Machine Learning Projects (Classic)/Car-sales-project-end-to-end.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Problem Definition: Predict car sales and check the accuracy of the prediction and find which features are more important.","metadata":{}},{"cell_type":"code","source":"# importing important libraries\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.461762Z","iopub.execute_input":"2023-06-06T13:35:09.462097Z","iopub.status.idle":"2023-06-06T13:35:09.467122Z","shell.execute_reply.started":"2023-06-06T13:35:09.462072Z","shell.execute_reply":"2023-06-06T13:35:09.466052Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"## Exploring Data","metadata":{}},{"cell_type":"code","source":"# initializing data \ndf = pd.read_csv(\"/kaggle/input/car-sales/Car_sales.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.469338Z","iopub.execute_input":"2023-06-06T13:35:09.469644Z","iopub.status.idle":"2023-06-06T13:35:09.508383Z","shell.execute_reply.started":"2023-06-06T13:35:09.469616Z","shell.execute_reply":"2023-06-06T13:35:09.507591Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"#let's check first 10 values\ndf.head(10)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.510976Z","iopub.execute_input":"2023-06-06T13:35:09.511992Z","iopub.status.idle":"2023-06-06T13:35:09.552602Z","shell.execute_reply.started":"2023-06-06T13:35:09.511949Z","shell.execute_reply":"2023-06-06T13:35:09.552014Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"  Manufacturer    Model  Sales_in_thousands  __year_resale_value Vehicle_type  \\\n0        Acura  Integra              16.919               16.360    Passenger   \n1        Acura       TL              39.384               19.875    Passenger   \n2        Acura       CL              14.114               18.225    Passenger   \n3        Acura       RL               8.588               29.725    Passenger   \n4         Audi       A4              20.397               22.255    Passenger   \n5         Audi       A6              18.780               23.555    Passenger   \n6         Audi       A8               1.380               39.000    Passenger   \n7          BMW     323i              19.747                  NaN    Passenger   \n8          BMW     328i               9.231               28.675    Passenger   \n9          BMW     528i              17.527               36.125    Passenger   \n\n   Price_in_thousands  Engine_size  Horsepower  Wheelbase  Width  Length  \\\n0               21.50          1.8       140.0      101.2   67.3   172.4   \n1               28.40          3.2       225.0      108.1   70.3   192.9   \n2                 NaN          3.2       225.0      106.9   70.6   192.0   \n3               42.00          3.5       210.0      114.6   71.4   196.6   \n4               23.99          1.8       150.0      102.6   68.2   178.0   \n5               33.95          2.8       200.0      108.7   76.1   192.0   \n6               62.00          4.2       310.0      113.0   74.0   198.2   \n7               26.99          2.5       170.0      107.3   68.4   176.0   \n8               33.40          2.8       193.0      107.3   68.5   176.0   \n9               38.90          2.8       193.0      111.4   70.9   188.0   \n\n   Curb_weight  Fuel_capacity  Fuel_efficiency Latest_Launch  \\\n0        2.639           13.2             28.0      2/2/2012   \n1        3.517           17.2             25.0      6/3/2011   \n2        3.470           17.2             26.0      1/4/2012   \n3        3.850           18.0             22.0     3/10/2011   \n4        2.998           16.4             27.0     10/8/2011   \n5        3.561           18.5             22.0      8/9/2011   \n6        3.902           23.7             21.0     2/27/2012   \n7        3.179           16.6             26.0     6/28/2011   \n8        3.197           16.6             24.0     1/29/2012   \n9        3.472           18.5             25.0      4/4/2011   \n\n   Power_perf_factor  \n0          58.280150  \n1          91.370778  \n2                NaN  \n3          91.389779  \n4          62.777639  \n5          84.565105  \n6         134.656858  \n7          71.191207  \n8          81.877069  \n9          83.998724  ","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>Manufacturer</th>\n      <th>Model</th>\n      <th>Sales_in_thousands</th>\n      <th>__year_resale_value</th>\n      <th>Vehicle_type</th>\n      <th>Price_in_thousands</th>\n      <th>Engine_size</th>\n      <th>Horsepower</th>\n      <th>Wheelbase</th>\n      <th>Width</th>\n      <th>Length</th>\n      <th>Curb_weight</th>\n      <th>Fuel_capacity</th>\n      <th>Fuel_efficiency</th>\n      <th>Latest_Launch</th>\n      <th>Power_perf_factor</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Acura</td>\n      <td>Integra</td>\n      <td>16.919</td>\n      <td>16.360</td>\n      <td>Passenger</td>\n      <td>21.50</td>\n      <td>1.8</td>\n      <td>140.0</td>\n      <td>101.2</td>\n      <td>67.3</td>\n      <td>172.4</td>\n      <td>2.639</td>\n      <td>13.2</td>\n      <td>28.0</td>\n      <td>2/2/2012</td>\n      <td>58.280150</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Acura</td>\n      <td>TL</td>\n      <td>39.384</td>\n      <td>19.875</td>\n      <td>Passenger</td>\n      <td>28.40</td>\n      <td>3.2</td>\n      <td>225.0</td>\n      <td>108.1</td>\n      <td>70.3</td>\n      <td>192.9</td>\n      <td>3.517</td>\n      <td>17.2</td>\n      <td>25.0</td>\n      <td>6/3/2011</td>\n      <td>91.370778</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Acura</td>\n      <td>CL</td>\n      <td>14.114</td>\n      <td>18.225</td>\n      <td>Passenger</td>\n      <td>NaN</td>\n      <td>3.2</td>\n      <td>225.0</td>\n      <td>106.9</td>\n      <td>70.6</td>\n      <td>192.0</td>\n      <td>3.470</td>\n      <td>17.2</td>\n      <td>26.0</td>\n      <td>1/4/2012</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Acura</td>\n      <td>RL</td>\n      <td>8.588</td>\n      <td>29.725</td>\n      <td>Passenger</td>\n      <td>42.00</td>\n      <td>3.5</td>\n      <td>210.0</td>\n      <td>114.6</td>\n      <td>71.4</td>\n      <td>196.6</td>\n      <td>3.850</td>\n      <td>18.0</td>\n      <td>22.0</td>\n      <td>3/10/2011</td>\n      <td>91.389779</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Audi</td>\n      <td>A4</td>\n      <td>20.397</td>\n      <td>22.255</td>\n      <td>Passenger</td>\n      <td>23.99</td>\n      <td>1.8</td>\n      <td>150.0</td>\n      <td>102.6</td>\n      <td>68.2</td>\n      <td>178.0</td>\n      <td>2.998</td>\n      <td>16.4</td>\n      <td>27.0</td>\n      <td>10/8/2011</td>\n      <td>62.777639</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Audi</td>\n      <td>A6</td>\n      <td>18.780</td>\n      <td>23.555</td>\n      <td>Passenger</td>\n      <td>33.95</td>\n      <td>2.8</td>\n      <td>200.0</td>\n      <td>108.7</td>\n      <td>76.1</td>\n      <td>192.0</td>\n      <td>3.561</td>\n      <td>18.5</td>\n      <td>22.0</td>\n      <td>8/9/2011</td>\n      <td>84.565105</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Audi</td>\n      <td>A8</td>\n      <td>1.380</td>\n      <td>39.000</td>\n      <td>Passenger</td>\n      <td>62.00</td>\n      <td>4.2</td>\n      <td>310.0</td>\n      <td>113.0</td>\n      <td>74.0</td>\n      <td>198.2</td>\n      <td>3.902</td>\n      <td>23.7</td>\n      <td>21.0</td>\n      <td>2/27/2012</td>\n      <td>134.656858</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>BMW</td>\n      <td>323i</td>\n      <td>19.747</td>\n      <td>NaN</td>\n      <td>Passenger</td>\n      <td>26.99</td>\n      <td>2.5</td>\n      <td>170.0</td>\n      <td>107.3</td>\n      <td>68.4</td>\n      <td>176.0</td>\n      <td>3.179</td>\n      <td>16.6</td>\n      <td>26.0</td>\n      <td>6/28/2011</td>\n      <td>71.191207</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>BMW</td>\n      <td>328i</td>\n      <td>9.231</td>\n      <td>28.675</td>\n      <td>Passenger</td>\n      <td>33.40</td>\n      <td>2.8</td>\n      <td>193.0</td>\n      <td>107.3</td>\n      <td>68.5</td>\n      <td>176.0</td>\n      <td>3.197</td>\n      <td>16.6</td>\n      <td>24.0</td>\n      <td>1/29/2012</td>\n      <td>81.877069</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>BMW</td>\n      <td>528i</td>\n      <td>17.527</td>\n      <td>36.125</td>\n      <td>Passenger</td>\n      <td>38.90</td>\n      <td>2.8</td>\n      <td>193.0</td>\n      <td>111.4</td>\n      <td>70.9</td>\n      <td>188.0</td>\n      <td>3.472</td>\n      <td>18.5</td>\n      <td>25.0</td>\n      <td>4/4/2011</td>\n      <td>83.998724</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.553498Z","iopub.execute_input":"2023-06-06T13:35:09.554450Z","iopub.status.idle":"2023-06-06T13:35:09.579490Z","shell.execute_reply.started":"2023-06-06T13:35:09.554410Z","shell.execute_reply":"2023-06-06T13:35:09.578349Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 157 entries, 0 to 156\nData columns (total 16 columns):\n #   Column               Non-Null Count  Dtype  \n---  ------               --------------  -----  \n 0   Manufacturer         157 non-null    object \n 1   Model                157 non-null    object \n 2   Sales_in_thousands   157 non-null    float64\n 3   __year_resale_value  121 non-null    float64\n 4   Vehicle_type         157 non-null    object \n 5   