Car_Fuel_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "import numpy as np \n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error,r2_score,mean_absolute_error,accuracy_score\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd \n",
    "import pickle as pkl"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the data \n",
    "The data is imported and loaded using pandas and numpy methods below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing data \n",
    "df = pd.read_csv(\"datafiles/Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 300 entries, 0 to 299\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Source       300 non-null    object \n",
      " 1   Destination  300 non-null    object \n",
      " 2   Distance     295 non-null    float64\n",
      " 3   Fuel Cost    295 non-null    float64\n",
      "dtypes: float64(2), object(2)\n",
      "memory usage: 9.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Source         0\n",
       "Destination    0\n",
       "Distance       5\n",
       "Fuel Cost      5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(295, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Fuel Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>295.000000</td>\n",
       "      <td>295.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1458.457664</td>\n",
       "      <td>10336.575620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>820.655980</td>\n",
       "      <td>5816.262483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.121000</td>\n",
       "      <td>206.390234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>809.747000</td>\n",
       "      <td>5738.946905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1407.682000</td>\n",
       "      <td>9976.711561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1992.999000</td>\n",
       "      <td>14125.048246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3547.261000</td>\n",
       "      <td>25140.621127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Distance     Fuel Cost\n",
       "count   295.000000    295.000000\n",
       "mean   1458.457664  10336.575620\n",
       "std     820.655980   5816.262483\n",
       "min      29.121000    206.390234\n",
       "25%     809.747000   5738.946905\n",
       "50%    1407.682000   9976.711561\n",
       "75%    1992.999000  14125.048246\n",
       "max    3547.261000  25140.621127"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Fuel Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>East Sikkim</td>\n",
       "      <td>Chandrapur</td>\n",
       "      <td>1689.540</td>\n",
       "      <td>11974.333160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>West Medinipur</td>\n",
       "      <td>Pudukkottai</td>\n",
       "      <td>1931.496</td>\n",
       "      <td>13689.155984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Darjeeling</td>\n",
       "      <td>Jehanabad</td>\n",
       "      <td>544.573</td>\n",
       "      <td>3859.570375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Fatehpur</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>595.338</td>\n",
       "      <td>4219.358852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Nagapattinam</td>\n",
       "      <td>Rangareddy</td>\n",
       "      <td>642.807</td>\n",
       "      <td>4555.787478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Aligarh</td>\n",
       "      <td>Upper Subansiri</td>\n",
       "      <td>2339.540</td>\n",
       "      <td>16581.099827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Baramulla</td>\n",
       "      <td>North Dinajpur</td>\n",
       "      <td>2270.577</td>\n",
       "      <td>16092.336058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Rajauri</td>\n",
       "      <td>Karaikal</td>\n",
       "      <td>3226.177</td>\n",
       "      <td>22864.991791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Cuddalore</td>\n",
       "      <td>Bharatpur</td>\n",
       "      <td>2180.102</td>\n",
       "      <td>15451.109575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Gulbarga</td>\n",
       "      <td>Kohima</td>\n",
       "      <td>2778.202</td>\n",
       "      <td>19690.043641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Ghaziabad</td>\n",
       "      <td>29.121</td>\n",
       "      <td>206.390234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Kheri</td>\n",
       "      <td>Surendranagar</td>\n",
       "      <td>1446.124</td>\n",
       "      <td>10249.162829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Raisen</td>\n",
       "      <td>Lower Subansiri</td>\n",
       "      <td>2186.660</td>\n",
       "      <td>15497.588307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Bareilly</td>\n",
       "      <td>Banswara</td>\n",
       "      <td>1059.830</td>\n",
       "      <td>7511.368487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Erode</td>\n",
