREyZMiIiIc845J6677rq44YYbonv37nHAAQfE/PnzN3tur7/+egwdOjRqa2vjsssui89+9rObfQ6g5UyfPr3x30888cQ48cQTN3jfhv0hImLfffeNF198sUXHtjFdytqvgQN0AP369YsbbrghvvSlL7X1UIAOxitOQLP54x//GJMmTWrye4MGDWr81WKALZVXnAAAkrw5HAAgSTgBACQJJwCAJOEEAJAknAAAkoQTAEDS/wHnmZQB0WsAOwAAAABJRU5ErkJggg==\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": "markdown",
   "metadata": {},
   "source": [
    "# remove_outliers using `IQR` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 removed outliers in {} is : {}'.format(col,len(indecies)))\n",
    "        df.drop(indecies,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of removed outliers in distance is : 2374\n",
      "number of removed outliers in surge_multiplier is : 16851\n"
     ]
    }
   ],
   "source": [
    "columns={'distance','surge_multiplier'}\n",
    "remove_outliers(taxi,columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(535231, 10)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</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>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</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>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</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>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</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>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</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>cheap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type    time_stamp         destination  \\\n",
       "0  0.787401     Uber  1.544770e+12            West End   \n",
       "1  1.506652     Uber  1.543400e+12   Boston University   \n",
       "2  1.414214     Lyft  1.544920e+12            Back Bay   \n",
       "3  1.994994     Lyft  1.543620e+12  Financial District   \n",
       "4  1.220656     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 RideCategory  \n",
       "0      Taxi      unknown  \n",
       "1  UberPool        cheap  \n",
       "2      Lyft     moderate  \n",
       "3   Lyft XL    expensive  \n",
       "4      Lyft        cheap  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.head()"
   ]
  },
  {
   "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": 20,
   "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>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</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>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</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>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</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>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</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>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</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>cheap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type    time_stamp         destination  \\\n",
       "0  0.787401     Uber  1.544770e+12            West End   \n",
       "1  1.506652     Uber  1.543400e+12   Boston University   \n",
       "2  1.414214     Lyft  1.544920e+12            Back Bay   \n",
       "3  1.994994     Lyft  1.543620e+12  Financial District   \n",
       "4  1.220656     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",
       "                             product_id      name RideCategory  \n",
       "0  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a      Taxi      unknown  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2  UberPool        cheap  \n",
       "2                                  lyft      Lyft     moderate  \n",
       "3                             lyft_plus   Lyft XL    expensive  \n",
       "4                                  lyft      Lyft        cheap  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.drop(['id'],axis=1,inplace=True)\n",
    "taxi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Financial District         45168\n",
       "West End                   44818\n",
       "Back Bay                   44748\n",
       "South Station              44739\n",
       "Fenway                     44730\n",
       "Beacon Hill                44625\n",
       "North End                  44621\n",
       "Haymarket Square           44620\n",
       "North Station              44532\n",
       "Theatre District           44522\n",
       "Boston University          44478\n",
       "Northeastern University    43630\n",
       "Name: destination, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North End                  46040\n",
       "Haymarket Square           45675\n",
       "North Station              45291\n",
       "West End                   45121\n",
       "South Station              44907\n",
       "Financial District         44638\n",
       "Theatre District           44238\n",
       "Beacon Hill                44093\n",
       "Boston University          44070\n",
       "Fenway                     43957\n",
       "Northeastern University    43764\n",
       "Back Bay                   43437\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UberX           43876\n",
       "Taxi            43799\n",
       "UberPool        43761\n",
       "Black SUV       43690\n",
       "WAV             43681\n",
       "Black           43646\n",
       "UberXL          43642\n",
       "Shared          40841\n",
       "Lyft XL         37748\n",
       "Lux Black       37695\n",
       "Lyft            37639\n",
       "Lux             37616\n",
       "Lux Black XL    37597\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55c66225-fbe7-4fd5-9072-eab1ece5e23e    43876\n",
       "8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a    43799\n",
       "997acbb5-e102-41e1-b155-9df7de0a73f2    43761\n",
       "6d318bcc-22a3-4af6-bddd-b409bfce1546    43690\n",
       "9a0e7b09-b92b-4c41-9779-2ad22b4d779d    43681\n",
       "6c84fd89-3f11-4782-9b50-97c468b19529    43646\n",
       "6f72dfc5-27f1-42e8-84db-ccc7a75f6969    43642\n",
       "lyft_line                               40841\n",
       "lyft_plus                               37748\n",
       "lyft_lux                                37695\n",
       "lyft                                    37639\n",
       "lyft_premier                            37616\n",
       "lyft_luxsuv                             37597\n",
       "Name: product_id, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['product_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "moderate          173014\n",
       "cheap             150920\n",
       "expensive         123318\n",
       "very expensive     44180\n",
       "unknown            43799\n",
       "Name: RideCategory, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['RideCategory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</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>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</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>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</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>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</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>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</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>cheap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type    time_stamp         destination  \\\n",
       "0  0.787401     Uber  1.544770e+12            West End   \n",
       "1  1.506652     Uber  1.543400e+12   Boston University   \n",
       "2  1.414214     Lyft  1.544920e+12            Back Bay   \n",
       "3  1.994994     Lyft  1.543620e+12  Financial District   \n",
       "4  1.220656     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",
       "                             product_id      name RideCategory  \n",
       "0  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a      Taxi      unknown  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2  UberPool        cheap  \n",
       "2                                  lyft      Lyft     moderate  \n",
       "3                             lyft_plus   Lyft XL    expensive  \n",
       "4                                  lyft      Lyft        cheap  "
      ]
     },
     "execution_count": 26,
     "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": "code",
   "execution_count": 27,
   "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>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</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>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</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>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</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>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</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>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</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>cheap</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>1.280625</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>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554452</th>\n",
       "      <td>0.959166</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>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554453</th>\n",
       "      <td>1.630951</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>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554454</th>\n",
       "      <td>1.726268</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>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554455</th>\n",
       "      <td>1.382027</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>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>535231 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance cab_type    time_stamp         destination  \\\n",
       "0       0.787401     Uber  1.544770e+12            West End   \n",
       "1       1.506652     Uber  1.543400e+12   Boston University   \n",
       "2       1.414214     Lyft  1.544920e+12            Back Bay   \n",
       "3       1.994994     Lyft  1.543620e+12  Financial District   \n",
       "4       1.220656     Lyft  1.543580e+12            Back Bay   \n",
       "...          ...      ...           ...                 ...   \n",
       "554451  1.280625     Uber  1.543620e+12            Back Bay   \n",
       "554452  0.959166     Uber  1.543550e+12       North Station   \n",
       "554453  1.630951     Uber  1.543530e+12    Theatre District   \n",
       "554454  1.726268     Uber  1.543650e+12    Theatre District   \n",
       "554455  1.382027     Uber  1.543620e+12    Theatre District   \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",
       "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 RideCategory  \n",
       "0       8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a       Taxi      unknown  \n",
       "1       997acbb5-e102-41e1-b155-9df7de0a73f2   UberPool        cheap  \n",
       "2                                       lyft       Lyft     moderate  \n",
       "3                                  lyft_plus    Lyft XL    expensive  \n",
       "4                                       lyft       Lyft        cheap  \n",
       "...                                      ...        ...          ...  \n",
       "554451  6f72dfc5-27f1-42e8-84db-ccc7a75f6969     UberXL     moderate  \n",
       "554452  6d318bcc-22a3-4af6-bddd-b409bfce1546  Black SUV    expensive  \n",
       "554453  6f72dfc5-27f1-42e8-84db-ccc7a75f6969     UberXL     moderate  \n",
       "554454  55c66225-fbe7-4fd5-9072-eab1ece5e23e      UberX     moderate  \n",
       "554455  6f72dfc5-27f1-42e8-84db-ccc7a75f6969     UberXL     moderate  \n",
       "\n",
       "[535231 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# now let's move to work on weather data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather=pd.read_csv(r'F:\\mohamed\\3rd year\\semester 2\\machine learning\\project\\milestone 2\\Milestone 2 Datasets\\taxi\\weather.csv')"
   ]
  },
  {
