CabFarePredictionAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style='color: Green'> UBER DATA ANALYSIS TO PREDICT THE PRICE </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](cabFare.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression,LinearRegression\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import gc\n",
    "import os\n",
    "import sys\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"weather.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"cab_rides.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cab_data = pd.read_csv(\"cab_rides.csv\")\n",
    "weather_data = pd.read_csv(\"weather.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>cab_type</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>price</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.44</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1544952607890</td>\n",
       "      <td>North Station</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>424553bb-7174-41ea-aeb4-fe06d4f4b9d7</td>\n",
       "      <td>lyft_line</td>\n",
       "      <td>Shared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.44</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1543284023677</td>\n",
       "      <td>North Station</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4bd23055-6827-41c6-b23b-3c491f24e74d</td>\n",
       "      <td>lyft_premier</td>\n",
       "      <td>Lux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.44</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1543366822198</td>\n",
       "      <td>North Station</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>981a3613-77af-4620-a42a-0c0866077d1e</td>\n",
       "      <td>lyft</td>\n",
       "      <td>Lyft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.44</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1543553582749</td>\n",
       "      <td>North Station</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>c2d88af2-d278-4bfd-a8d0-29ca77cc5512</td>\n",
       "      <td>lyft_luxsuv</td>\n",
       "      <td>Lux Black XL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.44</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>1543463360223</td>\n",
       "      <td>North Station</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>e0126e1f-8ca9-4f2e-82b3-50505a09db9a</td>\n",
       "      <td>lyft_plus</td>\n",
       "      <td>Lyft XL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693066</th>\n",
       "      <td>1.00</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1543708385534</td>\n",
       "      <td>North End</td>\n",
       "      <td>West End</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>616d3611-1820-450a-9845-a9ff304a4842</td>\n",
       "      <td>6f72dfc5-27f1-42e8-84db-ccc7a75f6969</td>\n",
       "      <td>UberXL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693067</th>\n",
       "      <td>1.00</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1543708385534</td>\n",
       "      <td>North End</td>\n",
       "      <td>West End</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>633a3fc3-1f86-4b9e-9d48-2b7132112341</td>\n",
       "      <td>55c66225-fbe7-4fd5-9072-eab1ece5e23e</td>\n",
       "      <td>UberX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693068</th>\n",
       "      <td>1.00</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1543708385534</td>\n",
       "      <td>North End</td>\n",
       "      <td>West End</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64d451d0-639f-47a4-9b7c-6fd92fbd264f</td>\n",
       "      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n",
       "      <td>Taxi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693069</th>\n",
       "      <td>1.00</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1543708385534</td>\n",
       "      <td>North End</td>\n",
       "      <td>West End</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>727e5f07-a96b-4ad1-a2c7-9abc3ad55b4e</td>\n",
       "      <td>6d318bcc-22a3-4af6-bddd-b409bfce1546</td>\n",
       "      <td>Black SUV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693070</th>\n",
       "      <td>1.00</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1543708385534</td>\n",
       "      <td>North End</td>\n",
       "      <td>West End</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>e7fdc087-fe86-40a5-a3c3-3b2a8badcbda</td>\n",
       "      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n",
       "      <td>UberPool</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>693071 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance cab_type     time_stamp    destination            source  \\\n",
       "0           0.44     Lyft  1544952607890  North Station  Haymarket Square   \n",
       "1           0.44     Lyft  1543284023677  North Station  Haymarket Square   \n",
       "2           0.44     Lyft  1543366822198  North Station  Haymarket Square   \n",
       "3           0.44     Lyft  1543553582749  North Station  Haymarket Square   \n",
       "4           0.44     Lyft  1543463360223  North Station  Haymarket Square   \n",
       "...          ...      ...            ...            ...               ...   \n",
       "693066      1.00     Uber  1543708385534      North End          West End   \n",
       "693067      1.00     Uber  1543708385534      North End          West End   \n",
       "693068      1.00     Uber  1543708385534      North End          West End   \n",
       "693069      1.00     Uber  1543708385534      North End          West End   \n",
       "693070      1.00     Uber  1543708385534      North End          West End   \n",
       "\n",
       "        price  surge_multiplier                                    id  \\\n",
       "0         5.0               1.0  424553bb-7174-41ea-aeb4-fe06d4f4b9d7   \n",
       "1        11.0               1.0  4bd23055-6827-41c6-b23b-3c491f24e74d   \n",
       "2         7.0               1.0  981a3613-77af-4620-a42a-0c0866077d1e   \n",
       "3        26.0               1.0  c2d88af2-d278-4bfd-a8d0-29ca77cc5512   \n",
       "4         9.0               1.0  e0126e1f-8ca9-4f2e-82b3-50505a09db9a   \n",
       "...       ...               ...                                   ...   \n",
       "693066   13.0               1.0  616d3611-1820-450a-9845-a9ff304a4842   \n",
       "693067    9.5               1.0  633a3fc3-1f86-4b9e-9d48-2b7132112341   \n",
       "693068    NaN               1.0  64d451d0-639f-47a4-9b7c-6fd92fbd264f   \n",
       "693069   27.0               1.0  727e5f07-a96b-4ad1-a2c7-9abc3ad55b4e   \n",
       "693070   10.0               1.0  e7fdc087-fe86-40a5-a3c3-3b2a8badcbda   \n",
       "\n",
       "                                  product_id          name  \n",
       "0                                  lyft_line        Shared  \n",
       "1                               lyft_premier           Lux  \n",
       "2                                       lyft          Lyft  \n",
