model_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('hyundai.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.0</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.504828</td>\n",
       "      <td>2013.638621</td>\n",
       "      <td>50148.853793</td>\n",
       "      <td>1.318621</td>\n",
       "      <td>1.128276</td>\n",
       "      <td>1.187586</td>\n",
       "      <td>19.697172</td>\n",
       "      <td>1299.600000</td>\n",
       "      <td>89.874372</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.543448</td>\n",
       "      <td>4.836428</td>\n",
       "      <td>2.343448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.947804</td>\n",
       "      <td>2.747505</td>\n",
       "      <td>36798.347812</td>\n",
       "      <td>0.469216</td>\n",
       "      <td>0.334628</td>\n",
       "      <td>0.434189</td>\n",
       "      <td>2.235651</td>\n",
       "      <td>168.853609</td>\n",
       "      <td>18.158003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.572802</td>\n",
       "      <td>1.898248</td>\n",
       "      <td>1.173770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.900000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>66.700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>30566.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>78.900000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>47000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>81.860000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>64000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>1396.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>620000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>126.320000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>14.280000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Location         Year  Kilometers_Driven   Fuel_Type  Transmission  \\\n",
       "count  725.000000   725.000000         725.000000  725.000000    725.000000   \n",
       "mean     5.504828  2013.638621       50148.853793    1.318621      1.128276   \n",
       "std      2.947804     2.747505       36798.347812    0.469216      0.334628   \n",
       "min      0.000000  2006.000000        1000.000000    1.000000      1.000000   \n",
       "25%      3.000000  2012.000000       30566.000000    1.000000      1.000000   \n",
       "50%      6.000000  2014.000000       47000.000000    1.000000      1.000000   \n",
       "75%      8.000000  2016.000000       64000.000000    2.000000      1.000000   \n",
       "max     10.000000  2019.000000      620000.000000    3.000000      2.000000   \n",
       "\n",
       "       Owner_Type     Mileage       Engine       Power  Seats   New_Price  \\\n",
       "count  725.000000  725.000000   725.000000  725.000000  725.0  725.000000   \n",
       "mean     1.187586   19.697172  1299.600000   89.874372    5.0    9.543448   \n",
       "std      0.434189    2.235651   168.853609   18.158003    0.0    2.572802   \n",
       "min      1.000000   13.900000  1086.000000   66.700000    5.0    6.500000   \n",
       "25%      1.000000   18.500000  1197.000000   78.900000    5.0    7.500000   \n",
       "50%      1.000000   18.900000  1197.000000   81.860000    5.0   10.000000   \n",
       "75%      1.000000   21.900000  1396.000000   90.000000    5.0   10.000000   \n",
       "max      4.000000   24.800000  1599.000000  126.320000    5.0   13.500000   \n",
       "\n",
       "            Price       model  \n",
       "count  725.000000  725.000000  \n",
       "mean     4.836428    2.343448  \n",
       "std      1.898248    1.173770  \n",
       "min      1.500000    1.000000  \n",
       "25%      3.400000    1.000000  \n",
       "50%      4.700000    2.000000  \n",
       "75%      6.000000    3.000000  \n",
       "max     14.280000    4.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 725 entries, 0 to 724\n",
      "Data columns (total 15 columns):\n",
      "Name                 725 non-null object\n",
      "Location             725 non-null int64\n",
      "Year                 725 non-null int64\n",
      "Kilometers_Driven    725 non-null int64\n",
      "Fuel_Type            725 non-null int64\n",
      "Transmission         725 non-null int64\n",
      "Owner_Type           725 non-null int64\n",
      "Mileage              725 non-null float64\n",
      "Engine               725 non-null int64\n",
      "Power                725 non-null float64\n",
      "Seats                725 non-null float64\n",
      "New_Price            725 non-null float64\n",
      "Price                725 non-null float64\n",
      "make                 725 non-null object\n",
      "model                725 non-null int64\n",
      "dtypes: float64(5), int64(8), object(2)\n",
      "memory usage: 85.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17e03bdb7b8>"
      ]
     },
     "execution_count": 9,
     "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": [
    "plt.scatter(df['Price'],df['model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17e03c7d550>"
      ]
     },
     "execution_count": 10,
     "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": [
    "plt.scatter(df['Price'],df['New_Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17e03ce2f60>"
      ]
     },
     "execution_count": 11,
     "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": [
    "plt.scatter(df['Price'],df['Kilometers_Driven'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf=df.drop(columns=['Name','make'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf=pd.get_dummies(newdf,columns=['Fuel_Type','Owner_Type','Location','Transmission','model','Seats'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### multivariate linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=newdf.drop('Price',axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=newdf['Price'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Kilometers_Driven', 'Mileage', 'Engine', 'Power', 'New_Price',\n",
       "       'Price', 'Fuel_Type_1', 'Fuel_Type_2', 'Fuel_Type_3', 'Owner_Type_1',\n",
       "       'Owner_Type_2', 'Owner_Type_3', 'Owner_Type_4', 'Location_0',\n",
       "       'Location_1', 'Location_2', 'Location_3', 'Location_4', 'Location_5',\n",
       "       'Location_6', 'Location_7', 'Location_8', 'Location_9', 'Location_10',\n",
       "       'Transmission_1', 'Transmission_2', 'model_1', 'model_2', 'model_3',\n",
       "       'model_4', 'Seats_5.0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def featureNormalization(X):\n",
    "    X_Norm = X.copy()\n",
    "    mu = np.zeros(X.shape[1])\n",
    "    sigma = np.zeros(X.shape[1])\n",
    "    mu = np.mean(X,axis=0)\n",
    "    sigma = np.std(X,axis=0)\n",
    "    X_Norm = (X-mu)/sigma\n",
    "    return X_Norm , mu , sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cont=X_train[:,:6]\n",
    "X_disc=X_train[:,6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_norm, mu, sigma = featureNormalization(X_cont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=np.concatenate([X_norm,X_disc],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=y.size\n",
    "X_train = np.concatenate([np.ones((X_train.shape[0], 1)), X_train], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeCost(X,y,theta):\n",
    "    h=np.dot(X,theta)\n",
    "    m=y.size\n",
    "    J=0\n",
    "    J=(1/(2*m))*np.sum(np.square(np.dot(X,theta)-y))\n",
    "    return J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GradientDescent(X,y,theta,alpha,num_iters):\n",
    "    theta=theta.copy()\n",
    "    J_history=[]\n",
    "    m=y.size\n",
    "    for i in range(num_iters):\n",
    "        theta=theta-(alpha/m)*((np.dot(X,theta)-y).dot(X))\n",
    "        J=computeCost(X,y,theta)\n",
    "        J_history.append(J)\n",
    "    return J_history ,theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.573573020257829"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computeCost(X_train,y_train,[0]*32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "J_history , theta = GradientDescent(X_train,y_train,[0.1]*32,0.02,2500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cost function value')"
      ]
     },
     "execution_count": 28,
     "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": [
    "plt.plot(np.arange(len(J_history)),J_history)\n",
    "plt.xlabel('number of iterrations')\n",
    "plt.ylabel('cost function value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=np.concatenate([np.ones((X_test.shape[0], 1)), X_test], axis=1)\n",
    "y_pred=np.dot(X_test,theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin1=linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=lin1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5827636506644873"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.square(np.subtract(ypred,y_test)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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