Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"traffic_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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>Date</th>\n",
       "      <th>Jadcherla L1 Total traffic</th>\n",
       "      <th>Jadcherla L1 trucks and buses traffic</th>\n",
       "      <th>Jadcherla L1 multi-axial traffic</th>\n",
       "      <th>Jadcherla L2 Total traffic</th>\n",
       "      <th>Jadcherla L2 trucks and buses traffic</th>\n",
       "      <th>Jadcherla L2 multi-axial traffic</th>\n",
       "      <th>Sher Mohamad L1 Total traffic</th>\n",
       "      <th>Sher Mohamad L1 trucks and buses traffic</th>\n",
       "      <th>Sher Mohamad L1 multi-axial traffic</th>\n",
       "      <th>Sher Mohamad L2 Total traffic</th>\n",
       "      <th>Sher Mohamad L2 trucks and buses traffic</th>\n",
       "      <th>Sher Mohamad L2 multi-axial traffic</th>\n",
       "      <th>Guntur L1 Total traffic</th>\n",
       "      <th>Guntur L1 trucks and buses traffic</th>\n",
       "      <th>Guntur L1 multi-axial traffic</th>\n",
       "      <th>Guntur L2 Total traffic</th>\n",
       "      <th>Guntur L2 trucks and buses traffic</th>\n",
       "      <th>Guntur L2 multi-axial traffic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-11-95</td>\n",
       "      <td>2365</td>\n",
       "      <td>1618</td>\n",
       "      <td>76</td>\n",
       "      <td>2410</td>\n",
       "      <td>1660</td>\n",
       "      <td>99</td>\n",
       "      <td>2279</td>\n",
       "      <td>1408</td>\n",
       "      <td>80</td>\n",
       "      <td>2356</td>\n",
       "      <td>1447</td>\n",
       "      <td>132</td>\n",
       "      <td>5091</td>\n",
       "      <td>2832</td>\n",
       "      <td>224</td>\n",
       "      <td>5005</td>\n",
       "      <td>3233</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-11-95</td>\n",
       "      <td>2529</td>\n",
       "      <td>1750</td>\n",
       "      <td>114</td>\n",
       "      <td>2422</td>\n",
       "      <td>1732</td>\n",
       "      <td>116</td>\n",
       "      <td>2283</td>\n",
       "      <td>1454</td>\n",
       "      <td>75</td>\n",
       "      <td>2342</td>\n",
       "      <td>1461</td>\n",
       "      <td>159</td>\n",
       "      <td>5112</td>\n",
       "      <td>3031</td>\n",
       "      <td>271</td>\n",
       "      <td>5164</td>\n",
       "      <td>3548</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-11-95</td>\n",
       "      <td>2459</td>\n",
       "      <td>1657</td>\n",
       "      <td>96</td>\n",
       "      <td>2406</td>\n",
       "      <td>1653</td>\n",
       "      <td>128</td>\n",
       "      <td>2381</td>\n",
       "      <td>1538</td>\n",
       "      <td>100</td>\n",
       "      <td>1956</td>\n",
       "      <td>1262</td>\n",
       "      <td>141</td>\n",
       "      <td>5011</td>\n",
       "      <td>3227</td>\n",
       "      <td>246</td>\n",
       "      <td>5084</td>\n",
       "      <td>3300</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-11-95</td>\n",
       "      <td>2517</td>\n",
       "      <td>1717</td>\n",
       "      <td>111</td>\n",
       "      <td>2388</td>\n",
       "      <td>1543</td>\n",
       "      <td>105</td>\n",
       "      <td>2104</td>\n",
       "      <td>1403</td>\n",
       "      <td>61</td>\n",
       "      <td>2196</td>\n",
       "      <td>1335</td>\n",
       "      <td>121</td>\n",
       "      <td>5090</td>\n",
       "      <td>3471</td>\n",
       "      <td>321</td>\n",
       "      <td>5056</td>\n",
       "      <td>3202</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-11-95</td>\n",
       "      <td>2380</td>\n",
       "      <td>1478</td>\n",
       "      <td>136</td>\n",
       "      <td>2469</td>\n",
       "      <td>1711</td>\n",
       "      <td>104</td>\n",
       "      <td>2047</td>\n",
       "      <td>1257</td>\n",
       "      <td>78</td>\n",
       "      <td>2072</td>\n",
       "      <td>1336</td>\n",
       "      <td>129</td>\n",
       "      <td>5316</td>\n",
       "      <td>3578</td>\n",
       "      <td>303</td>\n",
       "      <td>5164</td>\n",
       "      <td>3197</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date  Jadcherla L1 Total traffic  \\\n",
       "0  01-11-95                        2365   \n",
       "1  02-11-95                        2529   \n",
       "2  03-11-95                        2459   \n",
       "3  04-11-95                        2517   \n",
       "4  05-11-95                        2380   \n",
       "\n",
       "   Jadcherla L1 trucks and buses traffic  Jadcherla L1 multi-axial traffic  \\\n",
       "0                                   1618                                76   \n",
       "1                                   1750                               114   \n",
       "2                                   1657                                96   \n",
       "3                                   1717                               111   \n",
       "4                                   1478                               136   \n",
       "\n",
       "   Jadcherla L2 Total traffic  Jadcherla L2 trucks and buses traffic  \\\n",
       "0                        2410                                   1660   \n",
       "1                        2422                                   1732   \n",