       "      <td>Mahasamund</td>\n",
       "      <td>1553.199</td>\n",
       "      <td>11008.039046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Lower Dibang Valley</td>\n",
       "      <td>Baksa</td>\n",
       "      <td>648.426</td>\n",
       "      <td>4595.611204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Sant Kabir Nagar</td>\n",
       "      <td>Karimganj</td>\n",
       "      <td>1523.724</td>\n",
       "      <td>10799.139896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Surendranagar</td>\n",
       "      <td>Kanker</td>\n",
       "      <td>1253.364</td>\n",
       "      <td>8883.008456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Jalor</td>\n",
       "      <td>Alwar</td>\n",
       "      <td>611.381</td>\n",
       "      <td>4333.060941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Jehanabad</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>1088.803</td>\n",
       "      <td>7716.709795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Hamirpur</td>\n",
       "      <td>Poonch</td>\n",
       "      <td>273.988</td>\n",
       "      <td>1941.844285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Jhansi</td>\n",
       "      <td>Jalgaon</td>\n",
       "      <td>772.007</td>\n",
       "      <td>5471.470945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>North Delhi</td>\n",
       "      <td>Gadchiroli</td>\n",
       "      <td>1220.217</td>\n",
       "      <td>8648.084618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Murshidabad</td>\n",
       "      <td>Dhenkanal</td>\n",
       "      <td>654.766</td>\n",
       "      <td>4640.544897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Bilaspur</td>\n",
       "      <td>Kamrup</td>\n",
       "      <td>2221.559</td>\n",
       "      <td>15744.929153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Tirap</td>\n",
       "      <td>Krishna</td>\n",
       "      <td>2651.665</td>\n",
       "      <td>18793.233743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Kiphire</td>\n",
       "      <td>Sikar</td>\n",
       "      <td>2579.838</td>\n",
       "      <td>18284.171852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Chitrakoot</td>\n",
       "      <td>North Dinajpur</td>\n",
       "      <td>887.433</td>\n",
       "      <td>6289.533482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Tinsukia</td>\n",
       "      <td>Anantnag</td>\n",
       "      <td>3113.291</td>\n",
       "      <td>22064.931081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Dhar</td>\n",
       "      <td>Patan</td>\n",
       "      <td>449.073</td>\n",
       "      <td>3182.730042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Source      Destination  Distance     Fuel Cost\n",
       "52          East Sikkim       Chandrapur  1689.540  11974.333160\n",
       "54       West Medinipur      Pudukkottai  1931.496  13689.155984\n",
       "55           Darjeeling        Jehanabad   544.573   3859.570375\n",
       "56             Fatehpur          Gurgaon   595.338   4219.358852\n",
       "57         Nagapattinam       Rangareddy   642.807   4555.787478\n",
       "58              Aligarh  Upper Subansiri  2339.540  16581.099827\n",
       "59            Baramulla   North Dinajpur  2270.577  16092.336058\n",
       "60              Rajauri         Karaikal  3226.177  22864.991791\n",
       "61            Cuddalore        Bharatpur  2180.102  15451.109575\n",
       "62             Gulbarga           Kohima  2778.202  19690.043641\n",
       "63            New Delhi        Ghaziabad    29.121    206.390234\n",
       "64                Kheri    Surendranagar  1446.124  10249.162829\n",
       "65               Raisen  Lower Subansiri  2186.660  15497.588307\n",
       "66             Bareilly         Banswara  1059.830   7511.368487\n",
       "67                Erode       Mahasamund  1553.199  11008.039046\n",
       "68  Lower Dibang Valley            Baksa   648.426   4595.611204\n",
       "69     Sant Kabir Nagar        Karimganj  1523.724  10799.139896\n",
       "70        Surendranagar           Kanker  1253.364   8883.008456\n",
       "71                Jalor            Alwar   611.381   4333.060941\n",
       "72            Jehanabad          Gurgaon  1088.803   7716.709795\n",
       "73             Hamirpur           Poonch   273.988   1941.844285\n",
       "74               Jhansi          Jalgaon   772.007   5471.470945\n",
       "75          North Delhi       Gadchiroli  1220.217   8648.084618\n",
       "76          Murshidabad        Dhenkanal   654.766   4640.544897\n",
       "77             Bilaspur           Kamrup  2221.559  15744.929153\n",
       "78                Tirap          Krishna  2651.665  18793.233743\n",
       "79              Kiphire            Sikar  2579.838  18284.171852\n",
       "80           Chitrakoot   North Dinajpur   887.433   6289.533482\n",
