car-price-prediction-linear-and-lasso-regression.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/yahyasoker/car-price-prediction-linear-and-lasso-regression?scriptVersionId=127916117\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"41a8f36f","metadata":{"papermill":{"duration":0.008134,"end_time":"2023-05-01T14:28:36.312182","exception":false,"start_time":"2023-05-01T14:28:36.304048","status":"completed"},"tags":[]},"source":["### 1. [Preparing Data](#1)\n","### 2. [Model Training](#2)\n","### 3. [Model Evaluation](#3)\n","        1-Linear Regression\n","        2-Lasso  Regression\n","\n","\n"]},{"cell_type":"code","execution_count":1,"id":"df0ea2ef","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:36.328725Z","iopub.status.busy":"2023-05-01T14:28:36.327938Z","iopub.status.idle":"2023-05-01T14:28:37.771918Z","shell.execute_reply":"2023-05-01T14:28:37.770569Z"},"papermill":{"duration":1.455599,"end_time":"2023-05-01T14:28:37.774901","exception":false,"start_time":"2023-05-01T14:28:36.319302","status":"completed"},"tags":[]},"outputs":[],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LinearRegression\n","from sklearn.linear_model import Lasso\n","from sklearn import metrics"]},{"cell_type":"markdown","id":"886deca1","metadata":{"papermill":{"duration":0.006539,"end_time":"2023-05-01T14:28:37.788322","exception":false,"start_time":"2023-05-01T14:28:37.781783","status":"completed"},"tags":[]},"source":["<a id='1'></a>\n","### 1.Preparing Data\n"]},{"cell_type":"code","execution_count":2,"id":"09f8b4a3","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:37.803712Z","iopub.status.busy":"2023-05-01T14:28:37.803272Z","iopub.status.idle":"2023-05-01T14:28:37.848295Z","shell.execute_reply":"2023-05-01T14:28:37.847003Z"},"papermill":{"duration":0.056007,"end_time":"2023-05-01T14:28:37.851066","exception":false,"start_time":"2023-05-01T14:28:37.795059","status":"completed"},"tags":[]},"outputs":[],"source":["df = pd.read_csv(\"/kaggle/input/vehicle-dataset-from-cardekho/car data.csv\")"]},{"cell_type":"code","execution_count":3,"id":"af004919","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:37.866506Z","iopub.status.busy":"2023-05-01T14:28:37.866092Z","iopub.status.idle":"2023-05-01T14:28:37.874151Z","shell.execute_reply":"2023-05-01T14:28:37.873012Z"},"papermill":{"duration":0.018754,"end_time":"2023-05-01T14:28:37.876789","exception":false,"start_time":"2023-05-01T14:28:37.858035","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(301, 9)"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df.shape"]},{"cell_type":"code","execution_count":4,"id":"9557b804","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:37.892572Z","iopub.status.busy":"2023-05-01T14:28:37.892148Z","iopub.status.idle":"2023-05-01T14:28:37.918658Z","shell.execute_reply":"2023-05-01T14:28:37.916901Z"},"papermill":{"duration":0.037087,"end_time":"2023-05-01T14:28:37.920961","exception":false,"start_time":"2023-05-01T14:28:37.883874","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 301 entries, 0 to 300\n","Data columns (total 9 columns):\n"," #   Column         Non-Null Count  Dtype  \n","---  ------         --------------  -----  \n"," 0   Car_Name       301 non-null    object \n"," 1   Year           301 non-null    int64  \n"," 2   Selling_Price  301 non-null    float64\n"," 3   Present_Price  301 non-null    float64\n"," 4   Kms_Driven     301 non-null    int64  \n"," 5   Fuel_Type      301 non-null    object \n"," 6   Seller_Type    301 non-null    object \n"," 7   Transmission   301 non-null    object \n"," 8   Owner          301 non-null    int64  \n","dtypes: float64(2), int64(3), object(4)\n","memory usage: 21.3+ KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":5,"id":"d0156d90","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:37.937172Z","iopub.status.busy":"2023-05-01T14:28:37.936382Z","iopub.status.idle":"2023-05-01T14:28:37.945968Z","shell.execute_reply":"2023-05-01T14:28:37.944891Z"},"papermill":{"duration":0.020259,"end_time":"2023-05-01T14:28:37.948349","exception":false,"start_time":"2023-05-01T14:28:37.92809","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Car_Name         