       "2                        2406                                   1653   \n",
       "3                        2388                                   1543   \n",
       "4                        2469                                   1711   \n",
       "\n",
       "   Jadcherla L2 multi-axial traffic  Sher Mohamad L1 Total traffic  \\\n",
       "0                                99                           2279   \n",
       "1                               116                           2283   \n",
       "2                               128                           2381   \n",
       "3                               105                           2104   \n",
       "4                               104                           2047   \n",
       "\n",
       "   Sher Mohamad L1 trucks and buses traffic  \\\n",
       "0                                      1408   \n",
       "1                                      1454   \n",
       "2                                      1538   \n",
       "3                                      1403   \n",
       "4                                      1257   \n",
       "\n",
       "   Sher Mohamad L1 multi-axial traffic  Sher Mohamad L2 Total traffic  \\\n",
       "0                                   80                           2356   \n",
       "1                                   75                           2342   \n",
       "2                                  100                           1956   \n",
       "3                                   61                           2196   \n",
       "4                                   78                           2072   \n",
       "\n",
       "   Sher Mohamad L2 trucks and buses traffic  \\\n",
       "0                                      1447   \n",
       "1                                      1461   \n",
       "2                                      1262   \n",
       "3                                      1335   \n",
       "4                                      1336   \n",
       "\n",
       "   Sher Mohamad L2 multi-axial traffic  Guntur L1 Total traffic  \\\n",
       "0                                  132                     5091   \n",
       "1                                  159                     5112   \n",
       "2                                  141                     5011   \n",
       "3                                  121                     5090   \n",
       "4                                  129                     5316   \n",
       "\n",
       "   Guntur L1 trucks and buses traffic  Guntur L1 multi-axial traffic  \\\n",
       "0                                2832                            224   \n",
       "1                                3031                            271   \n",
       "2                                3227                            246   \n",
       "3                                3471                            321   \n",
       "4                                3578                            303   \n",
       "\n",
       "   Guntur L2 Total traffic  Guntur L2 trucks and buses traffic  \\\n",
       "0                     5005                                3233   \n",
       "1                     5164                                3548   \n",
       "2                     5084                                3300   \n",
       "3                     5056                                3202   \n",
       "4                     5164                                3197   \n",
       "\n",
       "   Guntur L2 multi-axial traffic  \n",
       "0                            240  \n",
       "1                            191  \n",
       "2                            275  \n",
       "3                            258  \n",
       "4                            253  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data.iloc[:,1:4]\n",
    "data2 = data.iloc[:,4:7]\n",
    "data3 = data.iloc[:,7:10]\n",
    "data4 = data.iloc[:,10:13]\n",
    "data5 = data.iloc[:,13:16]\n",
    "data6 = data.iloc[:,16:19]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Guntur L2 Total traffic</th>\n",
       "      <th>Guntur L2 trucks and buses traffic</th>\n",
       "      <th>Guntur L2 multi-axial traffic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5005</td>\n",
       "      <td>3233</td>\n",
       "      <td>240</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>3548</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5084</td>\n",
       "      <td>3300</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5056</td>\n",
       "      <td>3202</td>\n",
       "      <td>258</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5164</td>\n",
       "      <td>3197</td>\n",
       "      <td>253</td>\n",
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      "text/plain": [
       "   Guntur L2 Total traffic  Guntur L2 trucks and buses traffic  \\\n",
       "0                     5005                                3233   \n",
       "1                     5164                                3548   \n",
       "2                     5084                                3300   \n",
       "3                     5056                                3202   \n",
       "4                     5164                                3197   \n",
       "\n",
       "   Guntur L2 multi-axial traffic  \n",