   "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(x=weather['temp'])\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['clouds'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAAJPCAYAAAB4uQ71AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVsElEQVR4nO3da4zW9Z338e8wg1CXFUQOrZU1jaTQ0lpaeNKIAj0IKqORpodIakhjpGCbmrRWrBqsSKOEUm1ITGnaB7fVYg8EI21oyhK7URJCTG1rPPTetOGk4GiFHViOw/9+YDr3uusoXWEu58Pr9Yj5zzVz/WY+mZl3rmvCtDVN0xQAQLBBrT4AAMCpJngAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCI1/FWN3j11QN1/PjA+a96zjlnWL3yyv5WH4MTZK+BxV4Di70GFnu9PYMGtdXZZ/9Tn69/y+A5frwZUMFTVQPuvKc7ew0s9hpY7DWw2OvU8ZQWABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8QQPABBP8AAA8TpafQDemR566P/Ujh3bTvn9DB7cXkeP9pzy+0m3b9/eqqoaPnzEKb0fe/WvcePOr2uuubbVx4AIgoc3tGPHtnr+//57tQ8d0eqjcAJ6Du2tqqqu/zjW2oNw0vx9U+DkEDz0qX3oiDrz/E+2+hicgP/c9q9VVfYK8vdNgZPD7/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQr+XB88QT/1ZPPPFvrT4GAHCKvBN+1ne09N6r6vHHf1dVVRdddEmLTwIAnArvhJ/1LX+EBwDgVBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxOto9QH27dtb+/btq3vuWXpS3t/gwe119GjPSXlfp7Pt27fV8Z72Vh8DTlvHjx2q7du3va3vjb4fDizJe23fvq2GDx/e0jN4hAcAiNfyR3iGDx9Rw4ePqJtvvv2kvL/Ro/+5urq6T8r7Op3dc8/S+vcdL7f6GHDaGtQxtP5l3Ki39b3R98OBJXmvk/UsztvhER4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDidbT6ANOmTW/1EQCAU+id8LO+5cFz0UWXtPoIAMAp9E74We8pLQAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOJ1tPoAvHP1HNpb/7ntX1t9DE5Az6G9VVX2CvLapqNafQyIIXh4Q+PGnd8v9zN4cHsdPdrTL/eVbN++176Uhw8fcUrvx179aVS/fR3C6UDw8Iauuebafrmf0aP/ubq6uvvlvnj77AUMVH6HBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCI1/FWNxg0qK0/znFSDcQzn87sNbDYa2Cx18Bir/+9t/rctTVN0/TTWQAAWsJTWgBAPMEDAMQTPABAPMEDAMQTPABAPMEDAMQTPABAPMEDAMQTPABAvAEXPKtXr65Zs2ZVZ2dn3X///VVVtXbt2rr88surs7Oz7rrrrjp27FhVVT377LM1d+7cmjVrVt1666291zn19u/fX3PmzKmdO3dWVdXmzZurs7OzLr300vre977Xe7u+NnrhhRdq3rx5NXv27Fq4cGEdOHCgJR/H6eJE9/q7b37zm7V27drel+3Vv050r40bN9ZVV11VV155ZS1atKj27dtXVfbqbye6129/+9vq7OysK664ohYvXlxHjhypKnudNM0A8sQTTzRz5sxpuru7m2PHjjULFixofvCDHzQXX3xxs2fPnqZpmmbJkiXNj3/846ZpmuaKK65ofv/73zdN0zS33HJL8+CDD7bq6KeVp556qpkzZ04zadKkZseOHc3Bgweb6dOnN9u3b2+OHj3afOlLX2oee+yxpmn63uj6669v1q9f3zRN06xatapZvnx5Sz6W08E/stfu3bubBQsWNBdeeGHzy1/+svd92Kv/nOhe3d3dzUUXXdTs3r27aZqmuffee5ulS5c2TWOv/nSiex04cKCZNm1a09XV1TRN09x4443NmjVrmqax18kyoB7heeaZZ2ratGk1bNiwam9vr4svvrhWrVpVkydPrjFjxlRV1cyZM2vjxo21a9euOnToUE2ePLmqqubOnVsbNmxo4elPHz/72c9qyZIlvZv88Y9/rPPPP7/GjRtXHR0d1dnZWRs2bOhzo6NHj9bWrVtr1qxZr7vOqXGie1VVPfroo/XJT36yLrvsst63t1f/OtG9jh49WkuWLKmxY8dWVdWECRPqxRdftFc/O9G9zjzzzNq0aVONGjWqDh48WK+88kqdddZZ9jqJBlTwTJo0qR5//PHau3dvHT58uDZt2lSTJ0+uP/zhD/Xiiy9WT09PbdiwoV5++eV66aWXavTo0b1vO3r06NqzZ08LT3/6WLZsWU2dOrX35f++xZgxY2rPnj19bvTqq6/WsGHDqqOj43XXOTVOdK+qquuuu64++9nPvu7t7dW/TnSvs88+uz796U9XVdWhQ4dq9erV9alPfcpe/ewf+foaPHhw/e53v6sZM2bUq6++WtOmTbPXSTSggufjH/94zZ07t774xS/WddddV1OmTKl3vetd9fWvf70WLlxY8+bNqwkTJtTgwYPr+PHj1db2//9UfNM0r3uZ/tPXFn1df6OtbNd//tGvHXu11lvt1d3dXddff31NnDixrr76anu12FvtNX369NqyZUvNnDmz7rjjDnudRAMqePbv31+XXnppPfroo/XAAw/UGWecUWPHjq0LL7yw1q1bV2vWrKmxY8fWuHHj6t3vfnd1dXX1vu3LL7/c+5Ai/eu/b9HV1VVjxozpc6ORI0dWd3d39fT0vO729I++9uqLvVrrzfZ66aWX6pprrqkJEybUsmXLqsperdbXXnv37q3HH3+893pnZ2c9//zz9jqJBlTw7Ny5sxYtWlTHjh2r7u7u+sUvflFXXXVVzZ8/v/bv319Hjhypn/zkJ3X55ZfXe9/73hoyZEg9+eSTVVX1yCOP1CWXXNLij+D09JGPfKT++te/1rZt26qnp6fWr19fl1xySZ8bDR48uKZOnVq//vWvq6pq3bp1tutHfe3VF3u1Vl979fT01Je//OW67LLL6tZbb+19VMBerdXXXk3T1E033VQvvPBCVVVt2LChPvaxj9nrJOpo9QH+ERMnTqxLL720rrzyyurp6an58+fXlClT6oYbbqjPf/7zdezYsZozZ051dnZWVdWKFSvqtttuq/3799ekSZPq2muvbfFHcHoaMmRI3X333fXVr361Dh8+XNOnT6/Zs2dXVd8bLVmypBYvXlz3339/vec976mVK1e28kM4rbzZXn2xV+v0tdfGjRvrmWeeqZ6envrNb35TVVUf+tCHatmyZfZqob72amtrq6VLl9aCBQuqra2txo8fX9/+9rerytfXydLWNE3T6kMAAJxKA+opLQCA/w3BAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQLwB9R8PAv1ry5YttWLFijr33HPrL3/5Sw0dOrTuvvvu+uEPf1h79+6tHTt21IwZM+prX/tarVixorZu3Vo9PT31wQ9+sG677bYaNmxYPfTQQ7VmzZoaPHhwDRkypO68884aP358n9c/8YlP1H333Vcf/vCHq6p6Xz777LNr3rx5dcEFF9SuXbvqgQceqJ07d9aKFSvq4MGDNWjQoPrKV75SM2fObPFnDXgnEjzAm3r66afr5ptvrqlTp9ZPf/rTuummm+r9739/HTp0qH71q19VVdWqVauqvb291q5dW21tbbVy5cpasWJF3X777fWd73ynNm3aVGPGjKl169bVk08+We973/ve8Pr48ePf9Cy7d++u7373uzV16tTat29f3XLLLfWjH/2ozjvvvNqzZ0997nOfqwkTJtS5557bH58aYAARPMCbmjhxYk2dOrWqqj7zmc/UnXfeWWPGjKkpU6b03uaxxx6r7u7u2rx5c1VVHT16tM4555xqb2+v2bNn1xe+8IWaMWNGTZs2raZPn97n9bfS0dFRkydPrqqqp556qrq6uuqGG27ofX1bW1s9//zzggf4HwQP8Kba29v/x7VBgwbVmWee2fvy8ePH61vf+lZvtBw4cKAOHz5cVa/9vbQ///nPtXnz5lq9enU98sgjdd999/V5varqv/7FmyNHjvT++4wzzqiOjte+bfX09NQFF1xQP//5z3tfv2fPnho5cuRJ/OiBFH5pGXhTzz33XD333HNVVfXwww/XRz/60TrrrLNed5tp06bVgw8+WEeOHKnjx4/X7bffXitXrqy//e1vNX369BoxYkTNnz+/brzxxvrTn/7U5/WqqpEjR9bTTz9dVa/9DlFXV9cbnmvy5Mm1bdu22rp1a1VVPfvsszVr1qzas2fPqfpUAAOYR3iANzVq1Ki69957a9euXTVy5Mhavnx5rVq16nW3WbRoUd1zzz119dVXV09PT33gAx+oxYsX17Bhw2rhwoU1f/78Gjp0aLW3t9ddd91VI0eOfMPrVVXf+MY36o477qiHH364Jk2aVJMmTXrDc40cObK+//3v1/Lly+vw4cPVNE0tX768zjvvvFP+OQEGHn8tHejTli1baunSpbV+/fpWHwXgbfGUFgAQzyM8AEA8j/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQ7/8BpfBrIa4+6vQAAAAASUVORK5CYII=\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": 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['humidity'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAAJPCAYAAAB4uQ71AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWrElEQVR4nO3df6zWdf3/8SfyQyMdyKdDOkewXD+cs3K6RWAyJH4I54Q/2iKbZFQ6c81caZr7DEfLH2QrTdpake4zMSU3KO2XjRb6QbdEs6w185MKGIbgDwSEwxHe3z/8ej4dDif9JJwLHud2++867/eb63U99zrXuXNdcK5BTdM0BQAQ7JBWLwAAYH8TPABAPMEDAMQTPABAPMEDAMQTPABAPMEDAMQb8nonvPDCttq9+9//VT3/8R+H13PPbf23r09mNn0zm76ZTd/Mpm9m0zez6dvBNJtDDhlURx751j6Pv27w7N7dvKngee3PYO/Mpm9m0zez6ZvZ9M1s+mY2fUuZjbe0AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiDek1Qug/9x223/VunVrWr2MN2To0MHV1bWr1ct4wzZvfrGqqkaMGLnf7+tgm01/GDNmbJ1zztxWLwM4gAmeAWTdujX12OP/U4MPG9nqpcTZtePFqqra+NIrrV3IAPTa7AH+FcEzwAw+bGQNHzul1cuI8/KaFVVVZtsCr80e4F/xb3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3gAgHiCBwCI1/LgWbXq3lq16t5WLwMA2E8OhJ/1Q1p671X13/+9sqqqJk48tcUrAQD2hwPhZ33LX+EBANjfBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxhrR6AZs3v1ibN2+u6677WquX0u+GDh1cXV27+u3+1q5dU7t3De63+4P+sPuVHbV27Zq67rqv9fv31MHEbPpmNn3bV7NZu3ZNjRgxYh+s6N/nFR4AIF7LX+EZMWJkjRgxsr7ylf9s9VL6XVvbEbVx45Z+u7/rrvta/c+6Tf12f9AfDhlyWL1jzNvqK1/5z37/njqYmE3fzKZv+2o2B8K7OF7hAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIN6QVi/glFMmtXoJAMB+dCD8rG958EyceGqrlwAA7EcHws96b2kBAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQb0irF0D/2rXjxXp5zYpWLyPOrh0vVlWZbQu8Ovu3tXoZwAFO8AwgY8aMbfUS3rChQwdXV9euVi/jDdu8+dVvpREjRu73+zrYZrP/ve2g2ttAawieAeScc+a2eglvWFvbEbVx45ZWL+OAZDYA/3f+DQ8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEG/I651wyCGD3vSd7Is/I5XZ9M1s+mY2fTObvplN38ymbwfLbF5vnYOapmn6aS0AAC3hLS0AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIN4+C5677rqrZs6cWdOmTaslS5b0Ov6Xv/ylzjrrrJo+fXpdeeWV9corr+yruz7g3XTTTTVr1qyaNWtWLVy4cK/HJ0+eXLNnz67Zs2fvdX6pzj333Jo1a1b3Y//DH/7Q4/hA3Tc//vGPu2cye/bsOumkk2rBggU9zhlo+2br1q3V3t5eTz/9dFVV3X///dXR0VHTpk2rb33rW3u9Zv369fXJT36yZsyYURdeeGFt27atP5fcb/aczR133FHt7e3V0dFRV1xxRe3cubPXNcuWLatTTjmle//0NcOD3Z6zueKKK2ratGndj/vXv/51r2sG4r5ZuXJlj+ec8ePH1wUXXNDrmoN63zT7wD/+8Y9m8uTJzQsvvNBs27at6ejoaB5//PEe58yaNav5/e9/3zRN01xxxRXNkiVL9sVdH/BWrVrVfPzjH286OzubnTt3NnPnzm3uueeeHudccMEFzcMPP9yiFbbO7t27m1NOOaXp6urq85yBum/+2V//+tdm6tSpzXPPPdfj6wNp3zzyyCNNe3t7c/zxxzfr1q1rtm/f3kyaNKlZu3Zt09XV1cybN6/57W9/2+u6888/v7n77rubpmmam266qVm4cGF/L32/23M2TzzxRDN16tRmy5Ytze7du5vLLrusufnmm3tdt2DBguauu+7q/wX3oz1n0zRN097e3mzYsOFfXjcQ980/e/bZZ5spU6Y0Tz75ZK/rDuZ9s09e4bn//vtr/PjxNXLkyBo+fHhNnz69fvnLX3Yf//vf/147duyoD3zgA1VVddZZZ/U4nqytra0uv/zyGjZsWA0dOrSOPfbYWr9+fY9z/vSnP9X3vve96ujoqAULFlRnZ2eLVtu/nnjiiaqqmjdvXn30ox+tW2+9tcfxgbxv/tlVV11Vl1xySY0aNarH1wfSvlm6dGnNnz+/Ro8eXVVVf/zjH2vs2LE1ZsyYGjJkSHV0dPTaG11dXfXggw/W9OnTqyp3/+w5m2HDhtX8+fPr8MMPr0GDBtW73/3uXs85VVWPPvpoLVu2rDo6OurLX/5ybd68ub+Xvt/tOZvt27fX+vXr66tf/Wp1dHTUjTfeWLt37+5xzUDdN/9s4cKFNWfOnBo3blyvYwfzvtknwfPss89WW1tb9+3Ro0fXhg0b+jze1tbW43iyd73rXd0/sJ966qn6xS9+UZMmTeo+vm3btjruuOPq0ksvrWXLltVLL71U3/3ud1u02v710ksv1Yc+9KFatGhR3XLLLXX77bfXqlWruo8P5H3zmvvvv7927NhRp59+eo+vD7R98/Wvf71OPvnk7tuv95xTVfXCCy/U4YcfXkOGDKmq3P2z52yOOeaYmjhxYlVVPf/887VkyZKaMmVKr+va2trq85//fP30pz+to48+utdbpgn2nM2mTZtq/PjxdfXVV9fSpUtr9erVdeedd/a4ZqDum9c89dRT9bvf/a7mzp271+sO5n2zT4Jn9+7dNWjQ/34se9M0PW6/3vGB4PHHH6958+bVZZdd1qOa3/rWt9b3v//9OvbYY2vIkCE1b968WrlyZesW2o9OPPHEWrhwYR1xxBE1atSo+tjHPtbjsds3Vbfffnt9+tOf7vX1gbxvqt7Y3tjb1wbS/tmwYUN96lOfqrPPPrs++MEP9jq+aNGiOumkk2rQoEH12c9+tu67774WrLJ/jRkzphYtWlSjR4+ut7zlLXXuuef2+r4Z6PvmjjvuqHPOOaeGDRu21+MH877ZJ8Fz1FFH1caNG7tvb9y4scfLZHse37Rp015fRkv10EMP1XnnnVdf+tKX6swzz+xxbP369T3+htE0TfffLNKtXr26Hnjgge7bez72gb5vdu7cWQ8++GCddtppvY4N5H1T9frPOVVVo0aNqi1bttSuXbv6PCfV3/72t5ozZ06deeaZddFFF/U6vmXLlrrlllu6bzdNU4MHD+7HFbbGY489Vr/61a+6b+/t+2Yg75uqqhUrVtTMmTP3euxg3zf7JHgmTJhQDzzwQD3//PO1ffv2uueee+rUU0/tPn7MMcfUoYceWg899FBVVf3kJz/pcTzZM888UxdddFFdf/31NWvWrF7HDzvssPrGN75R69atq6ZpasmSJTV16tQWrLT/bdmypRYuXFidnZ21devWWrZsWY/HPpD3TdWrT87jxo2r4cOH9zo2kPdNVdX73//+evLJJ2vNmjW1a9euuvvuu3vtjaFDh9bJJ59cP//5z6uqavny5QNi/2zdurU+85nP1MUXX1zz5s3b6znDhw+vH/zgB93/K/LWW28dEPunaZq6+uqra/PmzdXV1VV33HFHr8c9UPdN1atvge7YsaPGjBmz1+MH+77ZJ8Hz9re/vS655JKaO3dunXHGGdXe3l7ve9/76nOf+1w9+uijVVV1/fXX1zXXXFMzZsyol19+uc/3B9MsXry4Ojs769prr+3+b3w/+tGPumczatSoWrBgQV144YU1Y8aMappmr29hJJo8eXJNmjSpzjjjjDr77LPr7LPPrhNPPNG++f/WrVtXRx11VI+v2TevOvTQQ+vaa6+tL3zhCzVz5sx65zvfWTNmzKiqqiuvvLJWrFhRVVXz58+vpUuX1syZM2v16tX1xS9+sYWr7h933nlnbdq0qW6++ebu55wbbrihqv53NoMHD65vf/vbddVVV9Xpp59ef/7zn+vSSy9t8cr3v/e+9711/vnn1yc+8YmaNWtWHXfccdXe3l5V9k1V1dNPP93rOacqZ98MapqmafUiAAD2J79pGQCIJ3gAgHiCBwCIJ3gAgHiCBwCIJ3iA/e6GG26o5cuX/5+uWbx4cV1++eX7Z0HAgDNwfjUr0DIXX3xxq5cADHBe4QHetNmzZ3d/TMjdd99dJ5xwQu3YsaOqXv2lZRMmTKjFixdXVdUJJ5xQ3/nOd2rOnDl12mmn1W233VZVr35K9fz582vq1Kk1Z86cevjhh1vzYIBIggd406ZOnVr33ntvVVXdd999NWLEiFq9enU1TVMrV66s4447rvvcnTt31pFHHlm333573XjjjXXNNddUZ2dn3XbbbfXUU0/Vz372s/rhD39YzzzzTKseDhBI8ABv2mvB0zRNrV69us4777xatWpVPfLII/WOd7yj2traepw/ZcqUqqo6/vjja+fOnfXyyy/XAw88UO3t7TVs2LAaPnx4dXR0tOKhAKEED/Cmvec976murq5asWJFjRs3riZPnlyrVq2q3/zmNzV9+vRe5x966KFVVTVo0KCqevVDHfd0MH0KM3DgEzzAPvGRj3ykvvnNb9bEiRPr2GOPra1bt9Zdd91V06ZNe0PXf/jDH67ly5dXZ2dndXZ2dn9aNcC+4H9pAfvE1KlTa/HixTVhwoSqqpowYUI99thjdfTRR7+h6+fMmVNr166t9vb2GjlyZI0dO3Z/LhcYYHxaOgAQz1taAEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxBM8AEA8wQMAxPt/lMth6g1h8uMAAAAASUVORK5CYII=\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": 44,
   "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"
   ]
  },
  {
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of outliers in temp is : 401\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAAJPCAYAAAB4uQ71AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAASwUlEQVR4nO3dXYjV9b7H8e80M6jnVGPtHIiw7qIgIqEnsRrqkBU6FtSFCu7Iiwgq6eEijE4XbbKMHsgQugh6IDY9UQgldWFUbJXI6IGgYu8eNDNqSh0rTZcz61x0sk5n9s5N6X/NZ16vO2cW4/fv19+sN/81urra7Xa7AACCHdL0AAAAB5rgAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIF7Pbz1g27bva3R04vxXPX/606H1zTffNT0G+8m+xg+7Gl/sa3yxr6pDDumqI474z3/6+d8MntHR9oQKnqqacNc73tnX+GFX44t9jS/29a95SQsAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4ggcAiCd4AIB4PU0PAOn++tfH6rPPNjY9RuN6e7ur1RoZ83PDw9urqqqvb+rBG+ggmz79uFq48M9NjwETluCBA+yzzzbWh3//R3VPntr0KB1r5IftVVU1tGNvs4McID9dH9AcwQMHQffkqfUfx/1X02N0rJ0b11RVxf4Z/XR9QHP8DA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxGg+etWtfq7VrX2t6DADgAOmE5/qeRn/3qvrb316tqqpZs85peBIA4EDohOf6xu/wAAAcaIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIgneACAeIIHAIjX0/QAw8Pba3h4uJYv/0vTo1RVVW9vd7VaI02PwX4aD/vatGljjY50Nz0GDRrd+0Nt2rSxY77P7Y/xcLb4Wafva9OmjdXX19foDO7wAADxGr/D09c3tfr6ptZNN/1306NUVdW0aYfV0NC3TY/BfhoP+1q+/C/1j8++bnoMGnRIz+Q6dvpRHfN9bn+Mh7PFzzp9X51wd9MdHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgXk/TA5x11kDTIwAAB1AnPNc3HjyzZp3T9AgAwAHUCc/1XtICAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgnuABAOIJHgAgXk/TA8BEMPLD9tq5cU3TY3SskR+2V1XF/hn9eH1HNT0GTGiCBw6w6dOPa3qEjtDb212t1siYnxse/vFbUV/f1IM40cF0lL8H0DDBAwfYwoV/bnqEjjBt2mE1NPRt02MAE5Sf4QEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4vX81gMOOaTrYMzRUSbiNY9n9jV+2NX4Yl/jy0Tf129df1e73W4fpFkAABrhJS0AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIJ7gAQDiCR4AIN5vvrVEsvvvv79eeuml6urqqssuu6yuuOKKWrduXd1xxx21e/fuuuiii+r6669vekz+11j7Wrp0ab355ps1ZcqUqqq65ppr6vzzz294Un6yfPny2rZtW915553O1jjwy305W51r0aJFtXXr1urp+fEp/Lbbbqvvv//e+fot7Qnq9ddfb8+fP7/darXau3btap977rnt999/vz0wMNDetGlTu9VqtRcvXtx+5ZVXmh6V9tj7+uijj9pz585tf/nll02PxxjWrVvXPuOMM9o33XRTe9euXc5Wh/vlvtrttrPVoUZHR9tnnXVWu9Vq7fuY87V/JuxLWqeffno99thj1dPTU998802NjIzUjh076rjjjqvp06dXT09PDQ4O1osvvtj0qNTY+5o8eXJt2bKlbr755hocHKwVK1bU6Oho06NSVdu3b6/77ruvrrrqqqqqevfdd52tDvbrfe3atcvZ6lAff/xxVVUtXry45s2bV48//rjztZ8mbPBUVfX29taKFStqzpw5NXPmzPrqq69q2rRp+z7f399fX375ZYMT8ku/3tfevXvrzDPPrGXLltVTTz1VGzZsqGeeeabpMamqW2+9ta6//vo6/PDDq6qcrQ736319/fXXzlaH2rFjR82cObNWrlxZjzzySD3xxBO1ZcsW52s/TOjgqapasmRJrV+/vr744ov69NNPq6vr57eXb7fb/+fXNO+X+1q/fn2tXLmy+vv7a8qUKbVo0aJ69dVXmx5xwnv66afr6KOPrpkzZ+772OjoqLPVocba1/Tp052tDjVjxoy666676rDDDqsjjzyyLrvsslqxYoXztR8m7A8tf/TRR7Vnz5468cQTa8qUKTV79ux68cUXq7u7e99jhoaGqr+/v8Ep+clY+1q9enVNnTq1Lrjggqr68ZD/9EN8NGf16tU1NDRUF198cQ0PD9fOnTvr888/d7Y61Fj7uvrqq2vevHnOVgfasGFDtVqtfYHabrfrmGOOqaGhoX2Pcb7GNmHv8GzevLluueWW2rNnT+3Zs6fWrFlT8+fPr08++aQ2btxYIyMj9fzzz9c555zT9KjU2Ps67bTTatmyZTU8PFytVquefPJJ/4qkAzz88MP1/PPP16pVq2rJkiV13nnn1UMPPeRsdaix9nXttdc6Wx3q22+/rbvuuqt2795d3333XT333HN1ww03OF/7YcIm+8DAQL377rt1ySWXVHd3d82ePbvmzJlTRx55ZF177bW1e/fuGhgYqAsvvLDpUamx93XNNdfUEUccUQsWLKi9e/fW7Nmza+7cuU2PyhgmTZpUd955p7M1Tpxwwgl15ZVXOlsd6Nxzz6133nmnLrnkkhodHa2FCxfWjBkznK/90NVut9tNDwEAcCBN2Je0AICJQ/AAAPEEDwAQT/AAAPEEDwAQT/AAv9vixYtr69atTY8B8E8JHuB3W7t2bdMjAPxLE/Y/HgT+GEuXLq2qqssvv7wefPDBWrZsWX3xxRfVarVqzpw5ddVVV9XmzZvr8ssvr1mzZtV7771XIyMjtWTJknryySfr448/rpNOOqnuvffe2rJlSy1atKjOPvvseuedd6rdbtett95ap556asNXCYx37vAAv8sdd9xRVVWPPvpoLV26tC699NJ69tln65lnnql169bV6tWrq+rHtwcZGBioZ599tk455ZS6/fbb6957760XXnihNmzYUG+//XZVVW3ZsqVOO+20WrVqVd1444113XXXVavVaurygBDu8AB/iF27dtUbb7xRw8PDdf/991dV1c6dO+uDDz6ok08+uXp7e+u8886rqqpjjz22ZsyYUYceemhVVfX399fw8HD19/dXX19fDQ4OVtWPbynS3d1dH374YZ100knNXBgQQfAAf4iurq5qt9v1xBNP1JQpU6qqauvWrTVp0qTatm1b9fb2VldX177H9/b2jvl1fvmu6lVVo6Oj/+9jAP8uL2kBv1t3d3f19PTUKaecUg8//HBVVe3YsaMWLFhQa9as+be+1tatW+u1116rqqqXX365ent76/jjj//DZwYmFnd4gN/twgsvrEWLFtUDDzxQ99xzTw0ODtaePXtq7ty5NW/evNq8efN+f61JkybVqlWr6u67767JkyfXypUr3eEBfjfvlg50jM2bN9fg4GC99dZbTY8ChPGSFgAQzx0eACCeOzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDE+x9x7CPz3H3LlAAAAABJRU5ErkJggg==\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": 46,
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weath.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5875, 7)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weath.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi['destination'].unique().sort()==weath['location'].unique().sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Haymarket Square           492\n",
       "Back Bay                   491\n",
       "North End                  491\n",
       "Beacon Hill                490\n",
       "North Station              490\n",
       "Boston University          490\n",
       "West End                   489\n",
       "Fenway                     489\n",
       "Northeastern University    489\n",