0\n","Year             0\n","Selling_Price    0\n","Present_Price    0\n","Kms_Driven       0\n","Fuel_Type        0\n","Seller_Type      0\n","Transmission     0\n","Owner            0\n","dtype: int64"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.isnull().sum()"]},{"cell_type":"code","execution_count":6,"id":"8f151cac","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:37.965655Z","iopub.status.busy":"2023-05-01T14:28:37.964304Z","iopub.status.idle":"2023-05-01T14:28:37.992705Z","shell.execute_reply":"2023-05-01T14:28:37.991625Z"},"papermill":{"duration":0.039255,"end_time":"2023-05-01T14:28:37.994944","exception":false,"start_time":"2023-05-01T14:28:37.955689","status":"completed"},"tags":[]},"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>Car_Name</th>\n","      <th>Year</th>\n","      <th>Selling_Price</th>\n","      <th>Present_Price</th>\n","      <th>Kms_Driven</th>\n","      <th>Fuel_Type</th>\n","      <th>Seller_Type</th>\n","      <th>Transmission</th>\n","      <th>Owner</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ritz</td>\n","      <td>2014</td>\n","      <td>3.35</td>\n","      <td>5.59</td>\n","      <td>27000</td>\n","      <td>Petrol</td>\n","      <td>Dealer</td>\n","      <td>Manual</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>sx4</td>\n","      <td>2013</td>\n","      <td>4.75</td>\n","      <td>9.54</td>\n","      <td>43000</td>\n","      <td>Diesel</td>\n","      <td>Dealer</td>\n","      <td>Manual</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ciaz</td>\n","      <td>2017</td>\n","      <td>7.25</td>\n","      <td>9.85</td>\n","      <td>6900</td>\n","      <td>Petrol</td>\n","      <td>Dealer</td>\n","      <td>Manual</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>wagon r</td>\n","      <td>2011</td>\n","      <td>2.85</td>\n","      <td>4.15</td>\n","      <td>5200</td>\n","      <td>Petrol</td>\n","      <td>Dealer</td>\n","      <td>Manual</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>swift</td>\n","      <td>2014</td>\n","      <td>4.60</td>\n","      <td>6.87</td>\n","      <td>42450</td>\n","      <td>Diesel</td>\n","      <td>Dealer</td>\n","      <td>Manual</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>vitara brezza</td>\n","      <td>2018</td>\n","      <td>9.25</td>\n","      <td>9.83</td>\n","      <td>2071</td>\n","      <td>Diesel</td>\n","      <td>Dealer</td>\n","      <td>Manual</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>ciaz</td>\n","      <td>2015</td>\n","      <td>6.75</td>\n","      <td>8.12</td>\n","      <td>18796</td>\n","      <td>Petrol</td>\n","      <td>Dealer</td>\n","      <td>Manual</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>s cross</td>\n","      <td>2015</td>\n","      <td>6.50</td>\n","      <td>8.61</td>\n","      <td>33429</td>\n","      <td>Diesel</td>\n","      <td>Dealer</td>\n","      <td>Manual</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>ciaz</td>\n","      <td>2016</td>\n","      <td>8.75</td>\n","      <td>8.89</td>\n","      <td>20273</td>\n","      <td>Diesel</td>\n","      <td>Dealer</td>\n","      <td>Manual</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>ciaz</td>\n","      <td>2015</td>\n","      <td>7.45</td>\n","      <td>8.92</td>\n","      <td>42367</td>\n","      <td>Diesel</td>\n","      <td>Dealer</td>\n","      <td>Manual</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n","0           ritz  2014           3.35           5.59       27000    Petrol   \n","1            sx4  2013           4.75           9.54       43000    Diesel   \n","2           ciaz  2017           7.25           9.85        6900    Petrol   \n","3        wagon r  2011           2.85           4.15        5200    Petrol   \n","4          swift  2014           4.60           6.87       42450    Diesel   \n","5  vitara brezza  2018           9.25           9.83        2071    Diesel   \n","6           ciaz  2015           6.75           8.12       