       "0                            240  \n",
       "1                            191  \n",
       "2                            275  \n",
       "3                            258  \n",
       "4                            253  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['date'] = pd.date_range('11/01/1995','31/12/1997')\n",
    "data2['date'] = pd.date_range('11/01/1995','31/12/1997')\n",
    "data3['date'] = pd.date_range('11/01/1995','31/12/1997')\n",
    "data4['date'] = pd.date_range('11/01/1995','31/12/1997')\n",
    "data5['date'] = pd.date_range('11/01/1995','31/12/1997')\n",
    "data6['date'] = pd.date_range('11/01/1995','31/12/1997')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jadcherla L1 Total traffic</th>\n",
       "      <th>Jadcherla L1 trucks and buses traffic</th>\n",
       "      <th>Jadcherla L1 multi-axial traffic</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2365</td>\n",
       "      <td>1618</td>\n",
       "      <td>76</td>\n",
       "      <td>1995-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2529</td>\n",
       "      <td>1750</td>\n",
       "      <td>114</td>\n",
       "      <td>1995-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2459</td>\n",
       "      <td>1657</td>\n",
       "      <td>96</td>\n",
       "      <td>1995-11-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2517</td>\n",
       "      <td>1717</td>\n",
       "      <td>111</td>\n",
       "      <td>1995-11-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2380</td>\n",
       "      <td>1478</td>\n",
       "      <td>136</td>\n",
       "      <td>1995-11-05</td>\n",
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      "text/plain": [
       "   Jadcherla L1 Total traffic  Jadcherla L1 trucks and buses traffic  \\\n",
       "0                        2365                                   1618   \n",
       "1                        2529                                   1750   \n",
       "2                        2459                                   1657   \n",
       "3                        2517                                   1717   \n",
       "4                        2380                                   1478   \n",
       "\n",
       "   Jadcherla L1 multi-axial traffic       date  \n",
       "0                                76 1995-11-01  \n",
       "1                               114 1995-11-02  \n",
       "2                                96 1995-11-03  \n",
       "3                               111 1995-11-04  \n",
       "4                               136 1995-11-05  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Total traffic')"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "# plt.scatter(pd.date_range('11/01/1995','31/12/1997'),data4.iloc[:,0],marker='X')\n",
    "plt.scatter(pd.date_range('11/01/1995','31/12/1997'),data6.iloc[:,0],marker='x')\n",
    "# plt.scatter(pd.date_range('11/01/1995','31/12/1997'),data5.iloc[:,0],marker='+')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Total traffic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['day'] = data1['date'].dt.day\n",
    "data1['month'] = data1['date'].dt.month\n",
    "data1['year'] = data1['date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2['day'] = data1['date'].dt.day\n",
    "data2['month'] = data1['date'].dt.month\n",
    "data2['year'] = data1['date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data3['day'] = data1['date'].dt.day\n",
    "data3['month'] = data1['date'].dt.month\n",
    "data3['year'] = data1['date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data4['day'] = data1['date'].dt.day\n",
    "data4['month'] = data1['date'].dt.month\n",
    "data4['year'] = data1['date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data5['day'] = data1['date'].dt.day\n",
    "data5['month'] = data1['date'].dt.month\n",
    "data5['year'] = data1['date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data6['day'] = data1['date'].dt.day\n",
    "data6['month'] = data1['date'].dt.month\n",
    "data6['year'] = data1['date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data1.drop('date',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data2.drop('date',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data3 = data3.drop('date',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data4 = data4.drop('date',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data5 = data5.drop('date',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data6 = data6.drop('date',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Jadcherla L1 Total traffic</th>\n",
       "      <th>Jadcherla L1 trucks and buses traffic</th>\n",
       "      <th>Jadcherla L1 multi-axial traffic</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2365</td>\n",
       "      <td>1618</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2529</td>\n",
       "      <td>1750</td>\n",
       "      <td>114</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2459</td>\n",
       "      <td>1657</td>\n",
       "      <td>96</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2517</td>\n",
       "      <td>1717</td>\n",
       "      <td>111</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2380</td>\n",
       "      <td>1478</td>\n",
       "      <td>136</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Jadcherla L1 Total traffic  Jadcherla L1 trucks and buses traffic  \\\n",