       "Theatre District           488\n",
       "South Station              488\n",
       "Financial District         488\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weather shape:(5875, 7)\n",
      "Taxi shape:(535231, 9)\n"
     ]
    }
   ],
   "source": [
    "print('weather shape:{}'.format(weath.shape))\n",
    "print('Taxi shape:{}'.format(taxi.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=taxi.copy()\n",
    "data2=weath.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1544770</td>\n",
       "      <td>West End</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</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>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</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>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</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>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</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>cheap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type time_stamp         destination                   source  \\\n",
       "0  0.787401     Uber    1544770            West End         Haymarket Square   \n",
       "1  1.506652     Uber    1543400   Boston University              Beacon Hill   \n",
       "2  1.414214     Lyft    1544920            Back Bay         Haymarket Square   \n",
       "3  1.994994     Lyft    1543620  Financial District  Northeastern University   \n",
       "4  1.220656     Lyft    1543580            Back Bay  Northeastern University   \n",
       "\n",
       "   surge_multiplier                            product_id      name  \\\n",
       "0               1.0  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a      Taxi   \n",
       "1               1.0  997acbb5-e102-41e1-b155-9df7de0a73f2  UberPool   \n",
       "2               1.0                                  lyft      Lyft   \n",
       "3               1.0                             lyft_plus   Lyft XL   \n",
       "4               1.0                                  lyft      Lyft   \n",
       "\n",
       "  RideCategory  \n",
       "0      unknown  \n",
       "1        cheap  \n",
       "2     moderate  \n",
       "3    expensive  \n",
       "4        cheap  "
      ]
     },
     "execution_count": 52,
     "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": 53,
   "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>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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.rename(columns={'location':'destination'},inplace=True)\n",
    "data2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(204401, 14)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedf = data2.merge(data, how='inner')\n",
    "mergedf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedf.duplicated().sum() == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125120, 14)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedf.drop_duplicates(inplace=True)\n",
    "mergedf.duplicated().sum() == 0\n",
    "mergedf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data=mergedf.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying some Exploratory Data Analysis and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RideCategory', ylabel='count'>"
      ]
     },
     "execution_count": 58,
     "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.countplot(data=data,x='RideCategory',hue='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cab_type'>"
      ]
     },
     "execution_count": 59,
     "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')['RideCategory'].count().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# let's apply feature selection "
   ]
  },
  {
   "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",
       "      <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>RideCategory</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>1.545962</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>moderate</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>0.741620</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>moderate</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>1.462874</td>\n",
       "      <td>Uber</td>\n",
       "      <td>West End</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</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>0.663325</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>moderate</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>1.473092</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>moderate</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204388</th>\n",
       "      <td>40.31</td>\n",
       "      <td>West End</td>\n",
       "      <td>0.86</td>\n",
       "      <td>994.07</td>\n",
       "      <td>1543440</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9.54</td>\n",
       "      <td>1.754993</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft_line</td>\n",
       "      <td>Shared</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204390</th>\n",
       "      <td>40.31</td>\n",
       "      <td>West End</td>\n",
       "      <td>0.86</td>\n",
       "      <td>994.07</td>\n",
       "      <td>1543440</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9.54</td>\n",
       "      <td>1.417745</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>South Station</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204394</th>\n",
       "      <td>40.31</td>\n",
       "      <td>West End</td>\n",
       "      <td>0.86</td>\n",
       "      <td>994.07</td>\n",
       "      <td>1543440</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9.54</td>\n",
       "      <td>0.824621</td>\n",
       "      <td>Uber</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6d318bcc-22a3-4af6-bddd-b409bfce1546</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204395</th>\n",
       "      <td>40.31</td>\n",
       "      <td>West End</td>\n",
       "      <td>0.86</td>\n",
       "      <td>994.07</td>\n",
       "      <td>1543440</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9.54</td>\n",
       "      <td>1.086278</td>\n",
       "      <td>Uber</td>\n",
       "      <td>South Station</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6d318bcc-22a3-4af6-bddd-b409bfce1546</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204399</th>\n",
       "      <td>40.31</td>\n",
       "      <td>West End</td>\n",
       "      <td>0.86</td>\n",
       "      <td>994.07</td>\n",
       "      <td>1543440</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9.54</td>\n",
       "      <td>1.682260</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Fenway</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lyft_lux</td>\n",
       "      <td>Lux Black</td>\n",
       "      <td>expensive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125120 rows × 14 columns</p>\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",
       "204388  40.31       West End    0.86    994.07    1543440      0.66  9.54   \n",
       "204390  40.31       West End    0.86    994.07    1543440      0.66  9.54   \n",
       "204394  40.31       West End    0.86    994.07    1543440      0.66  9.54   \n",
       "204395  40.31       West End    0.86    994.07    1543440      0.66  9.54   \n",
       "204399  40.31       West End    0.86    994.07    1543440      0.66  9.54   \n",
       "\n",
       "        distance cab_type              source  surge_multiplier  \\\n",
       "0       1.545962     Lyft         Beacon Hill               1.0   \n",
       "1       0.741620     Uber    Theatre District               1.0   \n",
       "2       1.462874     Uber            West End               1.0   \n",
       "3       0.663325     Lyft  Financial District               1.0   \n",
       "4       1.473092     Uber       North Station               1.0   \n",
       "...          ...      ...                 ...               ...   \n",
       "204388  1.754993     Lyft   Boston University               1.0   \n",
       "204390  1.417745     Lyft       South Station               1.0   \n",
       "204394  0.824621     Uber    Haymarket Square               1.0   \n",
       "204395  1.086278     Uber       South Station               1.0   \n",
       "204399  1.682260     Lyft              Fenway               1.0   \n",
       "\n",
       "                                  product_id       name RideCategory  \n",
       "0                                  lyft_plus    Lyft XL     moderate  \n",
       "1       6c84fd89-3f11-4782-9b50-97c468b19529      Black     moderate  \n",
       "2       8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a       Taxi      unknown  \n",
       "3                                   lyft_lux  Lux Black     moderate  \n",
       "4       6f72dfc5-27f1-42e8-84db-ccc7a75f6969     UberXL     moderate  \n",
       "...                                      ...        ...          ...  \n",
       "204388                             lyft_line     Shared        cheap  \n",
       "204390                                  lyft       Lyft     moderate  \n",
       "204394  6d318bcc-22a3-4af6-bddd-b409bfce1546  Black SUV    expensive  \n",
       "204395  6d318bcc-22a3-4af6-bddd-b409bfce1546  Black SUV    expensive  \n",
       "204399                              lyft_lux  Lux Black    expensive  \n",
       "\n",
       "[125120 rows x 14 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop(['RideCategory','destination','source','product_id','name','cab_type'],axis=1)\n",
    "y = data['RideCategory']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_scores = chi2(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([9.38172095e-01, 1.21443264e-01, 6.30094483e+00, 7.19805289e-01,\n",
       "        1.22265832e-01, 6.86190830e+01, 1.51454218e+03, 0.00000000e+00]),\n",
       " array([9.19021670e-01, 9.98229399e-01, 1.77772568e-01, 9.48864253e-01,\n",
       "        9.98205822e-01, 4.44065997e-14, 0.00000000e+00, 1.00000000e+00]))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_values = pd.Series(chi_scores[1],index = x.columns)\n",
    "p_values.sort_values(ascending = False , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAKFCAYAAADlOBydAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAyXklEQVR4nO3de5SVdbnA8WeABBQUxRnwkldSysDsqCEaKnlAjLuIHhEsDTVFkpOGCmJlqHhZeGvl5ViKYoI3lFIkLqUFpuAxvEDeMTWZkVEQHIFh9vnDw86RgZH44bvH/fms5Vrz7r1hPfMTmO9+b7skl8vlAgCAzdIo6wEAAL4IRBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABJokvUAERHvvbcyamqyv11W69YtYunSFVmPUXCsy/qsSd2sS92sS92sy/qsSd0KZV0aNSqJ7bffZoPPF0RU1dTkCiKqIqJg5ig01mV91qRu1qVu1qVu1mV91qRuDWFdHP4DAEhAVAEAJCCqAAASEFUAAAmIKgCABEQVAEACogoAIAFRBQCQgKgCAEhAVAEAJCCqAAASEFUAAAmIKgCABEQVAEACogoAIAFRBQCQgKgCAEhAVAEAJCCqAAAS+ExRtWLFiujZs2e8+eab6z23cOHC6N+/f3Tv3j1GjRoV1dXVyYcEACh09UbV3/72t/iv//qveP311+t8/rzzzosxY8bEo48+GrlcLiZPnpx6RgCAgldvVE2ePDkuvvjiKCsrW++5t956Kz766KP4xje+ERER/fv3j2nTpiUfEgCg0DWp7wVjx47d4HPl5eVRWlqa3y4tLY0lS5Zs8hCtW7fY5F/zaavXrI2tvtR4s3+f0tKWBTFHodncdfkisiZ1sy51sy51sy7rsyZ1awjrUm9UbUxNTU2UlJTkt3O5XK3tz2rp0hVRU5PbnFGitLRl9Prxg5v1e6Qw9eo+UVHxQdZjREREy22bR7Omm/W/OJmPVlXHB8ursh4jIqzLllRa2rJg/vwXEutSN+uyPmtSt0JZl0aNSja6I2izfrK0bds2Kioq8tvvvvtunYcJyUazpk0KIjQjPo7N7P86fMy6ALAlbNYtFXbZZZdo2rRpzJ8/PyIiHnzwwejSpUuSwQAAGpJ/K6qGDh0azz77bEREXHXVVXHZZZfF0UcfHR9++GEMGTIk6YAAAA3BZz78N2vWrPzXt9xyS/7r9u3bx7333pt2KgCABsYd1QEAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACCBJlkPABSGlts2j2ZNN/+fhNLSlpv9e3y0qjo+WF612b8PwOdJVAEREdGsaZPo9eMHsx4jIiKmXt0nPsh6CIBN5PAfAEACogoAIAFRBQCQgKgCAEhAVAEAJCCqAAASEFUAAAmIKgCABEQVAEACogoAIAFRBQCQgKgCAEhAVAEAJNAk6wEAClnLbZtHs6ab/09laWnLzfr1H62qjg+WV232HMCWI6oANqJZ0ybR68cPZj1GTL26T3yQ9RDARjn8BwCQgKgCAEhAVAEAJCCqAAASEFUAAAmIKgCABEQVAEACogoAIAFRBQCQgKgCAEhAVAEAJCCqAAASEFUAAAmIKgCABEQVAEACogoAIAFRBQCQgKgCAEhAVAEAJCCqAAASEFUAAAmIKgCABEQVAEACogoAIAFRBQCQgKgCAEhAVAEAJCCqAAASEFUAAAmIKgCABEQVAEACogoAIAFRBQCQgKgCAEhAVAEAJCCqAAASEFUAAAmIKgCABEQVAEACogoAIAFRBQCQgKgCAEhAVAEAJCCqAAASEFUAAAmIKgCABEQVAEACogoAIAFRBQCQgKgCAEhAVAEAJCCqAAASEFUAAAmIKgCABEQVAEACogoAIAFRBQCQwGeKqqlTp8YxxxwT3bp1i4kTJ673/PPPPx/HHnts9O7dO04//fRYvnx58kEBAApZvVG1ZMmSGD9+fNx1110xZcqUmDRpUrz88su1XjN27NgYPnx4PPTQQ7HnnnvGrbfeusUGBgAoRPVG1Zw5c6JTp07RqlWr2HrrraN79+4xbdq0Wq+pqamJlStXRkREVVVVNGvWbMtMCwBQoJrU94Ly8vIoLS3Nb5eVlcWCBQtqveb888+PU045JS699NJo3rx5TJ48eZOGaN26xSa9vtCVlrbMeoSCZF3qZl3qZl3W90Vcky/i97S5rEndGsK61BtVNTU1UVJSkt/O5XK1tj/66KMYNWpU3HbbbdGxY8f4zW9+EyNHjoybb775Mw+xdOmKqKnJbeLotRXSYldUfJD1CBFRWGsSYV02xLrUzbqsr1DWJJXS0pZfuO9pc1mTuhXKujRqVLLRHUH1Hv5r27ZtVFRU5LcrKiqirKwsv/3iiy9G06ZNo2PHjhERcfzxx8eTTz65OTMDADQ49UZV586dY+7cuVFZWRlVVVUxffr06NKlS/753XffPd5555149dVXIyJi5syZ0aFDhy03MQBAAar38F+bNm1ixIgRMWTIkFizZk0MGDAgOnbsGEOHDo3hw4dHhw4d4rLLLotzzjkncrlctG7dOi699NLPY3YAgIJRb1RFRPTq1St69epV67Fbbrkl//Xhhx8ehx9+eNrJAAAaEHdUBwBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAp8pqqZOnRrHHHNMdOvWLSZOnLje86+++moMHjw4evfuHaeeemosW7Ys+aAAAIWs3qhasmRJjB8/Pu66666YMmVKTJo0KV5++eX887lcLn74wx/G0KFD46GHHoqvfvWrcfPNN2/RoQEACk29UTVnzpzo1KlTtGrVKrbeeuvo3r17TJs2Lf/8888/H1tvvXV06dIlIiLOOOOMGDRo0JabGACgANUbVeXl5VFaWprfLisriyVLluS333jjjdhxxx3jwgsvjH79+sXFF18cW2+99ZaZFgCgQDWp7wU1NTVRUlKS387lcrW2q6ur48knn4w777wzOnToENdcc01cfvnlcfnll3/mIVq3brGJYxe20tKWWY9QkKxL3axL3azL+r6Ia/JF/J42lzWpW0NYl3qjqm3btjFv3rz8dkVFRZSVleW3S0tLY/fdd48OHTpERETPnj1j+PDhmzTE0qUroqYmt0m/5tMKabErKj7IeoSIKKw1ibAuG2Jd6mZd1lcoa5JKaWnLL9z3tLmsSd0KZV0aNSrZ6I6geg//de7cOebOnRuVlZVRVVUV06dPz58/FRFxwAEHRGVlZSxatCgiImbNmhX77bdfgtEBABqOevdUtWnTJkaMGBFDhgyJNWvWxIABA6Jjx44xdOjQGD58