18796    Petrol   \n","7        s cross  2015           6.50           8.61       33429    Diesel   \n","8           ciaz  2016           8.75           8.89       20273    Diesel   \n","9           ciaz  2015           7.45           8.92       42367    Diesel   \n","\n","  Seller_Type Transmission  Owner  \n","0      Dealer       Manual      0  \n","1      Dealer       Manual      0  \n","2      Dealer       Manual      0  \n","3      Dealer       Manual      0  \n","4      Dealer       Manual      0  \n","5      Dealer       Manual      0  \n","6      Dealer       Manual      0  \n","7      Dealer       Manual      0  \n","8      Dealer       Manual      0  \n","9      Dealer       Manual      0  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df.head(10)"]},{"cell_type":"code","execution_count":7,"id":"482d2a0a","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.013435Z","iopub.status.busy":"2023-05-01T14:28:38.0127Z","iopub.status.idle":"2023-05-01T14:28:38.022057Z","shell.execute_reply":"2023-05-01T14:28:38.020958Z"},"papermill":{"duration":0.020573,"end_time":"2023-05-01T14:28:38.024244","exception":false,"start_time":"2023-05-01T14:28:38.003671","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Petrol    239\n","Diesel     60\n","CNG         2\n","Name: Fuel_Type, dtype: int64\n","Dealer        195\n","Individual    106\n","Name: Seller_Type, dtype: int64\n","Manual       261\n","Automatic     40\n","Name: Transmission, dtype: int64\n"]}],"source":["print(df.Fuel_Type.value_counts())\n","print(df.Seller_Type.value_counts())\n","print(df.Transmission.value_counts())"]},{"cell_type":"code","execution_count":8,"id":"c93f7c8a","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.041449Z","iopub.status.busy":"2023-05-01T14:28:38.040438Z","iopub.status.idle":"2023-05-01T14:28:38.050081Z","shell.execute_reply":"2023-05-01T14:28:38.048851Z"},"papermill":{"duration":0.020546,"end_time":"2023-05-01T14:28:38.052309","exception":false,"start_time":"2023-05-01T14:28:38.031763","status":"completed"},"tags":[]},"outputs":[],"source":["df.replace({\"Fuel_Type\":{\"Petrol\":0,\"Diesel\":1,\"CNG\":2},\n","           \"Seller_Type\":{\"Dealer\":0,\"Individual\":1},\n","           \"Transmission\":{\"Manual\":0,\"Automatic\":1}},inplace=True)\n"]},{"cell_type":"code","execution_count":9,"id":"8df49f32","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.069716Z","iopub.status.busy":"2023-05-01T14:28:38.069238Z","iopub.status.idle":"2023-05-01T14:28:38.077991Z","shell.execute_reply":"2023-05-01T14:28:38.076765Z"},"papermill":{"duration":0.020529,"end_time":"2023-05-01T14:28:38.080616","exception":false,"start_time":"2023-05-01T14:28:38.060087","status":"completed"},"tags":[]},"outputs":[],"source":["x = df.drop(['Car_Name','Selling_Price'],axis=1)\n","y = df['Selling_Price']"]},{"cell_type":"code","execution_count":10,"id":"e5496c03","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.098142Z","iopub.status.busy":"2023-05-01T14:28:38.097717Z","iopub.status.idle":"2023-05-01T14:28:38.117225Z","shell.execute_reply":"2023-05-01T14:28:38.116251Z"},"papermill":{"duration":0.031248,"end_time":"2023-05-01T14:28:38.119941","exception":false,"start_time":"2023-05-01T14:28:38.088693","status":"completed"},"tags":[]},"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>Car_Name</th>\n","      <th>Year</th>\n","      <th>Selling_Price</th>\n","      <th>Present_Price</th>\n","      <th>Kms_Driven</th>\n","      <th>Fuel_Type</th>\n","      <th>Seller_Type</th>\n","      <th>Transmission</th>\n","      <th>Owner</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ritz</td>\n","      <td>2014</td>\n","      <td>3.35</td>\n","      <td>5.59</td>\n","      <td>27000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>sx4</td>\n","      <td>2013</td>\n","      <td>4.75</td>\n","      <td>9.54</td>\n","      <td>43000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ciaz</td>\n","      <td>2017</td>\n","      <td>7.25</td>\n","      <td>9.85</td>\n","      <td>6900</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>wagon