Price_in_thousands   155 non-null    float64\n 6   Engine_size          156 non-null    float64\n 7   Horsepower           156 non-null    float64\n 8   Wheelbase            156 non-null    float64\n 9   Width                156 non-null    float64\n 10  Length               156 non-null    float64\n 11  Curb_weight          155 non-null    float64\n 12  Fuel_capacity        156 non-null    float64\n 13  Fuel_efficiency      154 non-null    float64\n 14  Latest_Launch        157 non-null    object \n 15  Power_perf_factor    155 non-null    float64\ndtypes: float64(12), object(4)\nmemory usage: 19.8+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.581917Z","iopub.execute_input":"2023-06-06T13:35:09.582926Z","iopub.status.idle":"2023-06-06T13:35:09.598768Z","shell.execute_reply.started":"2023-06-06T13:35:09.582882Z","shell.execute_reply":"2023-06-06T13:35:09.596853Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"Manufacturer            0\nModel                   0\nSales_in_thousands      0\n__year_resale_value    36\nVehicle_type            0\nPrice_in_thousands      2\nEngine_size             1\nHorsepower              1\nWheelbase               1\nWidth                   1\nLength                  1\nCurb_weight             2\nFuel_capacity           1\nFuel_efficiency         3\nLatest_Launch           0\nPower_perf_factor       2\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df.columns","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.601213Z","iopub.execute_input":"2023-06-06T13:35:09.601567Z","iopub.status.idle":"2023-06-06T13:35:09.612706Z","shell.execute_reply.started":"2023-06-06T13:35:09.601544Z","shell.execute_reply":"2023-06-06T13:35:09.611379Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"Index(['Manufacturer', 'Model', 'Sales_in_thousands', '__year_resale_value',\n       'Vehicle_type', 'Price_in_thousands', 'Engine_size', 'Horsepower',\n       'Wheelbase', 'Width', 'Length', 'Curb_weight', 'Fuel_capacity',\n       'Fuel_efficiency', 'Latest_Launch', 'Power_perf_factor'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"# summarizing our data\ndf.describe()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.614450Z","iopub.execute_input":"2023-06-06T13:35:09.614837Z","iopub.status.idle":"2023-06-06T13:35:09.661637Z","shell.execute_reply.started":"2023-06-06T13:35:09.614807Z","shell.execute_reply":"2023-06-06T13:35:09.660273Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"       Sales_in_thousands  __year_resale_value  Price_in_thousands  \\\ncount          157.000000           121.000000          155.000000   \nmean            52.998076            18.072975           27.390755   \nstd             68.029422            11.453384           14.351653   \nmin              0.110000             5.160000            9.235000   \n25%             14.114000            11.260000           18.017500   \n50%             29.450000            14.180000           22.799000   \n75%             67.956000            19.875000           31.947500   \nmax            540.561000            67.550000           85.500000   \n\n       Engine_size  Horsepower   Wheelbase       Width      Length  \\\ncount   156.000000  156.000000  156.000000  156.000000  156.000000   \nmean      3.060897  185.948718  107.487179   71.150000  187.343590   \nstd       1.044653   56.700321    7.641303    3.451872   13.431754   \nmin       1.000000   55.000000   92.600000   62.600000  149.400000   \n25%       2.300000  149.500000  103.000000   68.400000  177.575000   \n50%       3.000000  177.500000  107.000000   70.550000  187.900000   \n75%       3.575000  215.000000  112.200000   73.425000  196.125000   \nmax       8.000000  450.000000  138.700000   79.900000  224.500000   \n\n       Curb_weight  Fuel_capacity  Fuel_efficiency  Power_perf_factor  \ncount   155.000000     156.000000       154.000000         155.000000  \nmean      3.378026      17.951923        23.844156          77.043591  \nstd       0.630502       3.887921         4.282706          25.142664  \nmin       1.895000      10.300000        15.000000          23.276272  \n25%       2.971000      15.800000        21.000000          60.407707  \n50%       3.342000      17.200000        24.000000          72.030917  \n75%       3.799500      19.575000        26.000000          89.414878  \nmax       5.572000      32.000000        45.000000         188.144323  ","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>Sales_in_thousands</th>\n      <th>__year_resale_value</th>\n      <th>Price_in_thousands</th>\n      <th>Engine_size</th>\n      <th>Horsepower</th>\n      <th>Wheelbase</th>\n      <th>Width</th>\n      <th>Length</th>\n      <th>Curb_weight</th>\n      <th>Fuel_capacity</th>\n      <th>Fuel_efficiency</th>\n      <th>Power_perf_factor</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>157.000000</td>\n      <td>121.000000</td>\n      <td>155.000000</td>\n      <td>156.000000</td>\n      <td>156.000000</td>\n      <td>156.000000</td>\n      <td>156.000000</td>\n      <td>156.000000</td>\n      <td>155.000000</td>\n      <td>156.000000</td>\n      <td>154.000000</td>\n      <td>155.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>52.998076</td>\n      <td>18.072975</td>\n      <td>27.390755</td>\n      <td>3.060897</td>\n      <td>185.948718</td>\n      <td>107.487179</td>\n      <td>71.150000</td>\n      <td>187.343590</td>\n      <td>3.378026</td>\n      <td>17.951923</td>\n      <td>23.844156</td>\n      <td>77.043591</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>68.029422</td>\n      <td>11.453384</td>\n      <td>14.351653</td>\n      <td>1.044653</td>\n      <td>56.700321</td>\n      <td>7.641303</td>\n      <td>3.451872</td>\n      <td>13.431754</td>\n      <td>0.630502</td>\n      <td>3.887921</td>\n      <td>4.282706</td>\n      <td>25.142664</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.110000</td>\n      <td>5.160000</td>\n      <td>9.235000</td>\n      <td>1.000000</td>\n      <td>55.000000</td>\n      <td>92.600000</td>\n      <td>62.600000</td>\n      <td>149.400000</td>\n      <td>1.895000</td>\n      <td>10.300000</td>\n      <td>15.000000</td>\n      <td>23.276272</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>14.114000</td>\n      <td>11.260000</td>\n      <td>18.017500</td>\n      <td>2.300000</td>\n      <td>149.500000</td>\n      <td>103.000000</td>\n      <td>68.400000</td>\n      <td>177.575000</td>\n      <td>2.971000</td>\n      <td>15.800000</td>\n      <td>21.000000</td>\n      <td>60.407707</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>29.450000</td>\n      <td>14.180000</td>\n      <td>22.799000</td>\n      <td>3.000000</td>\n      <td>177.500000</td>\n      <td>107.000000</td>\n      <td>70.550000</td>\n      <td>187.900000</td>\n      <td>3.342000</td>\n      <td>17.200000</td>\n      <td>24.000000</td>\n      <td>72.030917</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>67.956000</td>\n      <td>19.875000</td>\n      <td>31.947500</td>\n      <td>3.575000</td>\n      <td>215.000000</td>\n      <td>112.200000</td>\n      <td>73.425000</td>\n      <td>196.125000</td>\n      <td>3.799500</td>\n      <td>19.575000</td>\n      <td>26.000000</td>\n      <td>89.414878</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>540.561000</td>\n      <td>67.550000</td>\n      <td>85.500000</td>\n      <td>8.000000</td>\n      <td>450.000000</td>\n      <td>138.700000</td>\n      <td>79.900000</td>\n      <td>224.500000</td>\n      <td>5.572000</td>\n      <td>32.000000</td>\n      <td>45.000000</td>\n      <td>188.144323</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# checking for the shape \ndf.shape","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.662784Z","iopub.execute_input":"2023-06-06T13:35:09.663127Z","iopub.status.idle":"2023-06-06T13:35:09.669669Z","shell.execute_reply.started":"2023-06-06T13:35:09.663100Z","shell.execute_reply":"2023-06-06T13:35:09.668465Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"(157, 16)"},"metadata":{}}]},{"cell_type":"code","source":"# checking for the duplicate values and removing them\ndf.drop_duplicates(keep=\"first\")\ndf.shape","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.670984Z","iopub.execute_input":"2023-06-06T13:35:09.671372Z","iopub.status.idle":"2023-06-06T13:35:09.687295Z","shell.execute_reply.started":"2023-06-06T13:35:09.671344Z","shell.execute_reply":"2023-06-06T13:35:09.686404Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"(157, 16)"},"metadata":{}}]},{"cell_type":"markdown","source":"We don't have any duplicate values","metadata":{}},{"cell_type":"markdown","source":"We can also drop unnecessary column that won't play any role in our solution. Additionally, this is not a time series problem as Launch Dates has nothing to do with price.","metadata":{}},{"cell_type":"code","source":"df[\"Vehicle_type\"].value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.688542Z","iopub.execute_input":"2023-06-06T13:35:09.688789Z","iopub.status.idle":"2023-06-06T13:35:09.696554Z","shell.execute_reply.started":"2023-06-06T13:35:09.688764Z","shell.execute_reply":"2023-06-06T13:35:09.695629Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Passenger    116\nCar           41\nName: Vehicle_type, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# let's plot to see if vehicle type has any relationship with