       "81             Tinsukia         Anantnag  3113.291  22064.931081\n",
       "82                 Dhar            Patan   449.073   3182.730042"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[50:80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1769.071, 1585.198,  550.592, 2854.314, 1344.634, 3166.656,\n",
       "        823.409, 1646.833, 2039.039,  814.57 ,  828.69 , 1162.388,\n",
       "       1820.537, 1284.434, 2126.618,  529.31 ,  330.536, 2535.554,\n",
       "        956.693, 1968.112, 2044.173,  804.924, 1632.888, 1060.907,\n",
       "       2576.708,   64.972, 2086.804, 1149.772, 1943.118, 1776.089,\n",
       "       1758.991, 2416.213, 2399.73 ,  318.649, 1110.617, 2017.506,\n",
       "       1905.6  , 1033.814, 1263.154, 1611.1  , 2581.813,  591.942,\n",
       "        208.9  , 1122.099, 1056.763, 1222.878,  207.558, 1971.296,\n",
       "        690.155,  605.93 , 1689.54 , 1931.496,  544.573,  595.338,\n",
       "        642.807, 2339.54 , 2270.577, 3226.177, 2180.102, 2778.202,\n",
       "         29.121, 1446.124, 2186.66 , 1059.83 , 1553.199,  648.426,\n",
       "       1523.724, 1253.364,  611.381, 1088.803,  273.988,  772.007,\n",
       "       1220.217,  654.766, 2221.559, 2651.665, 2579.838,  887.433,\n",
       "       3113.291,  449.073,  746.015, 1951.508,  692.226,  755.094,\n",
       "       2784.574,  548.886, 1407.682, 1337.435,  869.945, 2139.806,\n",
       "       1730.506,  162.718,  375.724, 1295.223,  446.472, 2282.288,\n",
       "       1393.983,  422.781,  319.257,   77.836, 3033.776, 2296.345,\n",
       "       1466.171, 1189.721, 2456.439, 1831.139, 1929.207, 2972.211,\n",
       "       3085.97 ,  827.34 , 2229.881,  881.729,  586.069, 1848.003,\n",
       "       3535.197, 3031.61 ,  881.367,  374.573, 1486.676,  970.877,\n",
       "       1174.715, 1375.205, 2022.024, 1221.795,  302.378, 1716.466,\n",
       "       2206.864,  433.238, 2855.11 ,  542.205, 1487.712,  451.119,\n",
       "       1978.632, 1173.751, 3105.076, 1728.093, 1128.137, 2581.313,\n",
       "       1537.021, 1547.907, 1756.478,  688.118,  678.128, 1475.353,\n",
       "       1492.571, 1963.88 , 1427.801,  456.373, 2265.389, 1347.024,\n",
       "        303.276,  552.803,  671.704, 1099.062, 3461.377, 1660.552,\n",
       "       1450.932, 1456.541,  936.794, 1409.052, 2949.781,  364.574,\n",
       "       1000.426, 1572.851, 1517.999, 1781.621, 1156.13 , 3145.629,\n",
       "       1061.79 , 1693.852, 1572.004, 2425.796, 2489.492, 1315.088,\n",
       "        758.199,  855.765, 1675.731, 2367.256, 1402.035, 1705.923,\n",
       "         56.555, 2879.684, 1427.077, 1519.953, 1572.437, 1144.316,\n",
       "       1771.335,  639.601,  648.137,  576.274,  358.497, 1081.002,\n",
       "        186.505, 1494.397, 1759.788, 1692.18 , 2281.706,  618.97 ,\n",
       "       1209.621,  874.829, 2735.724, 1358.361, 2139.251,  913.477,\n",
       "        682.109, 1881.174, 3242.327, 1485.815, 2873.16 , 1640.056,\n",
       "       2887.72 ,  410.058, 2093.311, 1376.761,  294.833, 1578.125,\n",
       "       1979.754,  360.073,  214.818, 1213.362, 1586.689,  352.716,\n",
       "       1098.411, 2061.747, 1644.373, 1366.295, 1197.477,  443.095,\n",
       "       2931.514, 2337.614, 2187.152,  957.451, 1493.486, 1396.366,\n",
       "       2248.364, 1823.191, 1382.755, 1157.799,  690.567, 1415.018,\n",
       "       2712.331,  640.602, 1132.661, 1066.926, 1360.973, 1063.274,\n",
       "        367.116,  531.291,  381.298, 2203.684, 3547.261, 1473.952,\n",
       "       1894.679, 2267.783,  990.778, 1986.633, 1987.746,  214.887,\n",
       "       1058.818,  978.409,  390.499,  174.962, 2548.395, 1509.913,\n",
       "       2732.58 ,  803.914, 2403.146, 1099.504, 2672.823, 2663.027,\n",
       "       1469.858, 3130.999, 1148.674,  160.306, 1154.974, 2913.89 ,\n",
       "       1998.252, 2233.26 , 1819.516, 2266.026, 1200.709,  618.76 ,\n",
       "       1031.515,  139.097, 1815.277, 1000.001, 3044.544,  435.398,\n",
       "       1135.366, 1611.002,  199.902, 1177.679, 1890.077, 2197.553,\n",
       "        284.055])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Distance'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(df['Distance'],df['Fuel Cost'],train_size=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test = X_train.to_numpy(),X_test.to_numpy()\n",
    "y_train,y_test = y_train.to_numpy(),y_test.to_numpy()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize/Scale the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### This cell has been added at the end as to emphasize the importance of Normalisiing the data ######\n",
    "#Before Fitting to the model , we need to reshape the data\n",
    "X_train_shape = X_train.reshape((-1,1))\n",
    "\n",
    "#Calculating mean and standard deviation for single data points\n",
    "mean,sigma = np.mean(X_train),np.std(X_train)\n",