eHTo0CF++ctfxujRo6Oqqiratm0bV1xxxecxOwBAwag3qiIievXqFb169ar12C233JL/ev/9949777037WQAAA2IO6oDACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkMBniqqpU6fGMcccE926dYuJEydu8HV//OMfo2vXrsmGAwBoKJrU94IlS5bE+PHj4/7774+tttoqTjjhhPjWt74V7dq1q/W6d999N8aNG7fFBgUAKGT17qmaM2dOdOrUKVq1ahVbb711dO/ePaZNm7be60aPHh3Dhg3bIkMCABS6evdUlZeXR2lpaX67rKwsFixYUOs1EyZMiK997Wux//77/1tDtG7d4t/6dYWqtLRl1iMUJOtSN+tSN+uyvi/imnwRv6fNZU3q1hDWpd6oqqmpiZKSkvx2Lpertf3iiy/G9OnT47bbbot33nnn3xpi6dIVUVOT+7d+7TqFtNgVFR9kPUJEFNaaRFiXDbEudbMu6yuUNUmltLTlF+572lzWpG6Fsi6NGpVsdEdQvYf/2rZtGxUVFfntioqKKCsry29PmzYtKioq4thjj43TTjstysvL48QTT9zMsQEAGpZ6o6pz584xd+7cqKysjKqqqpg+fXp06dIl//zw4cPj0UcfjQcffDBuvvnmKCsri7vuumuLDg0AUGjqjao2bdrEiBEjYsiQIdG3b9/o2bNndOzYMYYOHRrPPvvs5zEjAEDBq/ecqoiIXr16Ra9evWo9dsstt6z3ul133TVmzZqVZjIAgAbEHdUBABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJCAqAIASEBUAQAkIKoAABIQVQAACYgqAIAERBUAQAKiCgAgAVEFAJDAZ4qqqVOnxjHHHBPdunWLiRMnrvf8jBkzok+fPtG7d+8488wzY9myZckHBQAoZPVG1ZIlS2L8+PFx1113xZQpU2LSpEnx8ssv559fsWJF/PSnP42bb745Hnroodh3333j+uuv36JDAwAUmnqjas6cOdGpU6do1apVbL311tG9e/eYNm1a/vk1a9bExRdfHG3atImIiH333Tf++c9/brmJAQAKUL1RVV5eHqWlpfntsrKyWLJkSX57++23j//8z/+MiIiPPvoobr755jjqqKO2wKgAAIWrSX0vqKmpiZKSkvx2Lpertb3OBx98EGeddVa0b98++vXrt0lDtG7dYpNeX+hKS1tmPUJBsi51sy51sy7r+yKuyRfxe9pc1qRuDWFd6o2qtm3bxrx58/LbFRUVUVZWVus15eXlceqpp0anTp3iwgsv3OQhli5dETU1uU3+dZ9USItdUfFB1iNERGGtSYR12RDrUjfrsr5CWZNUSktbfuG+p81lTepWKOvSqFHJRncE1Xv4r3PnzjF37tyorKyMqqqqmD59enTp0iX//Nq1a+OMM86IHj16xKhRo+rciwUA8EVX756qNm3axIgRI2LIkCGxZs2aGDBgQHTs2DGGDh0aw4cPj3feeSdeeOGFWLt2bTz66KMREfH1r389xo4du8WHBwAoFPVGVUREr169olevXrUeu+WWWyIiokOHDrFo0aL0kwEANCDuqA4AkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAQ+0y0VAGCdlts2j2ZN0/z42Nw71n+0qjo+WF6VZBbYXKIKgE3SrGmT6PXjB7MeIyIipl7dJ7L/8BL4mMN/AAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACQgqgAAEhBVAAAJiCoAgAREFQBAAqIKACABUQUAkICoAgBIQFQBACTwmaJq6tSpccwxx0S3bt1i4sSJ6z2/cOHC6N+/f3Tv3j1GjRoV1dXVyQcFAChk9UbVkiVLYvz48XHXXXfFlClTYtKkSfHyyy/Xes15550XY8aMiUcffTRyuVxMnjx5iw0MAFCImtT3gjlz5kSnTp2iVatWERHRvXv3mDZtWgwbNiwiIt5666346KOP4hvf+EZERPTv3z+uu+66OPHEEz/zEI0alWz65HUo2755kt9nc6X6flIolDWJsC4bYl3qZl3WZ03qVkjrksIX7ftJpRDWpb4Z6o2q8vLyKC0tzW+XlZXFggULNvh8aWlpLFmyZJOG3H77bTbp9Rty6+huSX6fzdW6dYusR8grlDWJsC4bYl3qZl3WZ03qVkjrksIX7ftJpSGsS72H/2pqaqKk5F9llsvlam3X9zwAQDGoN6ratm0bFRUV+e2KioooKyvb4PPvvvturecBAIpBvVHVuXPnmDt3blRWVkZVVVVMnz49unTpkn9+l112iaZNm8b8+fMjIuLBBx+s9TwAQDEoyeVyufpeNHXq1LjppptizZo1MWDAgBg6dGgMHTo0hg8fHh06dIhFixbF6NGjY8WKFbHffvvFZZddFltttdXnMT8AQEH4TFEFAMDGuaM6AEACogoAIAFRBQCQgKgCAEhAVAEAJFDUUVVZWZn1CADAF0RRR9WgQYOyHqFgrV69OiIiFi9eHH/84x+jpqYm44myt25N+Jc1a9bEbbfdFmeccUYMGzYsJk+eHO7S8rHq6up4/vnnY9GiRdbkEz788MP8mnz44YdZj0OBmz9/fvz2t7+N1atXx1NPPZX1OPUq6vtUjRgxIg4//PDo2LFjNGvWLP/4zjvvnOFU2bvhhhvi1VdfjXPPPTcGDhwY7dq1i3bt2sXo0aOzHi1TRxxxRBx55JHRr1+/6NixY9bjFISRI0fGRx99FH369Imampp48MEHo23btjFq1KisR8vUX/7ylxg5cmSUlZVFTU1NLF++PK655pqi/3Mzd+7cGDNmTKxduzYmTZoUPXv2jKuvvjoOO+ywrEf73LVv377W5+Q2adIkGjduHKtWrYoWLVo0iIDY0m6//faYMWNGlJeXx9133x0nnnhiDBgwIE499dSsR9uwXBE78sgj1/uva9euWY+VuX79+uWqqqpyN910U27cuHH5x4rdhx9+mHvggQdyJ598cq5nz565//mf/8mVl5dnPVamunfvXmt77dq1uR49emQ0TeH47ne/m1u4cGF+e8GCBf4O5XK5AQMG5MrLy3N9+vTJ5XK53EsvvZTr1atXtkNlbMyYMbkpU6bkampqcrlcLjdt2rTcqFGjMp6qMPTp0ye3atWq/J+XFStWFPy/L02yjroszZo1K+sRClJNTU00a9YsZs+eHeecc07U1NREVVVV1mNlrnnz5tG3b9/o27dv/OEPf4hf/OIXccMNN8QhhxwSI0eOjN133z3rET93u+66ayxevDj/vb/77rvRpk2bjKfK3lZbbRXt27fPb3fo0CHDaQpHTU1NlJaW5rfbtWuX4TSFYcGCBfGzn/0sv929e/f41a9+leFEhaNRo0a1PvKuadOm0bhx4wwnql9RR9WyZcviyiuvjDfeeCOuu+66GDduXFxwwQWx7bbbZj1apg455JDo2bNnNGvWLA466KA46aST4sgjj8x6rMwtXrw4Hnroofjd734XO++8c5x77rnRrVu3eOKJJ2Lo0KExffr0rEf83FVXV0efPn3iwAMPjMaNG8f8+fOjrKwshgwZEhEREyZMyHjCbBx44IExatSoGDhwYDRu3Dh+//vfxy677JI/pHPQQQdlPGE22rZtG7Nnz46SkpJYvnx5TJw4sehPt2jevHncd9990aNHj/wh9O222y7rsQrCwQcfHOPGjYuqqqqYMWNGTJo0KTp16pT1WBtV1OdUDR8+PA499NCYOHFi3HvvvfHLX/4yFi5cGDfffHPWo2Xu7bffjrZt20ajRo1i4cKF8dWvfjXrkTLXtWvX6N+/f/Tr1y922WWXWs9deumlceGFF2Y0WXaefPLJjT5/8MEHf06TFJbBgwdv8LmSkpKijc2lS5fG2LFjY86cOZHL5eJb3/pWjB49OsrKyrIeLTNvvfVWXHLJJfHXv/41SkpK4tBDD43Ro0fb4xsf79mcPHlyzJkzJ2pqauKQQw6J448/Ppo0Kdz9QUUdVf3794/7778/+vbtG1OmTImIiN69e8dDDz2U7WAZueCCCzb6/GWXXfY5TVKY7r333hgwYECtxyZOnFj0V5H+/e9/j+XLl9d6rFj3xLBx48ePjxEjRmQ9Bg3EypUrY8qUKTFo0KBYsmRJ3H333XHaaadF8+bNsx5tgwo39z4HjRs3jg8++CB/Bcbrr78ejRoV710m1u1VmD17dqxcuTJ69+4dTZo0iYcffjhatmyZ8XTZue2222LFihVx9913xzvvvJN/vLq6On73u98VdVT993//dzz//PO19jQU856YdebNmxe33357LFu2rNbjxb4u687T/ORVb8Xu8ccfj2uuuSaWLVtW69YbM2fOzHCqwvDjH/849t1334iI2GabbaKmpiZ+8pOfxPXXX5/xZBtW1FF19tlnx+DBg+Of//xnnHnmmfHMM8/EpZdemvVYmenXr19ERNx1110xadKkfGD26NEjBg4cmOVomdpjjz3iueeeW+/xpk2bxuWXX57BRIVj4cKF8fDDDxf8yaOft/PPPz+GDRtW9OcLfVqrVq3i6KOPjv322y+aNm2af7yY94L/4he/iPPPPz++8pWviM1Pefvtt+PGG2+MiIgWLVrEiBEjok+fPhlPtXFFHVVdunSJr3/967FgwYJYu3Zt/PznP48dd9wx67Ey98EHH8T7778fO+ywQ0R8fEVXMd+k74gjjogjjjgievToEXvvvXfW4xSU/fffPxYvXhx77bVX1qMUlDZt2kTfvn2zHqPgrHvjxr9sv/32LgTagJKSkvj73/+e31v1yiuvFPT5VBFFek7VpEmT4vjjj48bbrihzueHDRv2OU9UWKZMmRJXXXVVfPOb34xcLhfPPPNMjB49Orp37571aJk4/fTT46abboquXbvW+U6ymHfTT5kyJS688MIoKyuLxo0bRy6Xi5KSkqJek4iIadOmxYwZM6JTp061fggUe2i9/fbbdT5ezHv0rrzyyqiuro5vf/vbtfbeOS8xYs6cOXHeeeflT9p/77334oorrijotSns5NtCirAjN0nfvn2jc+fO8b//+79RUlISP/3pT6N169ZZj5WZSy65JCIi7rjjjownKTw33XRT3H777UX9Q7Eu9913X6xatSrmz59f6/Fij6qTTjopSkpKIpfLRXV1dbz77rvx1a9+Ne67776sR8vMggULIiLihRdeyD/mvMSPde7cOWbPnh0vvvhiNGnSJPbaa69a960qREW5p4qNswevtnVXhm5IMf+gPPHEE2PixInOBfmUfv36xQMPPJD1GAVvwYIFMXHixBg3blzWo1CA3nrrrbjzzjvXO4m/kM/BK8o9VZ/+zKV11h26WLhwYQZTFaY1a9bE448/Hvvvv3/Wo2Tmr3/9a0REvPHGG7F48eI4/PDDo3HjxvHnP/852rVrV9RRtccee8TAgQOjc+fO8aUvfSn/eLEG+DodO3aM2bNnR5cuXZzEvxEdO3Ysyvu7RURcdNFFcckll+RvlPtp9lRFnHPOOXHggQfGgQce2GDeuBVlVC1atCjrEQrap38gnnXWWXHKKadkNE321r0rGjx4cDz00EP5E/iXLVsWZ511VpajZW7nnXd26K8OM2fOjEmTJkVE5A93ecO2/l7wl156qWhPLTj++OMjIuLZZ5+NkSNHRrNmzfxd+pTq6uoYOXJk1mNskqKMqnWWL18e119/fTzxxBPRpEmT6NKlS/zwhz+MZs2aZT1aQVm5cuUGTzAtJuXl5dGqVav8dvPmzaOioiK7gQrApwM8l8vFm2++mdE0hePPf/5z1iM0CAcffHB897vfzXqMTHz961+PiIjf/OY38fjjj8djjz0Wa9eujS5durga8P/9x3/8R8yaNSsOO+ywgj+Xap2iPqfq9NNPj7322iv69u0buVwu7rvvvqisrIyrr74669Ey9cmr3HK5XCxbtix+8IMfxA9/+MOMJ8vWZZddFosWLYpu3bpFLpeLRx55JA466KA455xzsh4tM5MmTcp/Ntc6u+66a/zhD3/IcKrsrV69On7961/Ha6+9FhdddFHcdtttcdpppzWYHwxb0urVq2OrrbaKxYsXx2uvvRZdunQp6psur1NZWRnTpk2LG2+8MSorK+u8N16xOeyww+Ldd9+t9Vih7/Et6qjq2bNn/O53v6v3sWLz1ltv5b8uKSmJbbfdNlq0aJHhRIXj0UcfjSeffDJKSkrikEMOie985ztZj5Sprl27xu233x7XXHNNjBgxIv70pz/F008/XfRvTEaPHh077LBDzJo1K+65554YM2ZM5HK5uOqqq7IeLVO//OUv45VXXolzzz03Bg4cGF/5yldi7733jtGjR2c9WmZ+9rOfxfz586Nx48Zx0EEHxbe+9a04+OCDi/pTLBqyoj78165du5g3b14ceOCBEfHxuVa77757xlNlb+edd47f/va38cQTT0R1dXV06tQpTjrppKJ9N/n888/HfvvtF0899VTssMMOcfTRR+efe+qppwr6nilbWuvWrePLX/5y7LvvvvHiiy/GoEGD4re//W3WY2Xu+eefjwceeCAee+yxaN68eVxxxRXRq1evrMfK3MyZM+Ouu+6KCRMmRO/eveMnP/lJ9O/fP+uxMrV8+fLI5XKx5557xt577x177bWXoPp/lZWV8dBDD8XKlSsjl8tFTU1NvPnmm3HFFVdkPdoGFXVUvfrqq3HSSSfFnnvuGY0bN47XXnsttttuu/zhr2K9geEVV1wRixcvjmOPPTZyuVzcf//98cYbbxTtu8m77747LrnkkrjuuuvWe67Y7yfTvHnzeOKJJ2LfffeNGTNmRIcOHeKjjz7KeqzMlZSUxOrVq/OH0d97770Gc/XSllRTUxPNmjXLfwZgTU1NrUPHxWjdXt1XXnkl5s6dG2eccUZ8+OGH8fjjj2c8WfbOOeec2GmnneKZZ56Jo446Kv74xz9Ghw4dsh5ro4o6qn71q19lPUJB+stf/hJTpkzJ75k64ogjivpdtpt/bthFF10U99xzT5x//vlx7733Ro8ePYr+dgoREUOGDInvf//7UVFREWPHjo0//OEP1iUiDjnkkOjZs2c0a9YsDjrooDjppJOia9euWY+VqVdffTXmzp0bc+fOjUWLFkXHjh3j8MMPz3qsglBeXh4TJkyIcePGRbdu3eIHP/hBnHzyyVmPtVFFGVWzZ8+OI488Mp566qk6ny/m+w5FRKxduzaqq6vzJ9WuXbvWvXYiYt68eXH77bfHsmXLaj1ezHuqysvL8/cZWvfJ8dOnT89ypIIwc+bM+PnPfx5PPPFE1NTUxI033hiXXXZZDBgwIOvRMjVy5MgYPHhwtGnTJho1ahRjxoyJ9u3bZz1Wpn70ox/FkUceGd/73vfigAMO8G/tJ2y33XYREbHnnnvGokWLGsT9Eosyqp599tk48sgj8zd1/LRij6pevXrFkCFD8pc6//73vy/ay54/6fzzz49hw4a5l0xEPPzww7F69eq47rrrYvjw4fnHq6ur46abbopu3bplOF12hg0bFgsXLozy8vJ44YUX8neBvvXWW2OnnXbKeLrsLViwIObPnx+DBg2KoUOHxgsvvBBXXHFFdOnSJevRMjN16tSsRyhYnTp1iuHDh8fIkSPjlFNOieeff77gb3lU1Ff//eUvf4lDDz201mPTp08v2h8In/TYY4/F3LlzI5fLRadOneKII47IeqTMDRo0KCZOnJj1GAXhnnvuiaeffjpmzZpV6/BN48aNo3PnznHMMcdkOF12VqxYEe+//36MHTu21jmITZo0idatW9f6cOViNHDgwBg+fHi899578cgjj8RFF10Uw4YNK+rP/mPj3njjjdhtt93iueeei3nz5sUxxxwTZWVlWY+1QUUZVfW9yy7We+xs6HDoOsV8lVtExLRp02LGjBnRqVOnWj8ci3nP5ty5c+OQQw7Jb69YscLtN9igAQMGxL333hs//vGP49vf/nb07ds3+vbtW+/na1Kczj777PxpBeucfPLJcfvtt2c0Uf2K8m3TypUr4+mnn46VK1fWOgTYuHHjGDFiRIaTZeuTV7ctXbo0WrduHVVVVVFeXh577LFHUZ87FBFx3333xapVq2L+/Pm1Hi/mqKqqqoorr7wyzjzzzBgwYEBUVlbGyJEji/4yeerWvHnz+PWvfx1PPPFEjBkzJiZMmBDbbLNN1mNRYNYdRl+yZEmtewGuXbs22rZtm+Fk9SvKPVXrfPpdNh+bMGFC3H///TFlypR48803Y+jQoXHyySfHCSeckPVomerXr1888MADWY9RUI499tgYO3ZsPPvsszFv3rwYM2ZMDB48OO6///6sR6MALVmyJO6555449NBD44ADDogrr7wyBg8eXPA/KPl8NeTD6IU72RY0ePDg/D1j6rqtQrHvkZk8eXLcc889EfHxR47cf//9MXDgwKKPqo4dO8bs2bOjS5curtD5hPbt28f1118fvXv3jm222SbWrFmT9UgUqDZt2kSnTp1i0aJFsd9++8URRxwhqFhPixYtokWLFnHttdfGq6++Gu3bt4+pU6fGCy+8EEOHDs1/qH0hKsqoOvvss7MeoaCtWbMmvvSlL+W3P/l1MZs5c2ZMmjSp1mOF/jlUW9qOO+4Yl1xySTz33HNx5ZVXxuWXX+7qSDbo9ttvjxkzZkR5eXkcffTRMWbMmBgwYECceuqpWY9GATrvvPNi1113jVWrVsX1118fffr0iQsuuCBuuummrEfboKI+/LehE7OL/YTsK6+8Mp555pno0aNHlJSUxKOPPhrf/OY3i/qDg6nbihUrYsaMGXHAAQfE7rvvHhMnTow+ffpEixYt8h/vA+v07ds3Jk+eHAMHDowpU6bEypUr47jjjouHH34469EoQMcee2zcd999ceWVV8Z2220Xp512Wv6xQlWUe6rW+eSJ2dXV1fH3v/89DjzwwKKPqvPOOy+mTZsWTz31VDRp0iSGDBkSRx11VNZjZe6GG26o8/FivlN2ixYtap2oP2jQoPzXo0ePdg4atTRq1Ch/U+GIiKZNmzqUzgatXbs2KisrY8aMGXH99ddHRUVFrFq1KuuxNqqoo+rTHzvyj3/8Iy677LKMpiksRx99dK0PDqa2NWvWxOOPP94g7vCblSLeCc4GHHzwwTFu3LioqqqKGTNmxKRJk6JTp05Zj0WBOvXUU2PgwIHRtWvX2GeffaJ79+7xox/9KOuxNqqoD//V5eijj45p06ZlPQYNwOrVq+OUU06JO++8M+tRCpKrJfm0mpqamDx5csyZMydqamqiU6dOccIJJxT01VwUjobwkWlF/Sf5ggsuqLX9yiuvxD777JPRNDQ0K1eujLfffjvrMaDBGDp0aNx6661FfyUxG3f66afHTTfdFF27ds1fqf9JM2fOzGCqz6aoo+rggw/Of11SUhJHH320+1axQZ/8C57L5WLZsmXxgx/8IOOpoOGoqqqKf/7znz4HkY365je/GVOmTGmQV+oXdVT169cvVqxYEcuXL88/9u6777oknDrdeuutMWfOnHjvvfciImLbbbeNbbfdNuOpCpczC/i0ysrK6Nq1a7Ru3TqaNm2af7yQ9zzw+Xv99dfj9ddfj3/84x+xePHiOPzww6NRo0bx5z//Odq1axf9+vXLesQNKupzqsaNGxeTJ0+OVq1aRcTHPwRKSkr8BadOZ555ZlRUVMTee+9da5d0sV/cMH/+/HjxxRfj2GOPjb/97W/5q2f/8Y9/xJe//OWMp6OQvPbaa/GnP/0pnnjiiWjcuHEcfvjhccghh/hzQp0GDx4c1157bf5mn8uWLYuzzjqroM9jLeo9VTNnzozHHnvMZ0/xmbz66qsuYviUjd3M0Q9KPu3GG2+MVatWxcCBA6OmpiYefPDBeOmll2LUqFFZj0YBKi8vz+/0iPj4syMrKiqyG+gzKOqo2nfffWP16tWiis9kt912i7ffftvh4U944IEH8jdz3H777ePee++N4447zh2yqdPf/va3Wm9MunbtGj179sxwIgrZEUccEd///vejW7dukcvl4pFHHokePXpkPdZGFXVU9enTJ7p16xb77LNPrcs0i/2z/6ht3WdFVlZWRq9evaJ9+/b+vPw/N3NkU+y6666xePHi2H333SPi43NY27Rpk/FUFKoLLrggHn300XjyySejpKQkTjnllPjOd76T9VgbVdTnVH33u9+NoUOHrrfn4ZNXBcKTTz650eeL+c/L5ZdfHiUlJTFr1qw477zzYtKkSbHHHns4nEOdvve978UzzzwTBx54YDRp0iTmz58fpaWlseOOO0ZEcb9B4YuhqKPqhBNOiLvvvjvrMaDBcjNHNoU3KHzRFXVU/fznP4+Kioro0qVLfOlLX8o//snPMgM27tO3JYkI550BRamo305WVVVFixYt4umnn671uKiCz8ZtSQD+paj3VG3MRRddFJdccknWY0BB69atWzzwwAOuoAWIiEZZD1ConnvuuaxHgIK37rYkABT54T9g87gtCcC/iCrg3zZ+/PgYNWqUE9MBQlQBm6Fly5Yu7AD4f6JqA5y/D/X72te+FmeffbbbkgCEqNqgzp07Zz0CFDy3JQH4l6K+pcJbb70Vo0ePjrfeeivuvPPOOPfcc+PSSy+NXXfdNevRAIAGpqhvqTBmzJg49dRTY5tttonS0tLo2bNnjBw5MuuxoOCdfvrpERHRtWvX+M53vpP/r2vXrnHUUUdlPB1ANoo6qt5777047LDD8neBHjhwYKxYsSLrsaDg/eIXv4iIj8+puuOOO2LChAlxxx13xB133OFKQKBoFfU5Vc2aNYt33nknSkpKIiJi3rx5sdVWW2U8FRS+n/3sZ7Fw4cIoLy+PhQsX5h9fu3Zt7LTTThlOBpCdoj6nasGCBXHRRRfFG2+8EbvttlssW7YsrrnmmvjGN76R9WhQ0FasWBHvv/9+jB07NkaPHp1/vEmTJtG6deto0qSo368BRaqooyoiYs2aNfH666/H2rVrY6+99rKnCgD4txT128kLLrig1nZJSUk0a9Ys9t577zjuuOMEFgDwmRX1ieqNGzeOFStWxFFHHRVHHXVUrFq1KpYuXRqvvfZaXHzxxVmPBwA0IEW9p2rhwoVx33335be7du0axx13XFx77bXRu3fvDCcDABqaot5T9eGHH0ZFRUV+e+nSpbFq1aqI+PgqJgCAz6qo91SdffbZ0b9//zjggAOipqYmnnvuuRg1alRcf/31PqYGANgkRX3136JFi6KsrCzmz58fjRo1igMOOCB22GGHeP/996NVq1ZZjwcANCBFHVU9evSIRx55JOsxAIAvgKI+/NeuXbu44YYbYv/9949mzZrlHz/ooIMynAoAaIiKek/V4MGD13uspKQkJkyYkME0AEBDVtRRBQCQSlEf/hs8eHD+w5Q/yZ4qAGBTFXVUnX322fmvq6urY+bMmbHttttmOBEA0FA5/Pcpxx13XNxzzz1ZjwEANDBFvafq7bffzn+dy+XipZdeivfffz+7gQCABquoo+qkk06KkpKSyOVy0ahRo9h+++3joosuynosAKABKurP/hs/fnwMGjQopk2bFrvttlu88cYbWY8EADRQRR1VY8eOjX322SemT58ezZo1iylTpsS1116b9VgAQANU1FFVU1MThx12WMyePTu6desWO+20U6xduzbrsQCABqioo6p58+bx61//Ov7617/GkUceGRMmTIhtttkm67EAgAaoqKPqqquuig8//DCuu+662G677WLJkiVx9dVXZz0WANAAuU8VAEACRb2nCgAgFVEFAJCAqAIASEBUAQAkIKoAABL4P1yBNiq0tCLrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "datas=data.drop(['surge_multiplier','clouds','humidity','time_stamp','temp','pressure','destination','source','product_id','wind'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1#1st try the above\n",
    "#2nd drop the below ones as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.drop(['destination','source','product_id'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125120, 4)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>name</th>\n",
       "      <th>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.545962</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.741620</td>\n",
       "      <td>Uber</td>\n",
       "      <td>Black</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.462874</td>\n",
       "      <td>Uber</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.663325</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lux Black</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.473092</td>\n",
       "      <td>Uber</td>\n",
       "      <td>UberXL</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type       name RideCategory\n",
       "0  1.545962     Lyft    Lyft XL     moderate\n",
       "1  0.741620     Uber      Black     moderate\n",
       "2  1.462874     Uber       Taxi      unknown\n",
       "3  0.663325     Lyft  Lux Black     moderate\n",
       "4  1.473092     Uber     UberXL     moderate"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.head()"
   ]
  },
  {
   "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": 71,