price\nplt.scatter(df[\"Price_in_thousands\"],df[\"Vehicle_type\"])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.700501Z","iopub.execute_input":"2023-06-06T13:35:09.700889Z","iopub.status.idle":"2023-06-06T13:35:09.869129Z","shell.execute_reply.started":"2023-06-06T13:35:09.700855Z","shell.execute_reply":"2023-06-06T13:35:09.868261Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAlQAAAGdCAYAAADUl+3IAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAlmUlEQVR4nO3de3RV9Zn/8c/J/Z4YIE2CIQQENdwR0VSltlhvjLY/1nRqBYFhOm2ttlI7xaJtgSoNs3rRwVVwxFlAyVicFppBRBRaRFovUQSFgIgQAoXEjIRcICUJOd/fH0zOECDJSZ5cd96vtc5acPY+3/08371Pzifn7LPjc845AQAAoN1CursAAACA3o5ABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAUVh3F9BX+P1+HT9+XPHx8fL5fN1dDgAACIJzTtXV1UpPT1dISPPvQxGousjx48eVkZHR3WUAAIB2OHr0qC6//PJmlxOoukh8fLykczskISGhm6sBAADBqKqqUkZGRuB1vDkEqi7S+DFfQkICgQoAgF6mtdN1OCkdAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARF/bsgxr8TgVF5SqrPqP+cZGSkz49XauU+ChNzEpWaEjrf2vw/DHa8jhJqjvr1+o3D6u4vEaZyTG6L2ewIsJCLhrzmszL9E5Rud489Kkkn3KG9tP4QZfp+beLA4+997pM7TpaodKqMyo/Vavk2AilJkZrYlayGvxOq988rKITp+WTNC7jMqUlRWtsRpJWv3lYr+4tld9JV6bG6drMfkpLim5THw1+p7cOngjUd11WsiTpzUMndPzk3ySflJoQperaeoX4fBrcLzZQb2vz1t75bfA7vXXohN44+KmOnfyb0pOidMPQAbp+aL829XXhfthRfLJJLZLavf87m+XY7Moxe2MNVl7oAWiOzznnuruIrlRaWqpFixbppZde0rFjx5SSkqKxY8dqzpw5mjx5cqdtt6qqSomJiaqsrOzWK6Vv2lOihS/uVUnlmUsuT0uM0vy7snX7yLQ2jRHM4yQpd+NeLd9eJP95R12IT5p8dYr2HKtqMqZPUnsPztiIUNXUNbT58cH2sWlPiX64brcqaurbWWHz22vv/LZUU1JMuBZPHRVUXxduO8SnJvsrKSZckppsJ9h562yWY7Mrx+yNNVh5oQf0TcG+fvepQHX48GHdcMMNSkpK0sKFCzV69GjV19frlVde0bPPPqsPP/ywzWM2NDTI5/O1+BeopZ4RqDbtKdH9ee+1GDIaf1dcNn38JX/INTdGa4+TzoWpf3+9qK1ldzmfWu5j054SfSvvvQ7bls7bXnvnN9ianmmlr9aOj+YEs/87m+XY7Mox26on1GDlhR7QdwX7+t2nzqH69re/LZ/Pp4KCAv393/+9hg8frhEjRujhhx/WW2+9JUn61a9+pVGjRik2NlYZGRn69re/rVOnTgXGWLlypZKSkrRhwwZlZ2crMjJSxcXF3dVS0Br8Tgtf3Nvqi2Xj8oUv7lWDv+naLY3R0uOkcx/zLd/e88NUo+b6aPA7LVhf2GHbOX/e6s762zW/52raG9T2FqwvbLavYI6P5rS2/zub5djsyjHbqifUYOWFHoBg9JlAVV5erk2bNumBBx5QbGzsRcuTkpIkSSEhIVqyZIn27NmjVatW6U9/+pPmzp3bZN2amhrl5ubqueeeU2FhoVJSUi4ar7a2VlVVVU1u3amgqLzZj/ku5CSVVJ5RQVF5m8Zo7nGStPrNw+otPy9b6qOgqFylVbWdsr3Vbx5u1/yeqym4fVtaVdtsX8EeH22trytYjs2uHLOtekINVl7oAQhGnzkp/eOPP5ZzTldddVWL682ZMyfw76ysLD3++OO6//77tXTp0sD99fX1Wrp0qcaMGdPsOLm5uVq4cKG57o5SVt32F8sLHxPsGJdar7i8ps3b726X6qM98xisYOeovfulpfU7sq/OnCPrNttSW2eM2VY9oQYrL/QABKPPvEPVeKqYz9fyN0q2bt2qL37xixo4cKDi4+M1Y8YMnThxQqdPnw6sExERodGjR7c4zrx581RZWRm4HT161N6EQUp8lPkxwY5xqfUyk2PavP3udqk+2jOPwQp2jtq7X1pavyP76sw5sm6zLbV1xpht1RNqsPJCD0Aw+kygGjZsmHw+n/bt29fsOsXFxbrzzjs1cuRIrV27Vjt27NCvf/1rSefelWoUHR3dajCLjIxUQkJCk1t3mpiVrLTEKAXzBWWfzn37pvHr8cGO0dzjJOm+nMHqLd+ObqmPiVnJSk2I7JTt3ZczuF3ze66m4F6MUhMim+0r2OOjOS3NW2ezHJtdOWZb9YQarLzQAxCMPhOokpOTddttt+nXv/51k3ebGlVUVOjdd9/V2bNn9ctf/lLXX3+9hg8fruPHj3dDtR0vNMSn+XdlS1KLL5qNy+bflX3R9WFaGqOlx0lSRFiI/vmmrLYX3k2a6yM0xKcFd4/osO2cP28RYSHtmt9zNWUHtb0Fd49otq9gjo/mtLb/O5vl2OzKMduqJ9Rg5YUegGD0mUAlSUuXLlVDQ4MmTpyotWvX6sCBA9q3b5+WLFminJwcDR06VGfPntXTTz+tQ4cOafXq1XrmmWe6u+wOc/vINC2bPl6pic2/m5GaGNXiV5ibG6O1x0nSvDuz9c1JWRe9UxXik76YnaK0C8a0/HiNjQxt1+PTgujj9pFpemb6+MD1mCwunLf2zm9rNSXFhLd4yYSWtn3h/rosJvyi7QSz/zub5djsyjF7Yw1WXugBaE2fug6VJJWUlGjRokXasGGDSkpKNGDAAF1zzTX63ve+p5tvvllPPvmkfv7zn6uiokKTJk3StGnTNGPGDJ08eVJJSUlauXKl5syZo4qKijZttydch6oRV0rnSumtjcGV0jt/zN5Yg5UXekDfw4U9e5ieFKgAAEBwuLAnAABAFyFQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGBCoAAAAjAhUAAIARgQoAAMCIQAUAAGBEoAIAADAiUAEAABgRqAAAAIwIVAAAAEYEKgAAACMCFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGYd1dANqvwe9UUFSusuoz6h8bKfmkT0/VKiU+ShOzkhUa4muyzvn3Nz7+rUMn9ObBE3LOKSE6TCdP12v3sQr9ra5B0RGh6hcXpYGJUUqOi9BlMRF6/68VanBOoT6fxmVcprSkaI3NSFLeW8V65/AJxUSE6eq0BKXERyolPkp+5/RW0QkdO/k3Nfj9+rS6VrVn/YoOD1VyXKTk/CqvqVdMZJiuzUzWlSnxerv4hI5XnNHAy6I1YdBl2rS3RNv2fypJGpEWr1tHpKrqTIPKa2pVcvKM/HIK8fmUnhStG67or+uH9JMkFRSVq6Tib3q3uFz7S6vl80nDBsTpdN1Z1dQ1yEny+XyKjwzT1PGX67oh/bSj+KRKq86o/FStkmIiVH66Tp+eOqM9x6pUe7ZBA5NiNHXcQEnSup3H9NeTpxUVHqYxlycpZ+i57b5dVC7JKWdIf12blawdxScv2kf94yIlJ5VVn9Gnp+pUUVMnJ6fLYiLVLzZCJ2vqlBQdroq/1Ss5LlKpCU33XUvHQFnV/43p80k5Q/rr+qH9LrnfL1XnhcdJc8fehWOcv41L1XfhuHVn/Vr1xuHAcfP/xg5UWGiIyk7VqvxUrZJjI5SaGH3JY7l/bKT8zuntonI5OSVFh6t/XGRgfUl66+AJvXnoUzknJcVEqH9c0/Faqq8j5igYLc1PR+js8TtTb6297qxfq988rOLyGmUmx+i+nMGKCPu/9y46s6/eOGcdUXNP6dvnnHPBrjxr1iytWrVKkhQWFqaMjAxNnTpVCxcuVGxsbKcV6QVVVVVKTExUZWWlEhISzONt2lOihS/uVUnlmUsuT0uM0t1j0rT+/ZIm66QlRmn+XdmSpB+u262KmnpzLT1NTESoIsJC2tybT1LQT4ZuGLNx390+Mk1S68fA+ZJiwrV46ihJl97vF9Z54bbOt2lPySXHaNxGS/U1jrvzyEk9u71Iwfz0ae5YbqnXurN+1dQ1NDte43PgUvXdPSZNL7z7V9McBaOl+WnvmF05fmfqrbXnbtyr5duL5D/vQAnxSf98U5bm3ZndqX31xjnriJq7ou9gX7/bHKg++eQTrVixQvX19dq+fbu+/vWva+bMmVq2bFmHFN6bNDQ0yOfzKSSk9U9OOzJQbdpTovvz3mvXC3VnhAZ0HZ+kZdPHS1K7j4Fgt6P/3db5P5Q27SnRt/Lea/Gxz7RQn5eOv+bmKBjNPYctY3bl+J2pt9aeu3Gv/v31omaXfzE7RVv2lnVKX71xzjqi5q7qO9jX7zafQxUZGanU1FRlZGTo3nvv1bRp05Sfn6+8vDxNmDBB8fHxSk1N1b333quysrLA406ePKlp06ZpwIABio6O1rBhw7RixQpJUl1dnR588EGlpaUpKipKgwcPVm5ubuCxlZWV+sY3vqGUlBQlJCToC1/4gt5///3A8gULFmjs2LFavXq1Bg8erMTERN1zzz2qrq4OrFNdXa1p06YpNjZWaWlpevLJJ3XzzTdrzpw5gXXq6uo0d+5cDRw4ULGxsbruuuv02muvBZavXLlSSUlJ2rBhg7KzsxUZGani4uK2TqFJg99p4Yt72/2i5JUXs75s4Yt7tWB9Yafuy8axF764Vw3/++t2g99pwfq9rT52wfrCZuvz0vF3qTkKRkvP4faO2ZXjd6beWnvdWb+Wb28+TEnS5kuEKcneV2+cs46ouSf2bT4pPTo6WvX19aqrq9Pjjz+u999/X/n5+SoqKtKsWbMC6/34xz/W3r179fLLL2vfvn1atmyZ+vfvL0lasmSJ1q9fr//6r//S/v37lZeXp8GDB0uSnHOaMmWKSktLtXHjRu3YsUPjx4/X5MmTVV5eHhj/4MGDys/P14YNG7RhwwZt27ZNixcvDix/+OGH9Ze//EXr16/X5s2btX37dr33XtPftP/xH/9Rf/nLX7RmzRp98MEH+spXvqLbb79dBw4cCKxTU1Oj3NxcPffccyosLFRKSsol56W2tlZVVVVNbh2hoKg8qI894E1OUknlGZVW1XbZtgqKzj3PCorKVVrV+rFXWlXbJfX1BBfOUTBaew63Z8yuHL8z9dbaV795WJbXbUtfvXHOOqLmnti36aT0goICPf/885o8ebJmz54duH/IkCFasmSJJk6cqFOnTikuLk5HjhzRuHHjNGHCBEkKBCZJOnLkiIYNG6Ybb7xRPp9PmZmZgWVbt27V7t27VVZWpsjISEnSL37xC+Xn5+v3v/+9vvGNb0iS/H6/Vq5cqfj4eEnSfffdpz/+8Y9atGiRqqurtWrVqkCtkrRixQqlp6cHtnPw4EH99re/1V//+tfA/f/yL/+iTZs2acWKFfrZz34mSaqvr9fSpUs1ZsyYFucmNzdXCxcubNe8tqSsmjCFrtV4zHHsNa8tcxPsuu2d784evzP11tqLy2s6ZJz29NUb56wjau6Jfbf5HaoNGzYoLi5OUVFRysnJ0aRJk/T0009r586d+tKXvqTMzEzFx8fr5ptvlnQuLEnS/fffrzVr1mjs2LGaO3eu3njjjcCYs2bN0q5du3TllVfqu9/9rl599dXAsh07dujUqVPq16+f4uLiAreioiIdPHgwsN7gwYMDYUqS0tLSAh85Hjp0SPX19Zo4cWJgeWJioq688srA/9977z055zR8+PAm29m2bVuT7URERGj06NGtztO8efNUWVkZuB09ejTYKW5RSnxUh4wDBKvxmOPYa15b5ibYdds73509fmfqrbVnJsd0yDjt6as3zllH1NwT+27zO1Sf//zntWzZMoWHhys9PV3h4eE6ffq0br31Vt16663Ky8vTgAEDdOTIEd12222qq6uTJN1xxx0qLi7WSy+9pC1btmjy5Ml64IEH9Itf/ELjx49XUVGRXn75ZW3ZskX/8A//oFtuuUW///3v5ff7lZaW1uRcpkZJSUmBf4eHhzdZ5vP55Pf7JZ372LDxvvOdfz6+3+9XaGioduzYodDQ0CbrxcXFBf4dHR190TiXEhkZGXhHrSNNzEpWWmKUSivPeOp8FATHJyk1MUrOOX1SVdupx0DjthovQzAxK1mpCVGtfuyXmnDuuO/s+nqCC+coGK09h9szZleO35l6a+335QzWoo37Wv3Yr7kvZVj66o1z1hE198S+2/wOVWxsrK644gplZmYGQsyHH36oTz/9VIsXL9ZNN92kq666qskJ6Y0GDBigWbNmKS8vT0899ZSeffbZwLKEhAR99atf1fLly/XCCy9o7dq1Ki8v1/jx41VaWqqwsDBdccUVTW6N52C1ZujQoQoPD1dBQUHgvqqqqibnRo0bN04NDQ0qKyu7aDupqaltnaZOExriC3zluz1X2ejZVyRBMObfla0Fd4+Q1Hn7s3Hc+XdlB67nEhri04K7s1t97IK7RzRbX087/iz1XGqOgtHSc7i9Y3bl+J2pt9YeERaif74pq8V1vph97nzbju6rN85ZR9TcE/vukCulDxo0SBEREXr66ad16NAhrV+/Xo8//niTdX7yk5/ov//7v/Xxxx+rsLBQGzZs0NVXXy1JevLJJ7VmzRp9+OGH+uijj/S73/1OqampSkpK0i233KKcnBx9+ctf1iuvvKLDhw/rjTfe0I9+9CO9++67QdUXHx+vmTNn6gc/+IG2bt2qwsJCzZ49WyEhIYF3m4YPH65p06ZpxowZWrdunYqKivTOO+/oX//1X7Vx48aOmKYOc/vINC2bPl6pic2/lZmWGKVvTspS2gXrpCZG6Znp4/XM9PFKiglv5tG9W2xEaLt664ynXUeOmZYYFfgacDDHwPmSYsJb3O8X1pl63rbOd/vItGbHaNxGS/U1Hn/fnJSlIN7oldT8sdycpJhwxUSENrs87bznwIX1NW7LMkfBaGl+OuKr3p09fmfqrbXPuzNb35yUpQtfv0N80jcnZWn5jGs7ra/eOGcdUXNP67vN16GqqKhQfn7+Rct++9vf6tFHH1VJSYnGjx+vefPm6e6779bOnTs1duxYPfHEE3r++ed1+PBhRUdH66abbtKTTz6prKwsLV++XEuXLtWBAwcUGhqqa6+9Vj//+c81btw4SecuefDYY49p7dq1+p//+R+lpqZq0qRJys3NVUZGhhYsWKD8/Hzt2rUrUM9TTz2lp556SocPHw6M8a1vfUv5+flKSEjQ3LlztWbNGn3hC18IXKKhvr5eTzzxhH7zm9/o2LFj6tevn3JycrRw4UKNGjVKK1eu1Jw5c1RRUdHmie7oC3tKXCmdK6VzpXSulN6943em3lo7V0pvm95wpfROubCnl5w+fVoDBw7UL3/5S/3TP/1Tp2+vMwIVAADoXMG+fveZv+W3c+dOffjhh5o4caIqKyv105/+VJL0pS99qZsrAwAAvV2fCVTSuetX7d+/XxEREbrmmmu0ffv2oE9sBwAAaE6fCVTjxo3Tjh07ursMAADgQR3yLT8AAIC+jEAFAABgRKACAAAwIlABAAAYEagAAACMCFQAAABGfeayCd2t8YL0VVVV3VwJAAAIVuPrdmt/WIZA1UWqq6slSRkZGd1cCQAAaKvq6molJiY2u7zP/i2/rub3+3X8+HHFx8fL5+vcP1ZZVVWljIwMHT161PN/N5BevYlevasv9Uuv3uCcU3V1tdLT0xUS0vyZUrxD1UVCQkJ0+eWXd+k2ExISPHdgN4devYlevasv9UuvvV9L70w14qR0AAAAIwIVAACAEYHKgyIjIzV//nxFRkZ2dymdjl69iV69qy/1S699CyelAwAAGPEOFQAAgBGBCgAAwIhABQAAYESgAgAAMCJQ9WKvv/667rrrLqWnp8vn8yk/P7/JcuecFixYoPT0dEVHR+vmm29WYWFh9xRrkJubq2uvvVbx8fFKSUnRl7/8Ze3fv7/JOl7pddmyZRo9enTg4ng5OTl6+eWXA8u90uel5Obmyufzac6cOYH7vNTvggUL5PP5mtxSU1MDy73UqyQdO3ZM06dPV79+/RQTE6OxY8dqx44dgeVe6Xfw4MEX7Vefz6cHHnhAknf6lKSzZ8/qRz/6kbKyshQdHa0hQ4bopz/9qfx+f2AdL/XbZg691saNG91jjz3m1q5d6yS5P/zhD02WL1682MXHx7u1a9e63bt3u69+9asuLS3NVVVVdU/B7XTbbbe5FStWuD179rhdu3a5KVOmuEGDBrlTp04F1vFKr+vXr3cvvfSS279/v9u/f7979NFHXXh4uNuzZ49zzjt9XqigoMANHjzYjR492j300EOB+73U7/z5892IESNcSUlJ4FZWVhZY7qVey8vLXWZmpps1a5Z7++23XVFRkduyZYv7+OOPA+t4pd+ysrIm+3Tz5s1Oktu6datzzjt9OufcE0884fr16+c2bNjgioqK3O9+9zsXFxfnnnrqqcA6Xuq3rQhUHnFhoPL7/S41NdUtXrw4cN+ZM2dcYmKie+aZZ7qhwo5TVlbmJLlt27Y557zdq3POXXbZZe65557zbJ/V1dVu2LBhbvPmze5zn/tcIFB5rd/58+e7MWPGXHKZ13p95JFH3I033tjscq/1e76HHnrIDR061Pn9fs/1OWXKFDd79uwm902dOtVNnz7dOeft/RoMPvLzqKKiIpWWlurWW28N3BcZGanPfe5zeuONN7qxMrvKykpJUnJysiTv9trQ0KA1a9bo9OnTysnJ8WyfDzzwgKZMmaJbbrmlyf1e7PfAgQNKT09XVlaW7rnnHh06dEiS93pdv369JkyYoK985StKSUnRuHHjtHz58sByr/XbqK6uTnl5eZo9e7Z8Pp/n+rzxxhv1xz/+UR999JEk6f3339ef//xn3XnnnZK8u1+DxR9H9qjS0lJJ0mc+85km93/mM59RcXFxd5TUIZxzevjhh3XjjTdq5MiRkrzX6+7du5WTk6MzZ84oLi5Of/jDH5SdnR34geSVPiVpzZo1eu+99/