r</td>\n","      <td>2011</td>\n","      <td>2.85</td>\n","      <td>4.15</td>\n","      <td>5200</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>swift</td>\n","      <td>2014</td>\n","      <td>4.60</td>\n","      <td>6.87</td>\n","      <td>42450</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>95</th>\n","      <td>corolla altis</td>\n","      <td>2012</td>\n","      <td>5.85</td>\n","      <td>18.61</td>\n","      <td>72000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>96</th>\n","      <td>innova</td>\n","      <td>2016</td>\n","      <td>20.75</td>\n","      <td>25.39</td>\n","      <td>29000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>97</th>\n","      <td>corolla altis</td>\n","      <td>2017</td>\n","      <td>17.00</td>\n","      <td>18.64</td>\n","      <td>8700</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>98</th>\n","      <td>corolla altis</td>\n","      <td>2013</td>\n","      <td>7.05</td>\n","      <td>18.61</td>\n","      <td>45000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>99</th>\n","      <td>fortuner</td>\n","      <td>2010</td>\n","      <td>9.65</td>\n","      <td>20.45</td>\n","      <td>50024</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>100 rows × 9 columns</p>\n","</div>"],"text/plain":["         Car_Name  Year  Selling_Price  Present_Price  Kms_Driven  Fuel_Type  \\\n","0            ritz  2014           3.35           5.59       27000          0   \n","1             sx4  2013           4.75           9.54       43000          1   \n","2            ciaz  2017           7.25           9.85        6900          0   \n","3         wagon r  2011           2.85           4.15        5200          0   \n","4           swift  2014           4.60           6.87       42450          1   \n","..            ...   ...            ...            ...         ...        ...   \n","95  corolla altis  2012           5.85          18.61       72000          0   \n","96         innova  2016          20.75          25.39       29000          1   \n","97  corolla altis  2017          17.00          18.64        8700          0   \n","98  corolla altis  2013           7.05          18.61       45000          0   \n","99       fortuner  2010           9.65          20.45       50024          1   \n","\n","    Seller_Type  Transmission  Owner  \n","0             0             0      0  \n","1             0             0      0  \n","2             0             0      0  \n","3             0             0      0  \n","4             0             0      0  \n","..          ...           ...    ...  \n","95            0             0      0  \n","96            0             1      0  \n","97            0             0      0  \n","98            0             0      0  \n","99            0             0      0  \n","\n","[100 rows x 9 columns]"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df.head(100)"]},{"cell_type":"markdown","id":"ae66c041","metadata":{"papermill":{"duration":0.007707,"end_time":"2023-05-01T14:28:38.135914","exception":false,"start_time":"2023-05-01T14:28:38.128207","status":"completed"},"tags":[]},"source":["<a id='2'></a>\n","### 2.Model Training\n"]},{"cell_type":"code","execution_count":11,"id":"168784ff","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.154479Z","iopub.status.busy":"2023-05-01T14:28:38.153681Z","iopub.status.idle":"2023-05-01T14:28:38.162963Z","shell.execute_reply":"2023-05-01T14:28:38.161453Z"},"papermill":{"duration":0.021711,"end_time":"2023-05-01T14:28:38.165728","exception":false,"start_time":"2023-05-01T14:28:38.144017","status":"completed"},"tags":[]},"outputs":[],"source":["x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.1,random_state=42)"]},{"cell_type":"code","execution_count":12,"id":"0e45e222","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.184627Z","iopub.status.busy":"2023-05-01T14:28:38.18345Z","iopub.status.idle":"2023-05-01T14:28:38.188713Z","shell.execute_reply":"2023-05-01T14:28:38.187744Z"},"papermill":{"duration":0.017101,"end_time":"2023-05-01T14:28:38.191042","exception":false,"start_time":"2023-05-01T14:28:38.173941","status":"completed"},"tags":[]},"outputs":[],"source":["lin_reg_model = LinearRegression()"]},{"cell_type":"code","execution_count":13,"id":"0e417bcb","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.208754Z","iopub.status.busy":"2023-05-01T14:28:38.208282Z","iopub.status.idle":"2023-05-01T14:28:38.236978Z","shell.execute_reply":"2023-05-01T14:28:38.23591Z"},"papermill":{"duration":0.040294,"end_time":"2023-05-01T14:28:38.239309","exception":false,"start_time":"2023-05-01T14:28:38.199015","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["LinearRegression()"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["lin_reg_model.fit(x_train,y_train)"]},{"cell_type":"markdown","id":"86caa113","metadata":{"papermill":{"duration":0.007899,"end_time":"2023-05-01T14:28:38.255487","exception":false,"start_time":"2023-05-01T14:28:38.247588","status":"completed"},"tags":[]},"source":["<a id='3'></a>\n","### 3.Model Evaluation\n","\n","    1-Linear Regression\n","    2-Lasso Regression"]},{"cell_type":"markdown","id":"28dffbcc","metadata":{"papermill":{"duration":0.007776,"end_time":"2023-05-01T14:28:38.27127","exception":false,"start_time":"2023-05-01T14:28:38.263494","status":"completed"},"tags":[]},"source":["#### 1-Linear Regression"]},{"cell_type":"code","execution_count":14,"id":"52844e3a","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.289817Z","iopub.status.busy":"2023-05-01T14:28:38.288784Z","iopub.status.idle":"2023-05-01T14:28:38.298064Z","shell.execute_reply":"2023-05-01T14:28:38.296512Z"},"papermill":{"duration":0.021295,"end_time":"2023-05-01T14:28:38.300707","exception":false,"start_time":"2023-05-01T14:28:38.279412","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["R squared Error : 0.8806173715719129\n"]}],"source":["training_data_prediction = lin_reg_model.predict(x_train)\n","error_score = metrics.r2_score(y_train, training_data_prediction)\n","print(f\"R squared Error : {error_score}\")"]},{"cell_type":"code","execution_count":15,"id":"93f451cd","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.319022Z","iopub.status.busy":"2023-05-01T14:28:38.318595Z","iopub.status.idle":"2023-05-01T14:28:38.554844Z","shell.execute_reply":"2023-05-01T14:28:38.55363Z"},"papermill":{"duration":0.248498,"end_time":"2023-05-01T14:28:38.557495","exception":false,"start_time":"2023-05-01T14:28:38.308997","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.scatter(y_train, training_data_prediction)\n","plt.xlabel(\"Actual Price\")\n","plt.ylabel(\"Predicted Price\")\n","plt.title(\" Actual Prices vs Predicted Prices\")\n","plt.show()"]},{"cell_type":"code","execution_count":16,"id":"093b1bc2","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.576904Z","iopub.status.busy":"2023-05-01T14:28:38.576461Z","iopub.status.idle":"2023-05-01T14:28:38.586741Z","shell.execute_reply":"2023-05-01T14:28:38.585174Z"},"papermill":{"duration":0.024159,"end_time":"2023-05-01T14:28:38.590446","exception":false,"start_time":"2023-05-01T14:28:38.566287","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["R squared Error : 0.8311069476244114\n"]}],"source":["test_data_pred = lin_reg_model.predict(x_test)\n","error_score = metrics.r2_score(y_test, test_data_pred)\n","print(f\"R squared Error : {error_score}\")"]},{"cell_type":"code","execution_count":17,"id":"f196a69a","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.615726Z","iopub.status.busy":"2023-05-01T14:28:38.615165Z","iopub.status.idle":"2023-05-01T14:28:38.846493Z","shell.execute_reply":"2023-05-01T14:28:38.844937Z"},"papermill":{"duration":0.248043,"end_time":"2023-05-01T14:28:38.849749","exception":false,"start_time":"2023-05-01T14:28:38.601706","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.scatter(y_test, test_data_pred)\n","plt.xlabel(\"Actual Price\")\n","plt.ylabel(\"Predicted Price\")\n","plt.title(\" Actual Prices vs Predicted Prices\")\n","plt.show()"]},{"cell_type":"markdown","id":"9327f03d","metadata":{"papermill":{"duration":0.009142,"end_time":"2023-05-01T14:28:38.869167","exception":false,"start_time":"2023-05-01T14:28:38.860025","status":"completed"},"tags":[]},"source":["#### 