       "0                        2365                                   1618   \n",
       "1                        2529                                   1750   \n",
       "2                        2459                                   1657   \n",
       "3                        2517                                   1717   \n",
       "4                        2380                                   1478   \n",
       "\n",
       "   Jadcherla L1 multi-axial traffic  day  month  year  \n",
       "0                                76    1     11  1995  \n",
       "1                               114    2     11  1995  \n",
       "2                                96    3     11  1995  \n",
       "3                               111    4     11  1995  \n",
       "4                               136    5     11  1995  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay1 = data1.groupby('day')\n",
    "# groupbyMonth1 = data1.groupby('month') \n",
    "# groupbyYear1 = data1.groupby('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay2 = data2.groupby('day')\n",
    "# groupbyMonth2 = data2.groupby('month') \n",
    "# groupbyYear2 = data2.groupby('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay3 = data3.groupby('day')\n",
    "# groupbyMonth3 = data3.groupby('month') \n",
    "# groupbyYear3 = data3.groupby('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay4 = data4.groupby('day')\n",
    "# groupbyMonth4 = data4.groupby('month') \n",
    "# groupbyYear4 = data4.groupby('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay5 = data5.groupby('day')\n",
    "# groupbyMonth5 = data5.groupby('month') \n",
    "# groupbyYear5 = data5.groupby('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay6 = data6.groupby('day')\n",
    "# groupbyMonth6 = data6.groupby('month') \n",
    "# groupbyYear6 = data6.groupby('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay1.first()\n",
    "# groupbyMonth1.first()\n",
    "# groupbyYear1.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay2.first()\n",
    "# groupbyMonth2.first()\n",
    "# groupbyYear2.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay3.first()\n",
    "# groupbyMonth3.first()\n",
    "# groupbyYear3.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay4.first()\n",
    "# groupbyMonth4.first()\n",
    "# groupbyYear4.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay5.first()\n",
    "# groupbyMonth5.first()\n",
    "# groupbyYear5.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbyDay6.first()\n",
    "# groupbyMonth6.first()\n",
    "# groupbyYear6.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data1.iloc[:,1:].values\n",
    "y = data1.iloc[:,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1618,   76,    1,   11, 1995],\n",
       "       [1750,  114,    2,   11, 1995],\n",
       "       [1657,   96,    3,   11, 1995],\n",
       "       ...,\n",
       "       [2533,  171,   29,   12, 1997],\n",
       "       [2265,  139,   30,   12, 1997],\n",
       "       [2265,  139,   31,   12, 1997]], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1c2318cc2b0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(10,6))\n",
    "# plt.scatter(data1['day'],y,color='red')\n",
    "sns.jointplot(x=data1['month'],y=y,data=data1,kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1c23192ce48>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=data1['day'],y=y,data=data1,kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1618,   76,    1,   11, 1995],\n",
       "       [1750,  114,    2,   11, 1995],\n",
       "       [1657,   96,    3,   11, 1995],\n",
       "       ...,\n",
       "       [2533,  171,   29,   12, 1997],\n",
       "       [2265,  139,   30,   12, 1997],\n",
       "       [2265,  139,   31,   12, 1997]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2365, 2529, 2459, 2517, 2380, 2382, 2357, 2525, 2322, 2658, 2422,\n",
       "       2443, 2357, 2368, 2392, 2305, 2522, 2488, 2454, 2396, 2514, 2468,\n",
       "       2628, 2459, 2466, 2493, 2771, 2858, 2456, 2383, 2414, 2569, 2494,\n",
       "       2285, 2463, 2399, 2553, 2416, 2391, 2416, 2414, 2553, 2494, 2565,\n",
       "       2399, 2338, 2802, 2899, 2492, 2417, 2529, 2501, 2494, 2666, 2503,\n",
       "       2550, 2430, 2489, 2524, 2558, 2612, 2584, 2763, 2686, 2750, 2602,\n",
       "       2603, 2576, 2603, 2705, 2584, 2654, 2462, 2696, 2768, 2602, 2818,\n",
       "       2756, 2682, 2618, 2719, 2747, 2696, 2872, 2687, 2696, 2685, 2604,\n",
       "       3124, 3019, 2519, 2793, 2885, 2849, 2972, 2986, 2904, 2795, 2765,\n",
       "       2643, 2952, 2795, 2884, 2786, 2967, 2925, 2919, 2811, 2885, 3083,\n",
       "       2948, 2893, 2959, 2895, 3742, 3354, 2882, 2894, 2795, 2705, 2920,\n",
       "       2786, 2752, 2865, 2610, 2751, 2906, 2893, 2725, 2938, 2805, 2846,\n",
       "       2936, 2756, 2881, 2662, 2849, 2932, 3305, 2752, 2855, 2932, 2859,\n",
       "       3035, 3182, 2836, 2902, 2872, 2865, 2693, 2762, 2928, 2795, 2836,\n",
       "       2747, 2926, 2872, 2715, 2883, 2896, 2602, 2896, 2796, 2759, 2912,\n",
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       "       3930, 3939, 3567, 3729, 4015, 3706, 3973, 4131, 3576, 4000, 4057,\n",
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       "       3684, 3776, 3884, 3763, 3707, 3615, 3564, 3582, 3858, 3783, 3468,\n",