TOO+9ctMxr+/W6667Tb37zGw0fPlyffPKJnnjiCX32s59VYWGh53o9dOiQli1bpocffliPPvqoCgoK9N3vfleRkZGaMWOG5/ptlJ+fr4qKCs2aNUuS947hRx55RJWVlbrqqqsUGhqqhoYGLVq0SF/72tckea/ftiJQeZzP52vyf+fcRff1Jg8++KA++OAD/fnPf75omVd6vfLKK7Vr1y5VVFRo7dq1mjlzprZt2xZY7pU+jx49qoceekivvvqqoqKiml3PK/3ecccdgX+PGjVKOTk5Gjp0qFatWqXrr79eknd69fv9mjBhgn72s59JksaNG6fCwkItW7ZMM2bMCKznlX4b/cd//IfuuOMOpaenN7nfK32+8MILysvL0/PPP68RI0Zo165dmjNnjtLT0zVz5szAel7pt634yM+jGr891PgbQ6OysrKLfnvoLb7zne9o/fr12rp1qy6//PLA/V7rNSIiQldccYUmTJig3NxcjRkzRv/2b//muT537NihsrIyXXPNNQoLC1NYWJi2bdumJUuWKCwsLNCTV/q9UGxsrEaNGqUDBw54bt+mpaUpOzu7yX1XX321jhw5Isl7z1lJKi4u1pYtW/T1r389cJ/X+vzBD36gH/7wh7rnnns0atQo3Xffffre976n3NxcSd7rt60IVB6VlZWl1NRUbd68OXBfXV2dtm3bps9+9rPdWFnbOef04IMPat26dfrTn/6krKysJsu91OulOOdUW1vruT4nT56s3bt3a9euXYHbhAkTNG3aNO3atUtDhgzxVL8Xqq2t1b59+5SWlua5fXvDDTdcdGmTjz76SJmZmZK8+ZxdsWKFUlJSNGXKlMB9XuuzpqZGISFNY0NoaGjgsgle67fNuulkeHSA6upqt3PnTrdz504nyf3qV79yO3fudMXFxc65c19fTUxMdOvWrXO7d+92X/va13rl11fvv/9+l5iY6F577bUmX0+uqakJrOOVXufNm+def/11V1RU5D744AP36KOPupCQEPfqq68657zTZ3PO/5afc97q9/vf/7577bXX3KFDh9xbb73l/u7v/s7Fx8e7w4cPO+e81WtBQYELCwtzixYtcgcOHHD/+Z//6WJiYlxeXl5gHS/129DQ4AYNGuQeeeSRi5Z5qc+ZM2e6gQMHBi6bsG7dOte/f383d+7cwDpe6retCFS92NatW52ki24zZ850zp37Cuv8+fNdamqqi4yMdJMmTXK7d+/u3qLb4VI9SnIrVqwIrOOVXmfPnu0yMzNdRESEGzBggJs8eXIgTDnnnT6bc2Gg8lK/jdfjCQ8Pd+np6W7q1KmusLAwsNxLvTrn3IsvvuhGjhzpIiMj3VVXXeWeffbZJsu91O8rr7ziJLn9+/dftMxLfVZVVbmHHnrIDRo0yEVFRbkhQ4a4xx57zNXW1gbW8VK/beVzzrlueWsMAADAIziHCgAAwIhABQAAYESgAgAAMCJQAQAAGBGoAAAAjAhUAAAARgQqAAAAIwIVAACAEYEKAADAiEAFAABgRKACAAAwIlABAAAY/X+p1PCQewSuvwAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"markdown","source":"We don't see any such relation here. It just we have more passenger cars and less number of cars. It has nothing to do with prices but preferences here. So, we can drop this column. ","metadata":{}},{"cell_type":"code","source":"df.drop([\"Vehicle_type\", \"Latest_Launch\"], axis = 1, inplace=True)\ndf","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.870373Z","iopub.execute_input":"2023-06-06T13:35:09.870697Z","iopub.status.idle":"2023-06-06T13:35:09.896496Z","shell.execute_reply.started":"2023-06-06T13:35:09.870670Z","shell.execute_reply":"2023-06-06T13:35:09.895521Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"    Manufacturer    Model  Sales_in_thousands  __year_resale_value  \\\n0          Acura  Integra              16.919               16.360   \n1          Acura       TL              39.384               19.875   \n2          Acura       CL              14.114               18.225   \n3          Acura       RL               8.588               29.725   \n4           Audi       A4              20.397               22.255   \n..           ...      ...                 ...                  ...   \n152        Volvo      V40               3.545                  NaN   \n153        Volvo      S70              15.245                  NaN   \n154        Volvo      V70              17.531                  NaN   \n155        Volvo      C70               3.493                  NaN   \n156        Volvo      S80              18.969                  NaN   \n\n     Price_in_thousands  Engine_size  Horsepower  Wheelbase  Width  Length  \\\n0                 21.50          1.8       140.0      101.2   67.3   172.4   \n1                 28.40          3.2       225.0      108.1   70.3   192.9   \n2                   NaN          3.2       225.0      106.9   70.6   192.0   \n3                 42.00          3.5       210.0      114.6   71.4   196.6   \n4                 23.99          1.8       150.0      102.6   68.2   178.0   \n..                  ...          ...         ...        ...    ...     ...   \n152               24.40          1.9       160.0      100.5   67.6   176.6   \n153               27.50          2.4       168.0      104.9   69.3   185.9   \n154               28.80          2.4       168.0      104.9   69.3   186.2   \n155               45.50          2.3       236.0      104.9   71.5   185.7   \n156               36.00          2.9       201.0      109.9   72.1   189.8   \n\n     Curb_weight  Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n0          2.639           13.2             28.0          58.280150  \n1          3.517           17.2             25.0          91.370778  \n2          3.470           17.2             26.0                NaN  \n3          3.850           18.0             22.0          91.389779  \n4          2.998           16.4             27.0          62.777639  \n..           ...            ...              ...                ...  \n152        3.042           15.8             25.0          66.498812  \n153        3.208           17.9             25.0          70.654495  \n154        3.259           17.9             25.0          71.155978  \n155        3.601           18.5             23.0         101.623357  \n156        3.600           21.1             24.0          85.735655  \n\n[157 rows x 14 columns]","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>Manufacturer</th>\n      <th>Model</th>\n      <th>Sales_in_thousands</th>\n      <th>__year_resale_value</th>\n      <th>Price_in_thousands</th>\n      <th>Engine_size</th>\n      <th>Horsepower</th>\n      <th>Wheelbase</th>\n      <th>Width</th>\n      <th>Length</th>\n      <th>Curb_weight</th>\n      <th>Fuel_capacity</th>\n      <th>Fuel_efficiency</th>\n      <th>Power_perf_factor</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Acura</td>\n      <td>Integra</td>\n      <td>16.919</td>\n      <td>16.360</td>\n      <td>21.50</td>\n      <td>1.8</td>\n      <td>140.0</td>\n      <td>101.2</td>\n      <td>67.3</td>\n      <td>172.4</td>\n      <td>2.639</td>\n      <td>13.2</td>\n      <td>28.0</td>\n      <td>58.280150</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Acura</td>\n      <td>TL</td>\n      <td>39.384</td>\n      <td>19.875</td>\n      <td>28.40</td>\n      <td>3.2</td>\n      <td>225.0</td>\n      <td>108.1</td>\n      <td>70.3</td>\n      <td>192.9</td>\n      <td>3.517</td>\n      <td>17.2</td>\n      <td>25.0</td>\n      <td>91.370778</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Acura</td>\n      <td>CL</td>\n      <td>14.114</td>\n      <td>18.225</td>\n      <td>NaN</td>\n      <td>3.2</td>\n      <td>225.0</td>\n      <td>106.9</td>\n      <td>70.6</td>\n      <td>192.0</td>\n      <td>3.470</td>\n      <td>17.2</td>\n      <td>26.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Acura</td>\n      <td>RL</td>\n      <td>8.588</td>\n      <td>29.725</td>\n      <td>42.00</td>\n      <td>3.5</td>\n      <td>210.0</td>\n      <td>114.6</td>\n      <td>71.4</td>\n      <td>196.6</td>\n      <td>3.850</td>\n      <td>18.0</td>\n      <td>22.0</td>\n      <td>91.389779</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Audi</td>\n      <td>A4</td>\n      <td>20.397</td>\n      <td>22.255</td>\n      <td>23.99</td>\n      <td>1.8</td>\n      <td>150.0</td>\n      <td>102.6</td>\n      <td>68.2</td>\n      <td>178.0</td>\n      <td>2.998</td>\n      <td>16.4</td>\n      <td>27.0</td>\n      <td>62.777639</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>152</th>\n      <td>Volvo</td>\n      <td>V40</td>\n      <td>3.545</td>\n      <td>NaN</td>\n      <td>24.40</td>\n      <td>1.9</td>\n      <td>160.0</td>\n      <td>100.5</td>\n      <td>67.6</td>\n      <td>176.6</td>\n      <td>3.042</td>\n      <td>15.8</td>\n      <td>25.0</td>\n      <td>66.498812</td>\n    </tr>\n    <tr>\n      <th>153</th>\n      <td>Volvo</td>\n      <td>S70</td>\n      <td>15.245</td>\n      <td>NaN</td>\n      <td>27.50</td>\n      <td>2.4</td>\n      <td>168.0</td>\n      <td>104.9</td>\n      <td>69.3</td>\n      <td>185.9</td>\n      <td>3.208</td>\n      <td>17.9</td>\n      <td>25.0</td>\n      <td>70.654495</td>\n    </tr>\n    <tr>\n      <th>154</th>\n      <td>Volvo</td>\n      <td>V70</td>\n      <td>17.531</td>\n      <td>NaN</td>\n      <td>28.80</td>\n      <td>2.4</td>\n      <td>168.0</td>\n      <td>104.9</td>\n      <td>69.3</td>\n      <td>186.2</td>\n      <td>3.259</td>\n      <td>17.9</td>\n      <td>25.0</td>\n      <td>71.155978</td>\n    </tr>\n    <tr>\n      <th>155</th>\n      <td>Volvo</td>\n      <td>C70</td>\n      <td>3.493</td>\n      <td>NaN</td>\n      <td>45.50</td>\n      <td>2.3</td>\n      <td>236.0</td>\n      <td>104.9</td>\n      <td>71.5</td>\n      <td>185.7</td>\n      <td>3.601</td>\n      <td>18.5</td>\n      <td>23.0</td>\n      <td>101.623357</td>\n    </tr>\n    <tr>\n      <th>156</th>\n      <td>Volvo</td>\n      <td>S80</td>\n      <td>18.969</td>\n      <td>NaN</td>\n      <td>36.00</td>\n      <td>2.9</td>\n      <td>201.0</td>\n      <td>109.9</td>\n      <td>72.1</td>\n      <td>189.8</td>\n      <td>3.600</td>\n      <td>21.1</td>\n      <td>24.0</td>\n      <td>85.735655</td>\n    </tr>\n  </tbody>\n</table>\n<p>157 rows × 14 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# let's check the shapen and size of data\nlen(df.columns), df.shape","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.897565Z","iopub.execute_input":"2023-06-06T13:35:09.897827Z","iopub.status.idle":"2023-06-06T13:35:09.903540Z","shell.execute_reply.started":"2023-06-06T13:35:09.897805Z","shell.execute_reply":"2023-06-06T13:35:09.902715Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"(14, (157, 14))"},"metadata":{}}]},{"cell_type":"code","source":"# let's check the data distribution of our datasets\ndf.hist(bins=50, figsize=(20,15))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:09.904819Z","iopub.execute_input":"2023-06-06T13:35:09.905843Z","iopub.status.idle":"2023-06-06T13:35:12.565954Z","shell.execute_reply.started":"2023-06-06T13:35:09.905812Z","shell.execute_reply":"2023-06-06T13:35:12.564993Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1500 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we can se most of our graphs are normally distributed other than a few graphs that have a bit skewness towards the left and a few of the graphs may have outliers. We can remove those outliers if we want but due to small dataset let's not do that and work out. ","metadata":{}},{"cell_type":"markdown","source":"## Creating Training And Test Sets","metadata":{}},{"cell_type":"code","source":"np.random.seed(42)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:12.566785Z","iopub.execute_input":"2023-06-06T13:35:12.567089Z","iopub.status.idle":"2023-06-06T13:35:12.572953Z","shell.execute_reply.started":"2023-06-06T13:35:12.567065Z","shell.execute_reply":"2023-06-06T13:35:12.571507Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\ntrain_set, test_set = train_test_split(df, test_size=0.2, random_state=42)\ntest_set.