   "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>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</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>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</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>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</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>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</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>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</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>cheap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type    time_stamp         destination  \\\n",
       "0  0.787401     Uber  1.544770e+12            West End   \n",
       "1  1.506652     Uber  1.543400e+12   Boston University   \n",
       "2  1.414214     Lyft  1.544920e+12            Back Bay   \n",
       "3  1.994994     Lyft  1.543620e+12  Financial District   \n",
       "4  1.220656     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",
       "                             product_id      name RideCategory  \n",
       "0  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a      Taxi      unknown  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2  UberPool        cheap  \n",
       "2                                  lyft      Lyft     moderate  \n",
       "3                             lyft_plus   Lyft XL    expensive  \n",
       "4                                  lyft      Lyft        cheap  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193956"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(535231, 9)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## apply new feature again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi=taxi.drop(['surge_multiplier','time_stamp'],axis=1)#try #'destination','source','product_id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</td>\n",
       "      <td>Uber</td>\n",
       "      <td>West End</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</td>\n",
       "      <td>Uber</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type         destination                   source  \\\n",
       "0  0.787401     Uber            West End         Haymarket Square   \n",
       "1  1.506652     Uber   Boston University              Beacon Hill   \n",
       "2  1.414214     Lyft            Back Bay         Haymarket Square   \n",
       "3  1.994994     Lyft  Financial District  Northeastern University   \n",
       "4  1.220656     Lyft            Back Bay  Northeastern University   \n",
       "\n",
       "                             product_id      name RideCategory  \n",
       "0  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a      Taxi      unknown  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2  UberPool        cheap  \n",
       "2                                  lyft      Lyft     moderate  \n",
       "3                             lyft_plus   Lyft XL    expensive  \n",
       "4                                  lyft      Lyft        cheap  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</td>\n",
       "      <td>Uber</td>\n",
       "      <td>West End</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</td>\n",
       "      <td>Uber</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type         destination                   source  \\\n",
       "0  0.787401     Uber            West End         Haymarket Square   \n",
       "1  1.506652     Uber   Boston University              Beacon Hill   \n",
       "2  1.414214     Lyft            Back Bay         Haymarket Square   \n",
       "3  1.994994     Lyft  Financial District  Northeastern University   \n",
       "4  1.220656     Lyft            Back Bay  Northeastern University   \n",
       "\n",
       "                             product_id      name RideCategory  \n",
       "0  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a      Taxi      unknown  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2  UberPool        cheap  \n",
       "2                                  lyft      Lyft     moderate  \n",
       "3                             lyft_plus   Lyft XL    expensive  \n",
       "4                                  lyft      Lyft        cheap  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=taxi.copy()\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "d=pd.get_dummies(data=taxi,columns=['name','destination','source','product_id','cab_type'])\n",
    "lbl_Ridge=LabelEncoder()\n",
    "d['RideCategory']=lbl_Ridge.fit_transform(d['RideCategory'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>RideCategory</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>...</th>\n",
       "      <th>product_id_997acbb5-e102-41e1-b155-9df7de0a73f2</th>\n",
       "      <th>product_id_9a0e7b09-b92b-4c41-9779-2ad22b4d779d</th>\n",
       "      <th>product_id_lyft</th>\n",
       "      <th>product_id_lyft_line</th>\n",
       "      <th>product_id_lyft_lux</th>\n",
       "      <th>product_id_lyft_luxsuv</th>\n",
       "      <th>product_id_lyft_plus</th>\n",
       "      <th>product_id_lyft_premier</th>\n",
       "      <th>cab_type_Lyft</th>\n",
       "      <th>cab_type_Uber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</td>\n",
       "      <td>3</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</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>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</td>\n",
       "      <td>2</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance  RideCategory  name_Black  name_Black SUV  name_Lux  \\\n",
       "0  0.787401             3           0               0         0   \n",
       "1  1.506652             0           0               0         0   \n",
       "2  1.414214             2           0               0         0   \n",
       "3  1.994994             1           0               0         0   \n",
       "4  1.220656             0           0               0         0   \n",
       "\n",
       "   name_Lux Black  name_Lux Black XL  name_Lyft  name_Lyft XL  name_Shared  \\\n",
       "0               0                  0          0             0            0   \n",
       "1               0                  0          0             0            0   \n",
       "2               0                  0          1             0            0   \n",
       "3               0                  0          0             1            0   \n",
       "4               0                  0          1             0            0   \n",
       "\n",
       "   ...  product_id_997acbb5-e102-41e1-b155-9df7de0a73f2  \\\n",
       "0  ...                                                0   \n",
       "1  ...                                                1   \n",
       "2  ...                                                0   \n",
       "3  ...                                                0   \n",
       "4  ...                                                0   \n",
       "\n",
       "   product_id_9a0e7b09-b92b-4c41-9779-2ad22b4d779d  product_id_lyft  \\\n",
       "0                                                0                0   \n",
       "1                                                0                0   \n",
       "2                                                0                1   \n",
       "3                                                0                0   \n",
       "4                                                0                1   \n",
       "\n",
       "   product_id_lyft_line  product_id_lyft_lux  product_id_lyft_luxsuv  \\\n",
       "0                     0                    0                       0   \n",
       "1                     0                    0                       0   \n",
       "2                     0                    0                       0   \n",
       "3                     0                    0                       0   \n",
       "4                     0                    0                       0   \n",
       "\n",
       "   product_id_lyft_plus  product_id_lyft_premier  cab_type_Lyft  cab_type_Uber  \n",
       "0                     0                        0              0              1  \n",
       "1                     0                        0              0              1  \n",
       "2                     0                        0              1              0  \n",
       "3                     1                        0              1              0  \n",
       "4                     0                        0              1              0  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unknown =3\n",
    "#cheap = 0\n",
    "#moderate=2\n",
    "#epensive=1\n",
    "#very expensive=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    173014\n",
       "0    150920\n",
       "1    123318\n",
       "4     44180\n",
       "3     43799\n",
       "Name: RideCategory, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['RideCategory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>RideCategory</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>...</th>\n",
       "      <th>product_id_997acbb5-e102-41e1-b155-9df7de0a73f2</th>\n",
       "      <th>product_id_9a0e7b09-b92b-4c41-9779-2ad22b4d779d</th>\n",
       "      <th>product_id_lyft</th>\n",
       "      <th>product_id_lyft_line</th>\n",
       "      <th>product_id_lyft_lux</th>\n",
       "      <th>product_id_lyft_luxsuv</th>\n",
       "      <th>product_id_lyft_plus</th>\n",
       "      <th>product_id_lyft_premier</th>\n",
       "      <th>cab_type_Lyft</th>\n",
       "      <th>cab_type_Uber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</td>\n",
       "      <td>3</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</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>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</td>\n",
       "      <td>2</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>1.280625</td>\n",
       "      <td>2</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554452</th>\n",
       "      <td>0.959166</td>\n",
       "      <td>1</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554453</th>\n",
       "      <td>1.630951</td>\n",
       "      <td>2</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554454</th>\n",
       "      <td>1.726268</td>\n",
       "      <td>2</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554455</th>\n",
       "      <td>1.382027</td>\n",
       "      <td>2</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>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>535231 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance  RideCategory  name_Black  name_Black SUV  name_Lux  \\\n",
       "0       0.787401             3           0               0         0   \n",
       "1       1.506652             0           0               0         0   \n",
       "2       1.414214             2           0               0         0   \n",
       "3       1.994994             1           0               0         0   \n",
       "4       1.220656             0           0               0         0   \n",
       "...          ...           ...         ...             ...       ...   \n",
       "554451  1.280625             2           0               0         0   \n",
       "554452  0.959166             1           0               1         0   \n",
       "554453  1.630951             2           0               0         0   \n",
       "554454  1.726268             2           0               0         0   \n",
       "554455  1.382027             2           0               0         0   \n",
       "\n",
       "        name_Lux Black  name_Lux Black XL  name_Lyft  name_Lyft XL  \\\n",
       "0                    0                  0          0             0   \n",
       "1                    0                  0          0             0   \n",
       "2                    0                  0          1             0   \n",
       "3                    0                  0          0             1   \n",
       "4                    0                  0          1             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_Shared  ...  product_id_997acbb5-e102-41e1-b155-9df7de0a73f2  \\\n",
       "0                 0  ...                                                0   \n",
       "1                 0  ...                                                1   \n",
       "2                 0  ...                                                0   \n",
       "3                 0  ...                                                0   \n",
       "4                 0  ...                                                0   \n",
       "...             ...  ...                                              ...   \n",
       "554451            0  ...                                                0   \n",
       "554452            0  ...                                                0   \n",
       "554453            0  ...                                                0   \n",
       "554454            0  ...                                                0   \n",
       "554455            0  ...                                                0   \n",
       "\n",
       "        product_id_9a0e7b09-b92b-4c41-9779-2ad22b4d779d  product_id_lyft  \\\n",
       "0                                                     0                0   \n",
       "1                                                     0                0   \n",
       "2                                                     0                1   \n",