       "3                                lyft_luxsuv  Lux Black XL  \n",
       "4                                  lyft_plus       Lyft XL  \n",
       "...                                      ...           ...  \n",
       "693066  6f72dfc5-27f1-42e8-84db-ccc7a75f6969        UberXL  \n",
       "693067  55c66225-fbe7-4fd5-9072-eab1ece5e23e         UberX  \n",
       "693068  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a          Taxi  \n",
       "693069  6d318bcc-22a3-4af6-bddd-b409bfce1546     Black SUV  \n",
       "693070  997acbb5-e102-41e1-b155-9df7de0a73f2      UberPool  \n",
       "\n",
       "[693071 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cab_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1.00</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>1.00</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>1.00</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>1.00</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>1.00</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",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6271</th>\n",
       "      <td>44.72</td>\n",
       "      <td>North Station</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1000.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1543819974</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6272</th>\n",
       "      <td>44.85</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1000.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1543819974</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6273</th>\n",
       "      <td>44.82</td>\n",
       "      <td>South Station</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1000.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1543819974</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6274</th>\n",
       "      <td>44.78</td>\n",
       "      <td>Theatre District</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1000.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1543819974</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6275</th>\n",
       "      <td>44.69</td>\n",
       "      <td>West End</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1000.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1543819974</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6276 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp                 location  clouds  pressure    rain  time_stamp  \\\n",
       "0     42.42                 Back Bay    1.00   1012.14  0.1228  1545003901   \n",
       "1     42.43              Beacon Hill    1.00   1012.15  0.1846  1545003901   \n",
       "2     42.50        Boston University    1.00   1012.15  0.1089  1545003901   \n",
       "3     42.11                   Fenway    1.00   1012.13  0.0969  1545003901   \n",
       "4     43.13       Financial District    1.00   1012.14  0.1786  1545003901   \n",
       "...     ...                      ...     ...       ...     ...         ...   \n",
       "6271  44.72            North Station    0.89   1000.69     NaN  1543819974   \n",
       "6272  44.85  Northeastern University    0.88   1000.71     NaN  1543819974   \n",
       "6273  44.82            South Station    0.89   1000.70     NaN  1543819974   \n",
       "6274  44.78         Theatre District    0.89   1000.70     NaN  1543819974   \n",
       "6275  44.69                 West End    0.89   1000.70     NaN  1543819974   \n",
       "\n",
       "      humidity   wind  \n",
       "0         0.77  11.25  \n",
       "1         0.76  11.32  \n",
       "2         0.76  11.07  \n",
       "3         0.77  11.09  \n",
       "4         0.75  11.49  \n",
       "...        ...    ...  \n",
       "6271      0.96   1.52  \n",
       "6272      0.96   1.54  \n",
       "6273      0.96   1.54  \n",
       "6274      0.96   1.54  \n",
       "6275      0.96   1.52  \n",
       "\n",
       "[6276 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "cab_data['datetime']= pd.to_datetime(cab_data['time_stamp'])\n",
    "cab_data\n",
    "weather_data['date_time'] = pd.to_datetime(weather_data['time_stamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['distance', 'cab_type', 'time_stamp', 'destination', 'source', 'price',\n",
       "       'surge_multiplier', 'id', 'product_id', 'name', 'datetime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cab_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['temp', 'location', 'clouds', 'pressure', 'rain', 'time_stamp',\n",
       "       'humidity', 'wind', 'date_time'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(693071, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cab_data.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6276, 9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>price</th>\n",
       "      <th>surge_multiplier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>693071.000000</td>\n",
       "      <td>6.930710e+05</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>693071.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.189430</td>\n",
       "      <td>1.544046e+12</td>\n",
       "      <td>16.545125</td>\n",
       "      <td>1.013870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.138937</td>\n",
       "      <td>6.891925e+08</td>\n",
       "      <td>9.324359</td>\n",
       "      <td>0.091641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.543204e+12</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.280000</td>\n",
       "      <td>1.543444e+12</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.160000</td>\n",
       "      <td>1.543737e+12</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.920000</td>\n",
       "      <td>1.544828e+12</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.860000</td>\n",
       "      <td>1.545161e+12</td>\n",
       "      <td>97.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            distance    time_stamp          price  surge_multiplier\n",
       "count  693071.000000  6.930710e+05  637976.000000     693071.000000\n",
       "mean        2.189430  1.544046e+12      16.545125          1.013870\n",
       "std         1.138937  6.891925e+08       9.324359          0.091641\n",
       "min         0.020000  1.543204e+12       2.500000          1.000000\n",
       "25%         1.280000  1.543444e+12       9.000000          1.000000\n",
       "50%         2.160000  1.543737e+12      13.500000          1.000000\n",
       "75%         2.920000  1.544828e+12      22.500000          1.000000\n",
       "max         7.860000  1.545161e+12      97.500000          3.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cab_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration and Visualization\n",
    "Through our exploration we are going to visualize and analyse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=pd.concat([cab_data,weather_data])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "a['day']=a.date_time.dt.day\n",