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:12.574582Z","iopub.execute_input":"2023-06-06T13:35:12.574921Z","iopub.status.idle":"2023-06-06T13:35:13.047983Z","shell.execute_reply.started":"2023-06-06T13:35:12.574893Z","shell.execute_reply":"2023-06-06T13:35:13.047012Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"    Manufacturer           Model  Sales_in_thousands  __year_resale_value  \\\n126      Porsche  Carrera Cabrio               1.866               67.550   \n45         Dodge         Caravan             181.749               12.025   \n133       Saturn              LS              49.989                  NaN   \n138       Toyota          Avalon              63.849               18.140   \n111   Oldsmobile          Aurora              14.690               19.890   \n\n     Price_in_thousands  Engine_size  Horsepower  Wheelbase  Width  Length  \\\n126              74.970          3.4       300.0       92.6   69.5   174.5   \n45               19.565          2.4       150.0      113.3   76.8   186.3   \n133              15.010          2.2       137.0      106.5   69.0   190.4   \n138              25.545          3.0       210.0      107.1   71.7   191.9   \n111              36.229          4.0       250.0      113.8   74.4   205.4   \n\n     Curb_weight  Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n126        3.075           17.0             23.0         135.914710  \n45         3.533           20.0             24.0          61.227000  \n133        2.910           13.1             28.0          54.819728  \n138        3.417           18.5             26.0          84.911898  \n111        3.967           18.5             22.0         103.441693  ","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>Manufacturer</th>\n      <th>Model</th>\n      <th>Sales_in_thousands</th>\n      <th>__year_resale_value</th>\n      <th>Price_in_thousands</th>\n      <th>Engine_size</th>\n      <th>Horsepower</th>\n      <th>Wheelbase</th>\n      <th>Width</th>\n      <th>Length</th>\n      <th>Curb_weight</th>\n      <th>Fuel_capacity</th>\n      <th>Fuel_efficiency</th>\n      <th>Power_perf_factor</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>126</th>\n      <td>Porsche</td>\n      <td>Carrera Cabrio</td>\n      <td>1.866</td>\n      <td>67.550</td>\n      <td>74.970</td>\n      <td>3.4</td>\n      <td>300.0</td>\n      <td>92.6</td>\n      <td>69.5</td>\n      <td>174.5</td>\n      <td>3.075</td>\n      <td>17.0</td>\n      <td>23.0</td>\n      <td>135.914710</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>Dodge</td>\n      <td>Caravan</td>\n      <td>181.749</td>\n      <td>12.025</td>\n      <td>19.565</td>\n      <td>2.4</td>\n      <td>150.0</td>\n      <td>113.3</td>\n      <td>76.8</td>\n      <td>186.3</td>\n      <td>3.533</td>\n      <td>20.0</td>\n      <td>24.0</td>\n      <td>61.227000</td>\n    </tr>\n    <tr>\n      <th>133</th>\n      <td>Saturn</td>\n      <td>LS</td>\n      <td>49.989</td>\n      <td>NaN</td>\n      <td>15.010</td>\n      <td>2.2</td>\n      <td>137.0</td>\n      <td>106.5</td>\n      <td>69.0</td>\n      <td>190.4</td>\n      <td>2.910</td>\n      <td>13.1</td>\n      <td>28.0</td>\n      <td>54.819728</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>Toyota</td>\n      <td>Avalon</td>\n      <td>63.849</td>\n      <td>18.140</td>\n      <td>25.545</td>\n      <td>3.0</td>\n      <td>210.0</td>\n      <td>107.1</td>\n      <td>71.7</td>\n      <td>191.9</td>\n      <td>3.417</td>\n      <td>18.5</td>\n      <td>26.0</td>\n      <td>84.911898</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>Oldsmobile</td>\n      <td>Aurora</td>\n      <td>14.690</td>\n      <td>19.890</td>\n      <td>36.229</td>\n      <td>4.0</td>\n      <td>250.0</td>\n      <td>113.8</td>\n      <td>74.4</td>\n      <td>205.4</td>\n      <td>3.967</td>\n      <td>18.5</td>\n      <td>22.0</td>\n      <td>103.441693</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"len(test_set), len(train_set)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:13.049124Z","iopub.execute_input":"2023-06-06T13:35:13.049447Z","iopub.status.idle":"2023-06-06T13:35:13.056343Z","shell.execute_reply.started":"2023-06-06T13:35:13.049421Z","shell.execute_reply":"2023-06-06T13:35:13.054743Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"(32, 125)"},"metadata":{}}]},{"cell_type":"markdown","source":"We have 125 datapoints for training and 32 for test.","metadata":{}},{"cell_type":"markdown","source":"## Working With Training Set","metadata":{}},{"cell_type":"code","source":"# let's create a copy of training set and work on it and let's not touch test set at all\n\nnew_df = train_set.copy()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:13.057591Z","iopub.execute_input":"2023-06-06T13:35:13.057898Z","iopub.status.idle":"2023-06-06T13:35:13.066923Z","shell.execute_reply.started":"2023-06-06T13:35:13.057868Z","shell.execute_reply":"2023-06-06T13:35:13.066130Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"new_df.T","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:13.068755Z","iopub.execute_input":"2023-06-06T13:35:13.069189Z","iopub.status.idle":"2023-06-06T13:35:13.099098Z","shell.execute_reply.started":"2023-06-06T13:35:13.069155Z","shell.execute_reply":"2023-06-06T13:35:13.098087Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"                           136        55         124         84          105  \\\nManufacturer            Toyota       Ford    Porsche  Mitsubishi      Nissan   \nModel                  Corolla     Ranger     Boxter     Montero  Pathfinder   \nSales_in_thousands     142.535     220.65      8.982      11.337      42.574   \n__year_resale_value     10.025       7.85      41.25      19.125       17.81   \nPrice_in_thousands      13.108      12.05      41.43      31.807      29.299   \nEngine_size                1.8        2.5        2.7         3.5         3.3   \nHorsepower               120.0      119.0      217.0       200.0       170.0   \nWheelbase                 97.0      117.5       95.2       107.3       106.3   \nWidth                     66.7       69.4       70.1        69.9        71.7   \nLength                   174.0      200.7      171.0       186.6       182.6   \nCurb_weight               2.42      3.086      2.778        4.52       3.947   \nFuel_capacity             13.2       20.0       17.0        24.3        21.0   \nFuel_efficiency           33.0       23.0       22.0        18.0        19.0   \nPower_perf_factor    47.968972  47.389531  93.437331   83.920815   72.290355   \n\n                           42         67         127        26         135  \\\nManufacturer             Dodge       Jeep       Saab  Chevrolet     Subaru   \nModel                  Ram Van   Wrangler      5-Sep      Metro   Forester   \nSales_in_thousands      31.038     55.557      9.191     21.855     33.028   \n__year_resale_value     13.425     13.475        NaN       5.16        NaN   \nPrice_in_thousands      18.575      14.46      33.12      9.235     20.095   \nEngine_size                3.9        2.5        2.3        1.0        2.5   \nHorsepower               175.0      120.0      170.0       55.0      165.0   \nWheelbase                127.2       93.4      106.4       93.1       99.4   \nWidth                     78.8       66.7       70.6       62.6       68.3   \nLength                   208.5      152.0      189.2      149.4      175.2   \nCurb_weight              4.298      3.045       3.28      1.895      3.125   \nFuel_capacity             32.0       19.0       18.5       10.3       15.9   \nFuel_efficiency           16.0       17.0       23.0       45.0       24.0   \nPower_perf_factor    70.078322  48.672898  73.503778  23.276272  66.762943   \n\n                     ...        87          74          121        156  \\\nManufacturer         ...    Mercury       Lexus     Pontiac      Volvo   \nModel                ...     Cougar       LX470  Grand Prix        S80   \nSales_in_thousands   ...     26.529       9.126      92.364     18.969   \n__year_resale_value  ...      