       "3                                                     0                0   \n",
       "4                                                     0                1   \n",
       "...                                                 ...              ...   \n",
       "554451                                                0                0   \n",
       "554452                                                0                0   \n",
       "554453                                                0                0   \n",
       "554454                                                0                0   \n",
       "554455                                                0                0   \n",
       "\n",
       "        product_id_lyft_line  product_id_lyft_lux  product_id_lyft_luxsuv  \\\n",
       "0                          0                    0                       0   \n",
       "1                          0                    0                       0   \n",
       "2                          0                    0                       0   \n",
       "3                          0                    0                       0   \n",
       "4                          0                    0                       0   \n",
       "...                      ...                  ...                     ...   \n",
       "554451                     0                    0                       0   \n",
       "554452                     0                    0                       0   \n",
       "554453                     0                    0                       0   \n",
       "554454                     0                    0                       0   \n",
       "554455                     0                    0                       0   \n",
       "\n",
       "        product_id_lyft_plus  product_id_lyft_premier  cab_type_Lyft  \\\n",
       "0                          0                        0              0   \n",
       "1                          0                        0              0   \n",
       "2                          0                        0              1   \n",
       "3                          1                        0              1   \n",
       "4                          0                        0              1   \n",
       "...                      ...                      ...            ...   \n",
       "554451                     0                        0              0   \n",
       "554452                     0                        0              0   \n",
       "554453                     0                        0              0   \n",
       "554454                     0                        0              0   \n",
       "554455                     0                        0              0   \n",
       "\n",
       "        cab_type_Uber  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  \n",
       "...               ...  \n",
       "554451              1  \n",
       "554452              1  \n",
       "554453              1  \n",
       "554454              1  \n",
       "554455              1  \n",
       "\n",
       "[535231 rows x 54 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=d.drop('RideCategory',axis=1)\n",
    "y=d[['RideCategory']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>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_Taxi</th>\n",
       "      <th>...</th>\n",
       "      <th>product_id_997acbb5-e102-41e1-b155-9df7de0a73f2</th>\n",
       "      <th>product_id_9a0e7b09-b92b-4c41-9779-2ad22b4d779d</th>\n",
       "      <th>product_id_lyft</th>\n",
       "      <th>product_id_lyft_line</th>\n",
       "      <th>product_id_lyft_lux</th>\n",
       "      <th>product_id_lyft_luxsuv</th>\n",
       "      <th>product_id_lyft_plus</th>\n",
       "      <th>product_id_lyft_premier</th>\n",
       "      <th>cab_type_Lyft</th>\n",
       "      <th>cab_type_Uber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</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>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>1.280625</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554452</th>\n",
       "      <td>0.959166</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554453</th>\n",
       "      <td>1.630951</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554454</th>\n",
       "      <td>1.726268</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554455</th>\n",
       "      <td>1.382027</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>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>535231 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance  name_Black  name_Black SUV  name_Lux  name_Lux Black  \\\n",
       "0       0.787401           0               0         0               0   \n",
       "1       1.506652           0               0         0               0   \n",
       "2       1.414214           0               0         0               0   \n",
       "3       1.994994           0               0         0               0   \n",
       "4       1.220656           0               0         0               0   \n",
       "...          ...         ...             ...       ...             ...   \n",
       "554451  1.280625           0               0         0               0   \n",
       "554452  0.959166           0               1         0               0   \n",
       "554453  1.630951           0               0         0               0   \n",
       "554454  1.726268           0               0         0               0   \n",
       "554455  1.382027           0               0         0               0   \n",
       "\n",
       "        name_Lux Black XL  name_Lyft  name_Lyft XL  name_Shared  name_Taxi  \\\n",
       "0                       0          0             0            0          1   \n",
       "1                       0          0             0            0          0   \n",
       "2                       0          1             0            0          0   \n",
       "3                       0          0             1            0          0   \n",
       "4                       0          1             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",
       "        ...  product_id_997acbb5-e102-41e1-b155-9df7de0a73f2  \\\n",
       "0       ...                                                0   \n",
       "1       ...                                                1   \n",
       "2       ...                                                0   \n",
       "3       ...                                                0   \n",
       "4       ...                                                0   \n",
       "...     ...                                              ...   \n",
       "554451  ...                                                0   \n",
       "554452  ...                                                0   \n",
       "554453  ...                                                0   \n",
       "554454  ...                                                0   \n",
       "554455  ...                                                0   \n",
       "\n",
       "        product_id_9a0e7b09-b92b-4c41-9779-2ad22b4d779d  product_id_lyft  \\\n",
       "0                                                     0                0   \n",
       "1                                                     0                0   \n",
       "2                                                     0                1   \n",
       "3                                                     0                0   \n",
       "4                                                     0                1   \n",
       "...                                                 ...              ...   \n",
       "554451                                                0                0   \n",
       "554452                                                0                0   \n",
       "554453                                                0                0   \n",
       "554454                                                0                0   \n",
       "554455                                                0                0   \n",
       "\n",
       "        product_id_lyft_line  product_id_lyft_lux  product_id_lyft_luxsuv  \\\n",
       "0                          0                    0                       0   \n",
       "1                          0                    0                       0   \n",
       "2                          0                    0                       0   \n",
       "3                          0                    0                       0   \n",
       "4                          0                    0                       0   \n",
       "...                      ...                  ...                     ...   \n",
       "554451                     0                    0                       0   \n",
       "554452                     0                    0                       0   \n",
       "554453                     0                    0                       0   \n",
       "554454                     0                    0                       0   \n",
       "554455                     0                    0                       0   \n",
       "\n",
       "        product_id_lyft_plus  product_id_lyft_premier  cab_type_Lyft  \\\n",
       "0                          0                        0              0   \n",
       "1                          0                        0              0   \n",
       "2                          0                        0              1   \n",
       "3                          1                        0              1   \n",
       "4                          0                        0              1   \n",
       "...                      ...                      ...            ...   \n",
       "554451                     0                        0              0   \n",
       "554452                     0                        0              0   \n",
       "554453                     0                        0              0   \n",
       "554454                     0                        0              0   \n",
       "554455                     0                        0              0   \n",
       "\n",
       "        cab_type_Uber  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  \n",
       "...               ...  \n",
       "554451              1  \n",
       "554452              1  \n",
       "554453              1  \n",
       "554454              1  \n",
       "554455              1  \n",
       "\n",
       "[535231 rows x 53 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554451</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554452</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554453</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554454</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554455</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>535231 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RideCategory\n",
       "0                  3\n",
       "1                  0\n",
       "2                  2\n",
       "3                  1\n",
       "4                  0\n",
       "...              ...\n",
       "554451             2\n",
       "554452             1\n",
       "554453             2\n",
       "554454             2\n",
       "554455             2\n",
       "\n",
       "[535231 rows x 1 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,shuffle=True,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: (428184, 53)\n",
      "test: (107047, 53)\n"
     ]
    }
   ],
   "source": [
    "print('train:',x_train.shape)\n",
    "print('test:',x_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training phase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## we will use \n",
    "- Decision tree \n",
    "- Random forest \n",
    "- Adaboost\n",
    "- logistic rgression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "import time\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training time:  4.084328651428223 seconds\n",
      "Total testing time:  0.06678962707519531 seconds\n",
      "trainning acc=90.26026194346356 %\n",
      "testing acc=90.1370426074528 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training the model\n",
    "model_tree = DecisionTreeClassifier(criterion='entropy',max_depth=25,min_samples_leaf=20,min_samples_split=50,random_state=42)\n",
    "\n",
    "start=time.time()\n",
    "model_tree.fit(x_train,y_train)\n",
    "end=time.time()\n",
    "Total_training_time=end- start\n",
    "print(\"Total training time: \", Total_training_time, \"seconds\")\n",
    "\n",
    "# Saving the model to decision_tree.pkl\n",
    "joblib.dump(model_tree, 'decision_tree.pkl')\n",
    "\n",
    "# Making predictions\n",
    "y_pred_train_dtree=model_trees.predict(x_train)\n",
    "\n",
    "start=time.time()\n",
    "y_pred_test_dtree=model_trees.predict(x_test)\n",
    "end=time.time()\n",
    "Total_testing_time=end- start\n",
    "print(\"Total testing time: \", Total_testing_time, \"seconds\")\n",
    "\n",
    "# Calculating accuracies\n",
    "t_acc=accuracy_score(y_train,y_pred_train_dtree)\n",
    "v_acc=accuracy_score(y_test,y_pred_test_dtree)\n",
    "\n",
    "print('trainning acc={} %'.format(t_acc*100))\n",
    "print('testing acc={} %'.format(v_acc*100))\n",
    "\n",
    "#visualize \n",
    "bars=pd.DataFrame({'classification acuuracy':[t_acc],'total training time':[Total_training_time],'total test time':[Total_testing_time]})\n",