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       "       3518, 3910, 3916, 3812, 3422, 3665, 3992, 3869, 3634, 3654, 3654],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import GridSearchCV, cross_validate\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "X = scaler.fit_transform(X)\n",
    "y = scaler.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.41366719, 0.        , 0.        , 0.90909091, 0.        ],\n",
       "       [0.48252478, 0.0450237 , 0.03333333, 0.90909091, 0.        ],\n",
       "       [0.43401148, 0.02369668, 0.06666667, 0.90909091, 0.        ],\n",
       "       ...,\n",
       "       [0.89097548, 0.11255924, 0.93333333, 1.        , 1.        ],\n",
       "       [0.75117371, 0.07464455, 0.96666667, 1.        , 1.        ],\n",
       "       [0.75117371, 0.07464455, 1.        , 1.        , 1.        ]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [0.75660106],\n",
       "       [0.75660106]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42\n",
    "X,y = shuffle(X,y, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "para = {'C':[0.1,1,100,1000],\n",
    "       'epsilon':[0.0001,0.0005,0.001,0.005,0.01,0.05,0.1,0.5,1,5,10],\n",
    "       'gamma':[0.0001,0.001,0.005,0.1,1,3,5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "gsc = GridSearchCV(estimator=SVR(kernel='rbf'),param_grid=para,cv=5, scoring='neg_mean_squared_error', verbose=0, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "# grid_result = gsc.fit(X_train,y_train)\n",
    "grid_result = gsc.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 100, 'epsilon': 0.01, 'gamma': 0.1}\n"
     ]
    }
   ],
   "source": [
    "best_params = grid_result.best_params_\n",
    "best_predict = grid_result.predict(X_test)\n",
    "# print(best_predict)\n",
    "print(best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_svr = SVR(kernel='rbf',C=best_params['C'],epsilon=best_params['epsilon'],\n",
    "              gamma=best_params['gamma'],coef0=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring = {'abs_eror':'neg_mean_absolute_error',\n",
    "          'squared_error':'neg_mean_squared_error'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "scores = cross_validate(best_svr,X,y,cv=10,scoring=scoring,return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([0.11140418, 0.12731481, 0.09306407, 0.13558006, 0.11624813,\n",
       "        0.09972787, 0.1038363 , 0.10823154, 0.10237813, 0.1038959 ]),\n",
       " 'score_time': array([0.00444913, 0.00343275, 0.        , 0.00503445, 0.00098681,\n",
       "        0.        , 0.        , 0.00523186, 0.        , 0.        ]),\n",
       " 'test_abs_eror': array([-0.03386116, -0.03662611, -0.03578544, -0.0468251 , -0.03768729,\n",
       "        -0.03356286, -0.04341665, -0.04142331, -0.03735734, -0.03756094]),\n",
       " 'train_abs_eror': array([-0.03663888, -0.03634262, -0.03659444, -0.03524163, -0.03633526,\n",
       "        -0.03679312, -0.03560157, -0.03590873, -0.03630387, -0.0361898 ]),\n",
       " 'test_squared_error': array([-0.00194831, -0.00216565, -0.00189734, -0.00410446, -0.00252397,\n",
       "        -0.00174157, -0.00580637, -0.00308694, -0.00249884, -0.00219195]),\n",
       " 'train_squared_error': array([-0.00266549, -0.00263932, -0.00269593, -0.00243612, -0.00260949,\n",
       "        -0.00270679, -0.00225621, -0.00255497, -0.0026268 , -0.0026378 ])}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03841061855140569"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(scores['test_abs_eror'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05288231510637928"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sqrt(abs(scores['test_squared_error'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tiwar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR(C=100, cache_size=200, coef0=0.1, degree=3, epsilon=0.01, gamma=0.1,\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_svr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = best_svr.predict(X_test)\n",
    "# print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9231241564467731"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_svr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9271751764058141"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,best_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_predict.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "bus=1750\n",
    "multi=114\n",
    "day=2\n",
    "month=11\n",
    "year=1995"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans= best_svr.predict(scaler.transform(np.array([[bus,multi,day,month,year]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans=1+ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = scaler.inverse_transform([ans])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2762.80530884]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.scatter(pd.date_range('11/01/1995','31/12/1997'), data1.iloc[:,0], color='darkorange', label='data')\n",
    "# plt.hold('on')\n",
    "# plt.plot(pd.date_range('11/01/1995','31/12/1997',best_predict, color='navy', lw=lw, label='RBF model')\n",
    "# plt.xlabel('data')\n",
    "# plt.ylabel('target')\n",
    "# plt.title('Support Vector Regression')\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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