13.89         NaN       14.01        NaN   \nPrice_in_thousands   ...      16.54      60.105      21.665       36.0   \nEngine_size          ...        2.0         4.7         3.8        2.9   \nHorsepower           ...      125.0       230.0       195.0      201.0   \nWheelbase            ...      106.4       112.2       110.5      109.9   \nWidth                ...       69.6        76.4        72.7       72.1   \nLength               ...      185.0       192.5       196.5      189.8   \nCurb_weight          ...      2.892       5.401       3.396        3.6   \nFuel_capacity        ...       16.0        25.4        18.0       21.1   \nFuel_efficiency      ...       30.0        15.0        25.0       24.0   \nPower_perf_factor    ...  51.113474  105.760458   78.318168  85.735655   \n\n                           20         71         106         14          92   \\\nManufacturer         Chevrolet      Lexus     Nissan    Cadillac  Mercedes-B   \nModel                   Malibu      GS300     Xterra     DeVille     C-Class   \nSales_in_thousands     135.126     12.698     54.158      63.729      18.392   \n__year_resale_value     11.225     32.075        NaN      22.525       26.05   \nPrice_in_thousands      16.535     37.805     22.799      39.895       31.75   \nEngine_size                3.1        3.0        3.3         4.6         2.3   \nHorsepower               170.0      225.0      170.0       275.0       185.0   \nWheelbase                107.0      110.2      104.3       115.3       105.9   \nWidth                     69.4       70.9       70.4        74.5        67.7   \nLength                   190.4      189.2      178.0       207.2       177.4   \nCurb_weight              3.051      3.638      3.821       3.978        3.25   \nFuel_capacity             15.0       19.8       19.4        18.5        16.4   \nFuel_efficiency           25.0       23.0       18.0        22.0        26.0   \nPower_perf_factor    67.314462  94.946698  69.782944  113.854598   78.280731   \n\n                           102  \nManufacturer            Nissan  \nModel                   Altima  \nSales_in_thousands      88.094  \n__year_resale_value     11.295  \nPrice_in_thousands       20.39  \nEngine_size                2.4  \nHorsepower               155.0  \nWheelbase                103.1  \nWidth                     69.1  \nLength                   183.5  \nCurb_weight              3.012  \nFuel_capacity             15.9  \nFuel_efficiency           25.0  \nPower_perf_factor    63.313728  \n\n[14 rows x 125 columns]","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>136</th>\n      <th>55</th>\n      <th>124</th>\n      <th>84</th>\n      <th>105</th>\n      <th>42</th>\n      <th>67</th>\n      <th>127</th>\n      <th>26</th>\n      <th>135</th>\n      <th>...</th>\n      <th>87</th>\n      <th>74</th>\n      <th>121</th>\n      <th>156</th>\n      <th>20</th>\n      <th>71</th>\n      <th>106</th>\n      <th>14</th>\n      <th>92</th>\n      <th>102</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Manufacturer</th>\n      <td>Toyota</td>\n      <td>Ford</td>\n      <td>Porsche</td>\n      <td>Mitsubishi</td>\n      <td>Nissan</td>\n      <td>Dodge</td>\n      <td>Jeep</td>\n      <td>Saab</td>\n      <td>Chevrolet</td>\n      <td>Subaru</td>\n      <td>...</td>\n      <td>Mercury</td>\n      <td>Lexus</td>\n      <td>Pontiac</td>\n      <td>Volvo</td>\n      <td>Chevrolet</td>\n      <td>Lexus</td>\n      <td>Nissan</td>\n      <td>Cadillac</td>\n      <td>Mercedes-B</td>\n      <td>Nissan</td>\n    </tr>\n    <tr>\n      <th>Model</th>\n      <td>Corolla</td>\n      <td>Ranger</td>\n      <td>Boxter</td>\n      <td>Montero</td>\n      <td>Pathfinder</td>\n      <td>Ram Van</td>\n      <td>Wrangler</td>\n      <td>5-Sep</td>\n      <td>Metro</td>\n      <td>Forester</td>\n      <td>...</td>\n      <td>Cougar</td>\n      <td>LX470</td>\n      <td>Grand Prix</td>\n      <td>S80</td>\n      <td>Malibu</td>\n      <td>GS300</td>\n      <td>Xterra</td>\n      <td>DeVille</td>\n      <td>C-Class</td>\n      <td>Altima</td>\n    </tr>\n    <tr>\n      <th>Sales_in_thousands</th>\n      <td>142.535</td>\n      <td>220.65</td>\n      <td>8.982</td>\n      <td>11.337</td>\n      <td>42.574</td>\n      <td>31.038</td>\n      <td>55.557</td>\n      <td>9.191</td>\n      <td>21.855</td>\n      <td>33.028</td>\n      <td>...</td>\n      <td>26.529</td>\n      <td>9.126</td>\n      <td>92.364</td>\n      <td>18.969</td>\n      <td>135.126</td>\n      <td>12.698</td>\n      <td>54.158</td>\n      <td>63.729</td>\n      <td>18.392</td>\n      <td>88.094</td>\n    </tr>\n    <tr>\n      <th>__year_resale_value</th>\n      <td>10.025</td>\n      <td>7.85</td>\n      <td>41.25</td>\n      <td>19.125</td>\n      <td>17.81</td>\n      <td>13.425</td>\n      <td>13.475</td>\n      <td>NaN</td>\n      <td>5.16</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>13.89</td>\n      <td>NaN</td>\n      <td>14.01</td>\n      <td>NaN</td>\n      <td>11.225</td>\n      <td>32.075</td>\n      <td>NaN</td>\n      <td>22.525</td>\n      <td>26.05</td>\n      <td>11.295</td>\n    </tr>\n    <tr>\n      <th>Price_in_thousands</th>\n      <td>13.108</td>\n      <td>12.05</td>\n      <td>41.43</td>\n      <td>31.807</td>\n      <td>29.299</td>\n      <td>18.575</td>\n      <td>14.46</td>\n      <td>33.12</td>\n      <td>9.235</td>\n      <td>20.095</td>\n      <td>...</td>\n      <td>16.54</td>\n      <td>60.105</td>\n      <td>21.665</td>\n      <td>36.0</td>\n      <td>16.535</td>\n      <td>37.805</td>\n      <td>22.799</td>\n      <td>39.895</td>\n      <td>31.75</td>\n      <td>20.39</td>\n    </tr>\n    <tr>\n      <th>Engine_size</th>\n      <td>1.8</td>\n      <td>2.5</td>\n      <td>2.7</td>\n      <td>3.5</td>\n      <td>3.3</td>\n      <td>3.9</td>\n      <td>2.5</td>\n      <td>2.3</td>\n      <td>1.0</td>\n      <td>2.5</td>\n      <td>...</td>\n      <td>2.0</td>\n      <td>4.7</td>\n      <td>3.8</td>\n      <td>2.9</td>\n      <td>3.1</td>\n      <td>3.0</td>\n      <td>3.3</td>\n      <td>4.6</td>\n      <td>2.3</td>\n      <td>2.4</td>\n    </tr>\n    <tr>\n      <th>Horsepower</th>\n      <td>120.0</td>\n      <td>119.0</td>\n      <td>217.0</td>\n      <td>200.0</td>\n      <td>170.0</td>\n      <td>175.0</td>\n      <td>120.0</td>\n      <td>170.0</td>\n      <td>55.0</td>\n      <td>165.0</td>\n      <td>...</td>\n      <td>125.0</td>\n      <td>230.0</td>\n      <td>195.0</td>\n      <td>201.0</td>\n      <td>170.0</td>\n      <td>225.0</td>\n      <td>170.0</td>\n      <td>275.0</td>\n      <td>185.0</td>\n      <td>155.0</td>\n    </tr>\n    <tr>\n      <th>Wheelbase</th>\n      <td>97.0</td>\n      <td>117.5</td>\n      <td>95.2</td>\n      <td>107.3</td>\n      <td>106.3</td>\n      <td>127.2</td>\n      <td>93.4</td>\n      <td>106.4</td>\n      <td>93.1</td>\n      <td>99.4</td>\n      <td>...</td>\n      <td>106.4</td>\n      <td>112.2</td>\n      <td>110.5</td>\n      <td>109.9</td>\n      <td>107.0</td>\n      <td>110.2</td>\n      <td>104.3</td>\n      <td>115.3</td>\n      <td>105.9</td>\n      <td>103.1</td>\n    </tr>\n    <tr>\n      <th>Width</th>\n      <td>66.7</td>\n      <td>69.4</td>\n      <td>70.1</td>\n      <td>69.9</td>\n      <td>71.7</td>\n      <td>78.8</td>\n      <td>66.7</td>\n      <td>70.6</td>\n      <td>62.6</td>\n      <td>68.3</td>\n      <td>...</td>\n      <td>69.6</td>\n      <td>76.4</td>\n      <td>72.7</td>\n      <td>72.1</td>\n      <td>69.4</td>\n      <td>70.9</td>\n      <td>70.4</td>\n      <td>74.5</td>\n      <td>67.7</td>\n      <td>69.1</td>\n    </tr>\n    <tr>\n      <th>Length</th>\n      <td>174.0</td>\n      <td>200.7</td>\n      <td>171.0</td>\n      <td>186.6</td>\n      <td>182.6</td>\n      <td>208.5</td>\n      <td>152.0</td>\n      <td>189.2</td>\n      <td>149.4</td>\n      <td>175.2</td>\n      <td>...</td>\n      <td>185.0</td>\n      <td>192.5</td>\n      <td>196.5</td>\n      <td>189.8</td>\n      <td>190.4</td>\n      <td>189.2</td>\n      <td>178.0</td>\n      <td>207.2</td>\n      <td>177.4</td>\n      <td>183.5</td>\n    </tr>\n    <tr>\n      <th>Curb_weight</th>\n      <td>2.42</td>\n      <td>3.086</td>\n      <td>2.778</td>\n      <td>4.52</td>\n      <td>3.947</td>\n      <td>4.298</td>\n      <td>3.045</td>\n      <td>3.28</td>\n      <td>1.895</td>\n      <td>3.125</td>\n      <td>...</td>\n      <td>2.892</td>\n      <td>5.401</td>\n      <td>3.396</td>\n      <td>3.6</td>\n      <td>3.051</td>\n      <td>3.638</td>\n      <td>3.821</td>\n      <td>3.978</td>\n      <td>3.25</td>\n      <td>3.012</td>\n    </tr>\n    <tr>\n      <th>Fuel_capacity</th>\n      <td>13.2</td>\n      <td>20.0</td>\n      <td>17.0</td>\n      <td>24.3</td>\n      <td>21.0</td>\n      <td>32.0</td>\n      <td>19.0</td>\n      <td>18.5</td>\n      <td>10.3</td>\n      <td>15.9</td>\n      <td>...</td>\n      <td>16.0</td>\n      <td>25.4</td>\n      <td>18.0</td>\n      <td>21.1</td>\n      <td>15.0</td>\n      <td>19.8</td>\n      <td>19.4</td>\n      <td>18.5</td>\n      <td>16.4</td>\n      <td>15.9</td>\n    </tr>\n    <tr>\n      <th>Fuel_efficiency</th>\n      <td>33.0</td>\n      <td>23.0</td>\n      <td>22.0</td>\n      <td>18.0</td>\n      <td>19.0</td>\n      <td>16.0</td>\n      <td>17.0</td>\n      <td>23.0</td>\n      <td>45.0</td>\n      <td>24.0</td>\n      <td>...