    "sns.barplot(data=bars)\n",
    "plt.title(\"Decision tree\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training time:  9.287339210510254 seconds\n",
      "Total testing time:  0.39049863815307617 seconds\n",
      "trainning acc=89.79153821721503 %\n",
      "testing acc=89.83063514157332 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_forest = RandomForestClassifier(n_estimators=15, min_samples_split= 150 , min_samples_leaf= 50 , random_state=42,max_depth=20)\n",
    "\n",
    "start=time.time()\n",
    "model_forest.fit(x_train ,y_train )\n",
    "end=time.time()\n",
    "Total_training_time=end- start\n",
    "print(\"Total training time: \", Total_training_time, \"seconds\")\n",
    "\n",
    "# Saving the model to Random_forest.pkl\n",
    "joblib.dump(model_forest, 'Random_forest.pkl')\n",
    "#model_forests=joblib.load('Random_forest.pkl')\n",
    "\n",
    "# Making predictions\n",
    "y_pred_train_forest=model_forest.predict(x_train)\n",
    "\n",
    "start=time.time()\n",
    "y_pred_test_forest=model_forest.predict(x_test)\n",
    "end=time.time()\n",
    "Total_testing_time=end- start\n",
    "print(\"Total testing time: \", Total_testing_time, \"seconds\")\n",
    "\n",
    "# Calculating accuracies\n",
    "t_acc=accuracy_score(y_train,y_pred_train_forest)\n",
    "v_acc=accuracy_score(y_test,y_pred_test_forest)\n",
    "\n",
    "print('trainning acc={} %'.format(t_acc*100))\n",
    "print('testing acc={} %'.format(v_acc*100))\n",
    "\n",
    "#visualize \n",
    "bars=pd.DataFrame({'classification acuuracy':[t_acc],'total training time':[Total_training_time],'total test time':[Total_testing_time]})\n",
    "sns.barplot(data=bars)\n",
    "plt.title(\"Random_forest\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training time:  25.067487001419067 seconds\n",
      "Total testing time:  0.40131330490112305 seconds\n",
      "trainning acc=90.40926330736319 %\n",
      "testing acc=89.86706773660168 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_adaboost=AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=20),n_estimators=5)\n",
    "\n",
    "start=time.time()\n",
    "model_adaboost.fit(x_train ,y_train )\n",
    "end=time.time()\n",
    "Total_training_time=end- start\n",
    "print(\"Total training time: \", Total_training_time, \"seconds\")\n",
    "\n",
    "# Saving the model to Random_forest.pkl\n",
    "joblib.dump(model_adaboost, 'Random_adaboost.pkl')\n",
    "#model_adaboosts=joblib.load('Random_adaboost.pkl')\n",
    "\n",
    "# Making predictions\n",
    "y_pred_train_adaboost=model_adaboost.predict(x_train)\n",
    "\n",
    "start=time.time()\n",
    "y_pred_test_adaboost=model_adaboost.predict(x_test)\n",
    "end=time.time()\n",
    "Total_testing_time=end- start\n",
    "print(\"Total testing time: \", Total_testing_time, \"seconds\")\n",
    "\n",
    "# Calculating accuracies\n",
    "t_acc=accuracy_score(y_train,y_pred_train_adaboost)\n",
    "v_acc=accuracy_score(y_test,y_pred_test_adaboost)\n",
    "\n",
    "print('trainning acc={} %'.format(t_acc*100))\n",
    "print('testing acc={} %'.format(v_acc*100))\n",
    "\n",
    "#visualize \n",
    "bars=pd.DataFrame({'classification acuuracy':[t_acc],'total training time':[Total_training_time],'total test time':[Total_testing_time]})\n",
    "sns.barplot(data=bars)\n",
    "plt.title(\"AdaBoost\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training time:  27.39924192428589 seconds\n",
      "Total testing time:  0.04886984825134277 seconds\n",
      "trainning acc=88.87230723240475 %\n",
      "testing acc=88.99735630143769 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_log=LogisticRegression(max_iter=100,C=10)\n",
    "\n",
    "start=time.time()\n",
    "model_log.fit(x_train ,y_train )\n",
    "end=time.time()\n",
    "Total_training_time=end- start\n",
    "\n",
    "# Saving the model to Random_forest.pkl\n",
    "joblib.dump(model_log, 'model_log.pkl')\n",
    "#model_logs=joblib.load('model_log.pkl')\n",
    "\n",
    "print(\"Total training time: \", Total_training_time, \"seconds\")\n",
    "\n",
    "# Making predictions\n",
    "y_pred_train_model_log=model_log.predict(x_train)\n",
    "\n",
    "start=time.time()\n",
    "y_pred_test_model_log=model_log.predict(x_test)\n",
    "end=time.time()\n",
    "Total_testing_time=end- start\n",
    "print(\"Total testing time: \", Total_testing_time, \"seconds\")\n",
    "\n",
    "# Calculating accuracies\n",
    "t_acc=accuracy_score(y_train,y_pred_train_model_log)\n",
    "v_acc=accuracy_score(y_test,y_pred_test_model_log)\n",
    "\n",
    "print('trainning acc={} %'.format(t_acc*100))\n",
    "print('testing acc={} %'.format(v_acc*100))\n",
    "\n",
    "#visualize \n",
    "bars=pd.DataFrame({'classification acuuracy':[t_acc],'total training time':[Total_training_time],'total test time':[Total_testing_time]})\n",
    "sns.barplot(data=bars)\n",
    "plt.title(\"Logistic Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# hyper parameter tuning phase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree hyper parameter tuning\n",
    "|max depth | min_samples_split| train accuracy|test accuracy|\n",
    "|----------|------------------|---------------|-------------|\n",
    "|10 |200 | 84.56%|84.68%|\n",
    "|10 |100 | 84.57%|84.67%|\n",
    "|10 |50  | 84.57%|84.67%|\n",
    "|15 |50  | 89.31%|89.39%|\n",
    "|20 |50  | 89.94%|89.93%|\n",
    "|25 |50  |90.17 %|90.06%|\n",
    "|-----------------------|\n",
    "|**conclusion**|\n",
    "| the best combination of the two hyperparamters is max_depth=`25` and min_samples_split= `50` which result in training accuracy of `90.17%` and testing accuracy of `90.06%`|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoostClassifier hyper parameter tuning\n",
    "|base_estimator | n_estimators| train accuracy|test accuracy|\n",
    "|----------|------------------|---------------|-------------|\n",
    "|DecisionTreeClassifier(max_depth=20)|5 | 90.43%|89.90%|\n",
    "|LogisticRegression               |5  | 49.78%|49.80%|\n",
    "|GaussianNB             |5  |      57.01% |56.89%|\n",
    "|LogisticRegression(max_iter=500) |10 | 59.98%|59.94%|\n",
    "|LogisticRegression(max_iter=500) |15 | 70.98%|71.02%|\n",
    "|LogisticRegression(max_iter=500) |25 |71.13 %|71.18%|\n",
    "|-----------------------|\n",
    "|**conclusion**|\n",
    "| the best combination of the two hyperparamters is `base_estimator` = `DecisionTreeClassifier(max_depth=20)` and `n_estimators` = `5` which result in training accuracy of `90.43%` and testing accuracy of `89.90%`|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForest hyper parameter tuning\n",
    "|max_depth | n_estimators| train accuracy|test accuracy|\n",
    "|----------|------------------|---------------|-------------|\n",
    "| 10 |5  | 86.92%|87.07%|\n",
    "| 50 |5  | 89.82%|89.81%|\n",
    "|5 |5  | 67.65%|67.65%|\n",
    "|20  |10 | 89.80%|89.84%|\n",
    "| 20 |15 | 89.84%|89.83%|\n",
    "| 20 |30 | 89.79%|89.83%|\n",
    "|-----------------------|\n",
    "|**conclusion**|\n",
    "| the best combination of the two hyperparamters is `max_depth` = `20` and `n_estimators` = `15` which result in training accuracy of `89.84%` and testing accuracy of `89.83%`|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LogisticRegression hyper parameter tuning\n",
    "|max_iter | C| train accuracy|test accuracy|\n",
    "|----------|------------------|---------------|-------------|\n",
    "|100 |10 | 88.87%|88.99%|\n",
    "|100 |0.1 | 88.76%|88.89%|\n",
    "|100 |0.0001 | 74.15%|74.12%|\n",
    "|300      |0.001  | 85.03%|85.11%|\n",
    "|500 |0.001  | 85.03%|85.11%|\n",
    "|1000     |0.001  |85.03 %|85.11%|\n",
    "|-----------------------|\n",
    "|**conclusion**|\n",
    "| the best combination of the two hyperparamters is max_iter=`100` and C= `10` which result in training accuracy of `88.87%` and testing accuracy of `88.99%`|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>RideCategory</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>expensive</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>cheap</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>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.91</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1.544920e+12</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>1</td>\n",
       "      <td>0be75a58-949c-47b4-98ed-37de32b4bc5d</td>\n",
       "      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</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",
       "3      0.91     Uber  1.544920e+12    Beacon Hill  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",
       "3                 1  0be75a58-949c-47b4-98ed-37de32b4bc5d   \n",
       "\n",
       "                             product_id          name RideCategory  \n",
       "0                           lyft_luxsuv  Lux Black XL    expensive  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2      UberPool        cheap  \n",
       "2                             lyft_plus       Lyft XL        cheap  \n",
       "3  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a          Taxi      unknown  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_sample=pd.read_csv(r'F:\\mohamed\\3rd year\\semester 2\\machine learning\\project\\ProjectTestSamples\\Milestone 2\\taxi-test-samples.csv')\n",
    "taxi_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   distance          4 non-null      float64\n",
      " 1   cab_type          4 non-null      object \n",
      " 2   time_stamp        4 non-null      float64\n",
      " 3   destination       4 non-null      object \n",
      " 4   source            4 non-null      object \n",
      " 5   surge_multiplier  4 non-null      int64  \n",
      " 6   id                4 non-null      object \n",
      " 7   product_id        4 non-null      object \n",
      " 8   name              4 non-null      object \n",
      " 9   RideCategory      4 non-null      object \n",
      "dtypes: float64(2), int64(1), object(7)\n",
      "memory usage: 448.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "taxi_sample.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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",
       "RideCategory        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_sample.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#incase of null values #categorical\n",
    "#taxi_sample.fillna(taxi_sample['destination'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#incase of null values #continuous\n",
    "#taxi_sample.fillna(taxi_sample['distance'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "taxi_sample=taxi_sample.drop(['surge_multiplier','time_stamp','id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.columns == taxi.columns"
   ]
  },
  {
   "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": 109,
   "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>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</td>\n",
       "      <td>Uber</td>\n",
       "      <td>West End</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</td>\n",
       "      <td>Uber</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>cheap</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535230</th>\n",
       "      <td>1.382027</td>\n",
       "      <td>Uber</td>\n",
       "      <td>Theatre District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>6f72dfc5-27f1-42e8-84db-ccc7a75f6969</td>\n",
       "      <td>UberXL</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535231</th>\n",
       "      <td>2.140000</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>South Station</td>\n",
       "      <td>North Station</td>\n",
       "      <td>lyft_luxsuv</td>\n",
       "      <td>Lux Black XL</td>\n",
       "      <td>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535232</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>Uber</td>\n",
       "      <td>North End</td>\n",
       "      <td>North Station</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535233</th>\n",
       "      <td>0.570000</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>North Station</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535234</th>\n",
       "      <td>0.910000</td>\n",
       "      <td>Uber</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>535235 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance cab_type         destination                   source  \\\n",
       "0       0.787401     Uber            West End         Haymarket Square   \n",
       "1       1.506652     Uber   Boston University              Beacon Hill   \n",
       "2       1.414214     Lyft            Back Bay         Haymarket Square   \n",
       "3       1.994994     Lyft  Financial District  Northeastern University   \n",
       "4       1.220656     Lyft            Back Bay  Northeastern University   \n",
       "...          ...      ...                 ...                      ...   \n",
       "535230  1.382027     Uber    Theatre District  Northeastern University   \n",
       "535231  2.140000     Lyft       South Station            North Station   \n",
       "535232  1.080000     Uber           North End            North Station   \n",
       "535233  0.570000     Lyft       North Station         Haymarket Square   \n",
       "535234  0.910000     Uber         Beacon Hill         Haymarket Square   \n",
       "\n",
       "                                  product_id          name RideCategory  \n",