2-Lasso Regression"]},{"cell_type":"code","execution_count":18,"id":"ba115a93","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.890996Z","iopub.status.busy":"2023-05-01T14:28:38.889721Z","iopub.status.idle":"2023-05-01T14:28:38.896738Z","shell.execute_reply":"2023-05-01T14:28:38.895277Z"},"papermill":{"duration":0.021173,"end_time":"2023-05-01T14:28:38.899616","exception":false,"start_time":"2023-05-01T14:28:38.878443","status":"completed"},"tags":[]},"outputs":[],"source":["lass_reg_model = Lasso()"]},{"cell_type":"code","execution_count":19,"id":"738d7074","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.922003Z","iopub.status.busy":"2023-05-01T14:28:38.920506Z","iopub.status.idle":"2023-05-01T14:28:38.93632Z","shell.execute_reply":"2023-05-01T14:28:38.935093Z"},"papermill":{"duration":0.029557,"end_time":"2023-05-01T14:28:38.938859","exception":false,"start_time":"2023-05-01T14:28:38.909302","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Lasso()"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["lass_reg_model.fit(x_train,y_train)"]},{"cell_type":"code","execution_count":20,"id":"604036fe","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:38.961618Z","iopub.status.busy":"2023-05-01T14:28:38.960266Z","iopub.status.idle":"2023-05-01T14:28:38.972041Z","shell.execute_reply":"2023-05-01T14:28:38.970475Z"},"papermill":{"duration":0.025919,"end_time":"2023-05-01T14:28:38.974607","exception":false,"start_time":"2023-05-01T14:28:38.948688","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["R squared Error : 0.8436909482009372\n"]}],"source":["training_data_pred = lass_reg_model.predict(x_train)\n","error_score = metrics.r2_score(y_train, training_data_pred)\n","print(f\"R squared Error : {error_score}\")"]},{"cell_type":"code","execution_count":21,"id":"bf32623d","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:39.000788Z","iopub.status.busy":"2023-05-01T14:28:38.999204Z","iopub.status.idle":"2023-05-01T14:28:39.21088Z","shell.execute_reply":"2023-05-01T14:28:39.209446Z"},"papermill":{"duration":0.230053,"end_time":"2023-05-01T14:28:39.214239","exception":false,"start_time":"2023-05-01T14:28:38.984186","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.scatter(y_train, training_data_pred)\n","plt.xlabel(\"Actual Price\")\n","plt.ylabel(\"Predicted Price\")\n","plt.title(\" Actual Prices vs Predicted Prices\")\n","plt.show()"]},{"cell_type":"code","execution_count":22,"id":"49791a3e","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:39.246673Z","iopub.status.busy":"2023-05-01T14:28:39.24591Z","iopub.status.idle":"2023-05-01T14:28:39.255746Z","shell.execute_reply":"2023-05-01T14:28:39.254517Z"},"papermill":{"duration":0.029235,"end_time":"2023-05-01T14:28:39.25884","exception":false,"start_time":"2023-05-01T14:28:39.229605","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["R squared Error :0.839226320049874 \n"]}],"source":["test_data_prediction = lass_reg_model.predict(x_test)\n","error_score = metrics.r2_score(y_test, test_data_prediction)\n","print(f\"R squared Error :{error_score} \")"]},{"cell_type":"code","execution_count":23,"id":"3b90d600","metadata":{"execution":{"iopub.execute_input":"2023-05-01T14:28:39.291773Z","iopub.status.busy":"2023-05-01T14:28:39.291261Z","iopub.status.idle":"2023-05-01T14:28:39.511455Z","shell.execute_reply":"2023-05-01T14:28:39.509963Z"},"papermill":{"duration":0.240715,"end_time":"2023-05-01T14:28:39.515025","exception":false,"start_time":"2023-05-01T14:28:39.27431","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.scatter(y_test, test_data_prediction)\n","plt.xlabel(\"Actual Price\")\n","plt.ylabel(\"Predicted Price\")\n","plt.title(\" Actual Prices vs Predicted Prices\")\n","plt.show()"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":14.573026,"end_time":"2023-05-01T14:28:40.358173","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-05-01T14:28:25.785147","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}