    "#Normalise the data \n",
    "scale = StandardScaler()\n",
    "X_train_norm = scale.fit_transform(X_train_shape)\n",
    "X_train_norm.shape\n",
    "# X_train_shape\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply the gradient Descent \n",
    "SGDRegressor is used to apply the gradient descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDRegressor</label><div class=\"sk-toggleable__content\"><pre>SGDRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SGDRegressor()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Training our model by fitting to the linear model\n",
    "gradient_descent = SGDRegressor(max_iter=1000)\n",
    "gradient_descent.fit(X_train_norm,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The weights and the bias would be w : [5874.71514281] and b : [10322.45490027] respectively\n"
     ]
    }
   ],
   "source": [
    "#Extracting weights and bias from gradient Descent \n",
    "w_norm = gradient_descent.coef_\n",
    "b_norm = gradient_descent.intercept_\n",
    "print(f\"The weights and the bias would be w : {w_norm} and b : {b_norm} respectively\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the data \n",
    "y_pred = gradient_descent.predict(X_train_norm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring Accuracy for Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for training set\n",
      "--------------------------------------\n",
      "MAE is 0.48544612137109894\n",
      "MSE is 0.34481458910512147\n",
      "R2 score is 0.9999999900109269\n",
      "Accuracy : 99.999999%\n"
     ]
    }
   ],
   "source": [
    "#Measuring the accuracy \n",
    "# model evaluation for training set\n",
    "\n",
    "mae = mean_absolute_error(y_train, y_pred)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "r2 = r2_score(y_train, y_pred)\n",
    "\n",
    "print(\"The model performance for training set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('MAE is {}'.format(mae))\n",
    "print('MSE is {}'.format(mse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "\n",
    "print(f\"Accuracy : {r2 * 100:.6f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(X_train,y_train,y_pred,sep=\" \")\n",
    "plt.scatter(X_train,y_train,marker='x',c='black',label='actual Value')\n",
    "plt.plot(X_train,y_pred,color='green',label='Predicted Value')\n",
    "plt.xlabel(\"Distance Travelled\"); \n",
    "plt.ylabel(\"Cost of the Fuel\"); \n",
    "plt.legend(); \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting for a new value\n",
    "X_test_new = X_test.reshape((-1,1))\n",
    "# X_test_norm = scale.fit(X_test_new)\n",
    "X_test_norm = scale.transform(X_test_new)\n",
    "# print(X_test_norm[:10])\n",
    "\n",
    "### Computating Z_Score normalisation instead of scale.transform() for better accuracy #####\n",
    "\n",
    "# X_test_norm = (X_test_new - mean) / sigma\n",
    "# print(X_test_new)\n",
    "y_test_pred = gradient_descent.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(X_test,y_test,y_pred,sep=\" \")\n",
    "plt.scatter(X_test,y_test,marker='x',c='black',label='actual Value')\n",
    "plt.plot(X_test,y_test_pred,color='green',label='Predicted Value')\n",
    "plt.xlabel(\"Distance Travelled\"); \n",
    "plt.ylabel(\"Cost of the Fuel\"); \n",
    "plt.legend(); \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring the accuracy for testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for testing set\n",
      "--------------------------------------\n",
      "MAE is 0.44385671778980246\n",
      "MSE is 0.31278693027884447\n",
      "R2 score is 0.9999999900093325\n",
      "Accuracy : 99.999999%\n"
     ]
    }
   ],
   "source": [
    "# model evaluation for testing set\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_test_pred)\n",
    "mse = mean_squared_error(y_test, y_test_pred)\n",
    "r2 = r2_score(y_test, y_test_pred)\n",
    "\n",
    "print(\"The model performance for testing set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('MAE is {}'.format(mae))\n",
    "print('MSE is {}'.format(mse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "\n",
    "print(f\"Accuracy : {r2 * 100:.6f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save And Load the model\n",
    "\n",
    "modelName = \"fuel_model.pkl\"\n",
    "pkl.dump(gradient_descent,open(\"datafiles/\" + modelName,'wb'))\n",
    "\n",
    "scalerName = \"scale.pkl\"\n",
    "pkl.dump(scale,open(\"datafiles/\" + scalerName,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([142.76257473])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = pkl.load(open(\"datafiles/\" + modelName,'rb'))\n",
    "scale = pkl.load(open(\"datafiles/\" + scalerName,'rb'))\n",
    "# model.predict(scale.transform([[1]]))\n",
    "# model.predict([[]])\n",
    "# model.predict(scale.transform(np.array(20).reshape(1,-1)))"
   ]
  }
 ],
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