       "0       8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a          Taxi      unknown  \n",
       "1       997acbb5-e102-41e1-b155-9df7de0a73f2      UberPool        cheap  \n",
       "2                                       lyft          Lyft     moderate  \n",
       "3                                  lyft_plus       Lyft XL    expensive  \n",
       "4                                       lyft          Lyft        cheap  \n",
       "...                                      ...           ...          ...  \n",
       "535230  6f72dfc5-27f1-42e8-84db-ccc7a75f6969        UberXL     moderate  \n",
       "535231                           lyft_luxsuv  Lux Black XL    expensive  \n",
       "535232  997acbb5-e102-41e1-b155-9df7de0a73f2      UberPool        cheap  \n",
       "535233                             lyft_plus       Lyft XL        cheap  \n",
       "535234  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a          Taxi      unknown  \n",
       "\n",
       "[535235 rows x 7 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data=pd.concat([taxi,taxi_sample],axis=0,ignore_index=True)\n",
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unknown =3\n",
    "#cheap = 0 \n",
    "#moderate=2\n",
    "#epensive=1\n",
    "#very expensive=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.787401</td>\n",
       "      <td>Uber</td>\n",
       "      <td>West End</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.506652</td>\n",
       "      <td>Uber</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.414214</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.994994</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>expensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220656</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance cab_type         destination                   source  \\\n",
       "0  0.787401     Uber            West End         Haymarket Square   \n",
       "1  1.506652     Uber   Boston University              Beacon Hill   \n",
       "2  1.414214     Lyft            Back Bay         Haymarket Square   \n",
       "3  1.994994     Lyft  Financial District  Northeastern University   \n",
       "4  1.220656     Lyft            Back Bay  Northeastern University   \n",
       "\n",
       "                             product_id      name RideCategory  \n",
       "0  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a      Taxi      unknown  \n",
       "1  997acbb5-e102-41e1-b155-9df7de0a73f2  UberPool        cheap  \n",
       "2                                  lyft      Lyft     moderate  \n",
       "3                             lyft_plus   Lyft XL    expensive  \n",
       "4                                  lyft      Lyft        cheap  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=final_data.copy()\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "d=pd.get_dummies(data=d,columns=['name','destination','source','product_id','cab_type'])\n",
    "lbl_Ridge=LabelEncoder()\n",
    "d['RideCategory']=lbl_Ridge.fit_transform(d['RideCategory'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>RideCategory</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>...</th>\n",
       "      <th>product_id_997acbb5-e102-41e1-b155-9df7de0a73f2</th>\n",
       "      <th>product_id_9a0e7b09-b92b-4c41-9779-2ad22b4d779d</th>\n",
       "      <th>product_id_lyft</th>\n",
       "      <th>product_id_lyft_line</th>\n",
       "      <th>product_id_lyft_lux</th>\n",
       "      <th>product_id_lyft_luxsuv</th>\n",
       "      <th>product_id_lyft_plus</th>\n",
       "      <th>product_id_lyft_premier</th>\n",
       "      <th>cab_type_Lyft</th>\n",
       "      <th>cab_type_Uber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>535230</th>\n",
       "      <td>1.382027</td>\n",
       "      <td>2</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535231</th>\n",
       "      <td>2.140000</td>\n",
       "      <td>1</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535232</th>\n",
       "      <td>1.080000</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>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535233</th>\n",
       "      <td>0.570000</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535234</th>\n",
       "      <td>0.910000</td>\n",
       "      <td>3</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>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance  RideCategory  name_Black  name_Black SUV  name_Lux  \\\n",
       "535230  1.382027             2           0               0         0   \n",
       "535231  2.140000             1           0               0         0   \n",
       "535232  1.080000             0           0               0         0   \n",
       "535233  0.570000             0           0               0         0   \n",
       "535234  0.910000             3           0               0         0   \n",
       "\n",
       "        name_Lux Black  name_Lux Black XL  name_Lyft  name_Lyft XL  \\\n",
       "535230               0                  0          0             0   \n",
       "535231               0                  1          0             0   \n",
       "535232               0                  0          0             0   \n",
       "535233               0                  0          0             1   \n",
       "535234               0                  0          0             0   \n",
       "\n",
       "        name_Shared  ...  product_id_997acbb5-e102-41e1-b155-9df7de0a73f2  \\\n",
       "535230            0  ...                                                0   \n",
       "535231            0  ...                                                0   \n",
       "535232            0  ...                                                1   \n",
       "535233            0  ...                                                0   \n",
       "535234            0  ...                                                0   \n",
       "\n",
       "        product_id_9a0e7b09-b92b-4c41-9779-2ad22b4d779d  product_id_lyft  \\\n",
       "535230                                                0                0   \n",
       "535231                                                0                0   \n",
       "535232                                                0                0   \n",
       "535233                                                0                0   \n",
       "535234                                                0                0   \n",
       "\n",
       "        product_id_lyft_line  product_id_lyft_lux  product_id_lyft_luxsuv  \\\n",
       "535230                     0                    0                       0   \n",
       "535231                     0                    0                       1   \n",
       "535232                     0                    0                       0   \n",
       "535233                     0                    0                       0   \n",
       "535234                     0                    0                       0   \n",
       "\n",
       "        product_id_lyft_plus  product_id_lyft_premier  cab_type_Lyft  \\\n",
       "535230                     0                        0              0   \n",
       "535231                     0                        0              1   \n",
       "535232                     0                        0              0   \n",
       "535233                     1                        0              1   \n",
       "535234                     0                        0              0   \n",
       "\n",
       "        cab_type_Uber  \n",
       "535230              1  \n",
       "535231              0  \n",
       "535232              1  \n",
       "535233              0  \n",
       "535234              1  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>RideCategory</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>...</th>\n",
       "      <th>product_id_997acbb5-e102-41e1-b155-9df7de0a73f2</th>\n",
       "      <th>product_id_9a0e7b09-b92b-4c41-9779-2ad22b4d779d</th>\n",
       "      <th>product_id_lyft</th>\n",
       "      <th>product_id_lyft_line</th>\n",
       "      <th>product_id_lyft_lux</th>\n",
       "      <th>product_id_lyft_luxsuv</th>\n",
       "      <th>product_id_lyft_plus</th>\n",
       "      <th>product_id_lyft_premier</th>\n",
       "      <th>cab_type_Lyft</th>\n",
       "      <th>cab_type_Uber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>535231</th>\n",
       "      <td>2.14</td>\n",
       "      <td>1</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535232</th>\n",
       "      <td>1.08</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>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535233</th>\n",
       "      <td>0.57</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535234</th>\n",
       "      <td>0.91</td>\n",
       "      <td>3</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>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance  RideCategory  name_Black  name_Black SUV  name_Lux  \\\n",
       "535231      2.14             1           0               0         0   \n",
       "535232      1.08             0           0               0         0   \n",
       "535233      0.57             0           0               0         0   \n",
       "535234      0.91             3           0               0         0   \n",
       "\n",
       "        name_Lux Black  name_Lux Black XL  name_Lyft  name_Lyft XL  \\\n",
       "535231               0                  1          0             0   \n",
       "535232               0                  0          0             0   \n",
       "535233               0                  0          0             1   \n",
       "535234               0                  0          0             0   \n",
       "\n",
       "        name_Shared  ...  product_id_997acbb5-e102-41e1-b155-9df7de0a73f2  \\\n",
       "535231            0  ...                                                0   \n",
       "535232            0  ...                                                1   \n",
       "535233            0  ...                                                0   \n",
       "535234            0  ...                                                0   \n",
       "\n",
       "        product_id_9a0e7b09-b92b-4c41-9779-2ad22b4d779d  product_id_lyft  \\\n",
       "535231                                                0                0   \n",
       "535232                                                0                0   \n",
       "535233                                                0                0   \n",
       "535234                                                0                0   \n",
       "\n",
       "        product_id_lyft_line  product_id_lyft_lux  product_id_lyft_luxsuv  \\\n",
       "535231                     0                    0                       1   \n",
       "535232                     0                    0                       0   \n",
       "535233                     0                    0                       0   \n",
       "535234                     0                    0                       0   \n",
       "\n",
       "        product_id_lyft_plus  product_id_lyft_premier  cab_type_Lyft  \\\n",
       "535231                     0                        0              1   \n",
       "535232                     0                        0              0   \n",
       "535233                     1                        0              1   \n",
       "535234                     0                        0              0   \n",
       "\n",
       "        cab_type_Uber  \n",
       "535231              0  \n",
       "535232              1  \n",
       "535233              0  \n",
       "535234              1  \n",
       "\n",
       "[4 rows x 54 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=len(taxi_sample)\n",
    "tests=d.iloc[-l:,:]\n",
    "tests.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=tests.drop('RideCategory',axis=1)\n",
    "y=tests[['RideCategory']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# choose your model to load to predict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model_trees=joblib.load('decision_tree.pkl')\n",
    "#model_forests=joblib.load('Random_forest.pkl')\n",
    "#model_adaboosts=joblib.load('Random_adaboost.pkl')\n",
    "#model_logs=joblib.load('model_log.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_logs=joblib.load('model_log.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test acc=0.75\n"
     ]
    }
   ],
   "source": [
    "#predict tests\n",
    "y_test_dtree=model_logs.predict(x)\n",
    "# Calculating accuracies\n",
    "test_acc=accuracy_score(y,y_test_dtree)\n",
    "print('test acc={}'.format(test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 0, 0, 3])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_dtree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>RideCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>535231</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535232</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535233</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535234</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RideCategory\n",
       "535231             1\n",
       "535232             0\n",
       "535233             0\n",
       "535234             3"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}