    "a['hour']=a.date_time.dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['distance', 'cab_type', 'time_stamp', 'destination', 'source', 'price',\n",
       "       'surge_multiplier', 'id', 'product_id', 'name', 'datetime', 'temp',\n",
       "       'location', 'clouds', 'pressure', 'rain', 'humidity', 'wind',\n",
       "       'date_time', 'day', 'hour'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking how many data we have for Uber & Lyft "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>price</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>datetime</th>\n",
       "      <th>temp</th>\n",
       "      <th>location</th>\n",
       "      <th>clouds</th>\n",
       "      <th>pressure</th>\n",
       "      <th>rain</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "      <th>date_time</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cab_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "      <td>6276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lyft</th>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "      <td>307408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uber</th>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "      <td>385663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          distance  time_stamp  destination  source   price  surge_multiplier  \\\n",
       "cab_type                                                                        \n",
       "0             6276        6276         6276    6276    6276              6276   \n",
       "Lyft        307408      307408       307408  307408  307408            307408   \n",
       "Uber        385663      385663       385663  385663  385663            385663   \n",
       "\n",
       "              id  product_id    name  datetime    temp  location  clouds  \\\n",
       "cab_type                                                                   \n",
       "0           6276        6276    6276      6276    6276      6276    6276   \n",
       "Lyft      307408      307408  307408    307408  307408    307408  307408   \n",
       "Uber      385663      385663  385663    385663  385663    385663  385663   \n",
       "\n",
       "          pressure    rain  humidity    wind  date_time     day    hour  \n",
       "cab_type                                                                 \n",
       "0             6276    6276      6276    6276       6276    6276    6276  \n",
       "Lyft        307408  307408    307408  307408     307408  307408  307408  \n",
       "Uber        385663  385663    385663  385663     385663  385663  385663  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.groupby('cab_type').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visulization for cab type i.e. Uber & Lyft."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b3e02820>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a.groupby('cab_type').count().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar plot for fluctuation of the price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b6806910>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 7200x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a['price'].value_counts().plot(kind='bar',figsize=(100,50),color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Peak hour for the cab?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b684ac70>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a['hour'].value_counts().plot(kind='bar',figsize=(10,5),color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How price & hour varies with each other?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x=a['hour']\n",
    "y=a['price']\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connection between rain and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=a['rain']\n",
    "y=a['price']\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['distance', 'cab_type', 'time_stamp', 'destination', 'source', 'price',\n",
       "       'surge_multiplier', 'id', 'product_id', 'name', 'datetime', 'temp',\n",
       "       'location', 'clouds', 'pressure', 'rain', 'humidity', 'wind',\n",
       "       'date_time', 'day', 'hour'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=a[['distance', 'temp','clouds', 'pressure', 'humidity','wind','rain','day','hour','surge_multiplier','clouds']]\n",
    "y1=a['price'] #labelled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Skicit-learn to split data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Split the data into training and testing sets\n",
    "x_train, y_train, x_test, y_test = train_test_split(x1, y1, test_size = 0.25, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear=LinearRegression()\n",
    "linear.fit(x_train,x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=linear.predict(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actual & predicted value"
   ]
  },
  {
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81607</th>\n",
       "      <td>42.5</td>\n",
       "      <td>32.547916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.755608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471801</th>\n",
       "      <td>32.5</td>\n",
       "      <td>16.816969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407645</th>\n",
       "      <td>27.5</td>\n",
       "      <td>10.480192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474760</th>\n",
       "      <td>27.5</td>\n",
       "      <td>10.556539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538489</th>\n",
       "      <td>7.5</td>\n",
       "      <td>10.709232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579511</th>\n",
       "      <td>13.5</td>\n",
       "      <td>15.519075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5421</th>\n",
       "      <td>9.0</td>\n",
       "      <td>11.828984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279982</th>\n",
       "      <td>8.0</td>\n",
       "      <td>11.854433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236315</th>\n",
       "      <td>27.5</td>\n",
       "      <td>13.228673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174837 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Actual  Predicted\n",
       "81607     42.5  32.547916\n",
       "354        0.0  14.755608\n",
       "471801    32.5  16.816969\n",
       "407645    27.5  10.480192\n",
       "474760    27.5  10.556539\n",
       "...        ...        ...\n",
       "538489     7.5  10.709232\n",
       "579511    13.5  15.519075\n",
       "5421       9.0  11.828984\n",
       "279982     8.0  11.854433\n",
       "236315    27.5  13.228673\n",
       "\n",
       "[174837 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': predictions})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar graph between actual and predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1872x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df.head(25)\n",
    "df1.plot(kind='bar',figsize=(26,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}