</td>\n      <td>30.0</td>\n      <td>15.0</td>\n      <td>25.0</td>\n      <td>24.0</td>\n      <td>25.0</td>\n      <td>23.0</td>\n      <td>18.0</td>\n      <td>22.0</td>\n      <td>26.0</td>\n      <td>25.0</td>\n    </tr>\n    <tr>\n      <th>Power_perf_factor</th>\n      <td>47.968972</td>\n      <td>47.389531</td>\n      <td>93.437331</td>\n      <td>83.920815</td>\n      <td>72.290355</td>\n      <td>70.078322</td>\n      <td>48.672898</td>\n      <td>73.503778</td>\n      <td>23.276272</td>\n      <td>66.762943</td>\n      <td>...</td>\n      <td>51.113474</td>\n      <td>105.760458</td>\n      <td>78.318168</td>\n      <td>85.735655</td>\n      <td>67.314462</td>\n      <td>94.946698</td>\n      <td>69.782944</td>\n      <td>113.854598</td>\n      <td>78.280731</td>\n      <td>63.313728</td>\n    </tr>\n  </tbody>\n</table>\n<p>14 rows × 125 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"Let first understand what are the columns involved and how they can be important:\n* Wheelbase: the distance between the front and rear axles of a vehicle. This may be!\n* Engine size is the volume of fuel and air that can be pushed through a car's cylinders and is measured in cubic centimetres (cc. This may also be important.\n* Year Resale Value: The price which something can achieve when sold in the future. This may vary depending on the brand value so this may also be a determining factor in car prices.\n* Curb Weight: the weight of an automotive vehicle including fuel, coolant, and lubricants but excluding occupants and cargo. This may also be an important feature\n* Rest other factors like fuel efficiency or fuel capacity may also be important","metadata":{}},{"cell_type":"code","source":"# let's check the correlation \ncorr_matrix = new_df.corr(numeric_only=True)\ncorr_matrix[\"Price_in_thousands\"].sort_values(ascending=False)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:13.100304Z","iopub.execute_input":"2023-06-06T13:35:13.100580Z","iopub.status.idle":"2023-06-06T13:35:13.117896Z","shell.execute_reply.started":"2023-06-06T13:35:13.100558Z","shell.execute_reply":"2023-06-06T13:35:13.116649Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"Price_in_thousands     1.000000\n__year_resale_value    0.931248\nPower_perf_factor      0.892800\nHorsepower             0.838090\nEngine_size            0.675106\nCurb_weight            0.603102\nFuel_capacity          0.462366\nWidth                  0.420226\nLength                 0.257026\nWheelbase              0.249175\nSales_in_thousands    -0.345753\nFuel_efficiency       -0.530948\nName: Price_in_thousands, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"Here we can see how features are correlated with our price. It seems like year resale value is what people look for while purchasing a car but this is just based on this output. There can be other factors. Also power perf factor is also important here.","metadata":{}},{"cell_type":"code","source":"new_df.plot(kind=\"scatter\", x=\"Power_perf_factor\", y=\"Price_in_thousands\");","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:13.119343Z","iopub.execute_input":"2023-06-06T13:35:13.119621Z","iopub.status.idle":"2023-06-06T13:35:13.342056Z","shell.execute_reply.started":"2023-06-06T13:35:13.119597Z","shell.execute_reply":"2023-06-06T13:35:13.341420Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"new_df.plot(kind=\"scatter\", x=\"Fuel_efficiency\", y=\"Price_in_thousands\");","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:13.342881Z","iopub.execute_input":"2023-06-06T13:35:13.343316Z","iopub.status.idle":"2023-06-06T13:35:13.567177Z","shell.execute_reply.started":"2023-06-06T13:35:13.343295Z","shell.execute_reply":"2023-06-06T13:35:13.565929Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"markdown","source":"The lower the fuel efficiecy the better it is that's why the car prices are higher on the left most side. Although other factors may also be the reason behind some of the lower prices.","metadata":{}},{"cell_type":"code","source":"# let's import searborn as sns and check our correlation between feastures\nimport seaborn as sns \nplt.figure(figsize=(10,15))\nsns.heatmap(corr_matrix, annot=True, cmap=\"YlGnBu\");","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:13.568463Z","iopub.execute_input":"2023-06-06T13:35:13.568781Z","iopub.status.idle":"2023-06-06T13:35:14.540760Z","shell.execute_reply.started":"2023-06-06T13:35:13.568755Z","shell.execute_reply":"2023-06-06T13:35:14.539699Z"},"trusted":true},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x1500 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"## Preparing The Data For The Model","metadata":{}},{"cell_type":"code","source":"# let's first check the data type \nnew_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:14.541897Z","iopub.execute_input":"2023-06-06T13:35:14.542176Z","iopub.status.idle":"2023-06-06T13:35:14.551560Z","shell.execute_reply.started":"2023-06-06T13:35:14.542154Z","shell.execute_reply":"2023-06-06T13:35:14.550255Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"Manufacturer            object\nModel                   object\nSales_in_thousands     float64\n__year_resale_value    float64\nPrice_in_thousands     float64\nEngine_size            float64\nHorsepower             float64\nWheelbase              float64\nWidth                  float64\nLength                 float64\nCurb_weight            float64\nFuel_capacity          float64\nFuel_efficiency        float64\nPower_perf_factor      float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"# let's create a new copy of training data by dropping the labels \n\nX_train = new_df.drop(\"Price_in_thousands\", axis=1)\nX_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:14.552643Z","iopub.execute_input":"2023-06-06T13:35:14.552907Z","iopub.status.idle":"2023-06-06T13:35:14.577418Z","shell.execute_reply.started":"2023-06-06T13:35:14.552886Z","shell.execute_reply":"2023-06-06T13:35:14.576136Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"    Manufacturer       Model  Sales_in_thousands  __year_resale_value  \\\n136       Toyota     Corolla             142.535               10.025   \n55          Ford      Ranger             220.650                7.850   \n124      Porsche      Boxter               8.982               41.250   \n84    Mitsubishi     Montero              11.337               19.125   \n105       Nissan  Pathfinder              42.574               17.810   \n\n     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n136          1.8       120.0       97.0   66.7   174.0        2.420   \n55           2.5       119.0      117.5   69.4   200.7        3.086   \n124          2.7       217.0       95.2   70.1   171.0        2.778   \n84           3.5       200.0      107.3   69.9   186.6        4.520   \n105          3.3       170.0      106.3   71.7   182.6        3.947   \n\n     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n136           13.2             33.0          47.968972  \n55            20.0             23.0          47.389531  \n124           17.0             22.0          93.437331  \n84            24.3             18.0          83.920815  \n105           21.0             19.0          72.290355  ","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>Manufacturer</th>\n      <th>Model</th>\n      <th>Sales_in_thousands</th>\n      <th>__year_resale_value</th>\n      <th>Engine_size</th>\n      <th>Horsepower</th>\n      <th>Wheelbase</th>\n      <th>Width</th>\n      <th>Length</th>\n      <th>Curb_weight</th>\n      <th>Fuel_capacity</th>\n      <th>Fuel_efficiency</th>\n      <th>Power_perf_factor</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>136</th>\n      <td>Toyota</td>\n      <td>Corolla</td>\n      <td>142.535</td>\n      <td>10.025</td>\n      <td>1.8</td>\n      <td>120.0</td>\n      <td>97.0</td>\n      <td>66.7</td>\n      <td>174.0</td>\n      <td>2.420</td>\n      <td>13.2</td>\n      <td>33.0</td>\n      <td>47.968972</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Ford</td>\n      <td>Ranger</td>\n      <td>220.650</td>\n      <td>7.850</td>\n      <td>2.5</td>\n      <td>119.0</td>\n      <td>117.5</td>\n      <td>69.4</td>\n      <td>200.7</td>\n      <td>3.086</td>\n      <td>20.0</td>\n      <td>23.0</td>\n      <td>47.389531</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>Porsche</td>\n      <td>Boxter</td>\n      <td>8.982</td>\n      <td>41.250</td>\n      <td>2.7</td>\n      <td>217.0</td>\n      <td>95.2</td>\n      <td>70.1</td>\n      <td>171.0</td>\n      <td>2.778</td>\n      <td>17.0</td>\n      <td>22.0</td>\n      <td>93.437331</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>Mitsubishi</td>\n      <td>Montero</td>\n      <td>11.337</td>\n      <td>19.125</td>\n      <td>3.5</td>\n      <td>200.0</td>\n      <td>107.3</td>\n      <td>69.9</td>\n      <td>186.6</td>\n      <td>4.520</td>\n      <td>24.3</td>\n      <td>18.0</td>\n      <td>83.920815</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>Nissan</td>\n      <td>Pathfinder</td>\n      <td>42.574</td>\n      <td>17.810</td>\n      <td>3.3</td>\n      <td>170.0</td>\n      <td>106.3</td>\n      <td>71.7</td>\n      <td>182.6</td>\n      <td>3.947</td>\n      <td>21.0</td>\n      <td>19.0</td>\n      <td>72.290355</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"new_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:14.584202Z","iopub.execute_input":"2023-06-06T13:35:14.584528Z","iopub.status.idle":"2023-06-06T13:35:14.602604Z","shell.execute_reply.started":"2023-06-06T13:35:14.584502Z","shell.execute_reply":"2023-06-06T13:35:14.601965Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"    Manufacturer       Model  Sales_in_thousands  __year_resale_value  \\\n136       Toyota     Corolla             142.535               10.025   \n55          Ford      Ranger             220.650                7.850   \n124      Porsche      Boxter               8.982               41.250   \n84    Mitsubishi     Montero              11.337               19.125   \n105       Nissan  Pathfinder              42.574               17.810   \n\n     Price_in_thousands  Engine_size  Horsepower  Wheelbase  Width  Length  \\\n136              13.108          1.8       120.0       97.0   66.7   174.0   \n55               12.050          2.5       119.0      117.5   69.4   200.7   \n124              41.430          2.7       217.0       95.2   70.1   171.0   \n84               31.807          3.5       200.0      107.3   69.9   186.6   \n105              29.299          3.3       170.0      106.3   71.7   182.6   \n\n     Curb_weight  Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n136        2.420           13.2             33.0          47.968972  \n55         3.086           20.0             23.0          47.389531  \n124        2.778           17.0             22.0          93.437331  \n84         4.520           24.3             18.0          83.920815  \n105        3.947           21.0             19.0          72.290355  ","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>Manufacturer</th>\n      <th>Model</th>\n      <th>Sales_in_thousands</th>\n      <th>__year_resale_value</th>\n      <th>Price_in_thousands</th>\n      <th>Engine_size</th>\n      <th>Horsepower</th>\n      <th>Wheelbase</th>\n      <th>Width</th>\n      <th>Length</th>\n      <th>Curb_weight</th>\n      <th>Fuel_capacity</th>\n      <th>Fuel_efficiency</th>\n      <th>Power_perf_factor</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>136</th>\n      <td>Toyota</td>\n      <td>Corolla</td>\n      <td>142.535</td>\n      <td>10.025</td>\n      <td>13.108</td>\n      <td>1.8</td>\n      <td>120.0</td>\n      <td>97.0</td>\n      <td>66.7</td>\n      <td>174.0</td>\n      <td>2.420</td>\n      <td>13.2</td>\n      <td>33.0</td>\n      <td>47.968972</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Ford</td>\n      <td>Ranger</td>\n      <td>220.650</td>\n      <td>7.850</td>\n      <td>12.050</td>\n      <td>2.5</td>\n      <td>119.0</td>\n      <td>117.5</td>\n      <td>69.4</td>\n      <td>200.7</td>\n      <td>3.086</td>\n      <td>20.0</td>\n      <td>23.0</td>\n      <td>47.389531</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>Porsche</td>\n      <td>Boxter</td>\n      <td>8.982</td>\n      <td>41.250</td>\n      <td>41.430</td>\n      <td>2.7</td>\n      <td>217.0</td>\n      <td>95.2</td>\n      <td>70.1</td>\n      <td>171.0</td>\n      <td>2.778</td>\n      <td>17.0</td>\n      <td>22.0</td>\n      <td>93.437331</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>Mitsubishi</td>\n      <td>Montero</td>\n      <td>11.337</td>\n      <td>19.125</td>\n      <td>31.807</td>\n      <td>3.5</td>\n      <td>200.0</td>\n      <td>107.3</td>\n      <td>69.9</td>\n      <td>186.6</td>\n      <td>4.520</td>\n      <td>24.3</td>\n      <td>18.0</td>\n      <td>83.920815</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>Nissan</td>\n      <td>Pathfinder</td>\n      <td>42.574</td>\n      <td>17.810</td>\n      <td>29.299</td>\n      <td>3.3</td>\n      <td>170.0</td>\n      <td>106.3</td>\n      <td>71.7</td>\n      <td>182.6</td>\n      <td>3.947</td>\n      <td>21.0</td>\n      <td>19.0</td>\n      <td>72.290355</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# let's create labels \n\ny_train = new_df[\"Price_in_thousands\"].copy()\ny_train","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:14.603412Z","iopub.execute_input":"2023-06-06T13:35:14.603680Z","iopub.status.idle":"2023-06-06T13:35:14.622087Z","shell.execute_reply.started":"2023-06-06T13:35:14.603659Z","shell.execute_reply":"2023-06-06T13:35:14.620795Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"136    13.108\n55     12.050\n124    41.430\n84     31.807\n105    29.299\n        ...  \n71     37.805\n106    22.799\n14     39.895\n92     31.750\n102    20.390\nName: Price_in_thousands, Length: 125, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Data Cleaning","metadata":{}},{"cell_type":"markdown","source":"#### Working With Missing Values","metadata":{}},{"cell_type":"code","source":"# let's check for the missing values \nX_train.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:14.623430Z","iopub.execute_input":"2023-06-06T13:35:14.623765Z","iopub.status.idle":"2023-06-06T13:35:14.639932Z","shell.execute_reply.started":"2023-06-06T13:35:14.623731Z","shell.execute_reply":"2023-06-06T13:35:14.638994Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"Manufacturer            0\nModel                   0\nSales_in_thousands      0\n__year_resale_value    28\nEngine_size             1\nHorsepower              1\nWheelbase               1\nWidth                   1\nLength                  1\nCurb_weight             1\nFuel_capacity           1\nFuel_efficiency         3\nPower_perf_factor       2\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"X_train.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:14.640986Z","iopub.execute_input":"2023-06-06T13:35:14.641288Z","iopub.status.idle":"2023-06-06T13:35:14.655898Z","shell.execute_reply.started":"2023-06-06T13:35:14.641267Z","shell.execute_reply":"2023-06-06T13:35:14.654491Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"Manufacturer            object\nModel                   object\nSales_in_thousands     float64\n__year_resale_value    float64\nEngine_size            float64\nHorsepower             float64\nWheelbase              float64\nWidth                  float64\nLength                 float64\nCurb_weight            float64\nFuel_capacity          float64\nFuel_efficiency        float64\nPower_perf_factor      float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"# let's remove them \n\nfrom sklearn.impute import SimpleImputer\n\nimputer = SimpleImputer(strategy=\"median\")\n\n# create a copy of the X_train because it can work on numerical values only \n\ndf_num = X_train.drop([\"Manufacturer\", \"Model\"], axis = 1)\n\n# fit the imputer \n\nimputer.fit(df_num)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:14.657404Z","iopub.execute_input":"2023-06-06T13:35:14.657739Z","iopub.status.idle":"2023-06-06T13:35:15.118777Z","shell.execute_reply.started":"2023-06-06T13:35:14.657712Z","shell.execute_reply":"2023-06-06T13:35:15.117713Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"SimpleImputer(strategy='median')","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#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>SimpleImputer(strategy=&#x27;median&#x27;)</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\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"imputer.statistics_","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.119995Z","iopub.execute_input":"2023-06-06T13:35:15.120321Z","iopub.status.idle":"2023-06-06T13:35:15.129054Z","shell.execute_reply.started":"2023-06-06T13:35:15.120298Z","shell.execute_reply":"2023-06-06T13:35:15.127960Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"array([ 30.696    ,  13.79     ,   3.       , 171.5      , 107.05     ,\n        70.45     , 189.       ,   3.368    ,  17.35     ,  24.       ,\n        71.1559776])"},"metadata":{}}]},{"cell_type":"code","source":"df_num.median()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.130242Z","iopub.execute_input":"2023-06-06T13:35:15.130525Z","iopub.status.idle":"2023-06-06T13:35:15.141595Z","shell.execute_reply.started":"2023-06-06T13:35:15.130504Z","shell.execute_reply":"2023-06-06T13:35:15.140619Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"Sales_in_thousands      30.696000\n__year_resale_value     13.790000\nEngine_size              3.000000\nHorsepower             171.500000\nWheelbase              107.050000\nWidth                   70.450000\nLength                 189.000000\nCurb_weight              3.368000\nFuel_capacity           17.350000\nFuel_efficiency         24.000000\nPower_perf_factor       71.155978\ndtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"# let's apply it then \nX = imputer.transform(df_num)\nX","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.142957Z","iopub.execute_input":"2023-06-06T13:35:15.143303Z","iopub.status.idle":"2023-06-06T13:35:15.153744Z","shell.execute_reply.started":"2023-06-06T13:35:15.143275Z","shell.execute_reply":"2023-06-06T13:35:15.152698Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"array([[142.535     ,  10.025     ,   1.8       , ...,  13.2       ,\n         33.        ,  47.96897242],\n       [220.65      ,   7.85      ,   2.5       , ...,  20.        ,\n         23.        ,  47.38953131],\n       [  8.982     ,  41.25      ,   2.7       , ...,  17.        ,\n         22.        ,  93.4373307 ],\n       ...,\n       [ 63.729     ,  22.525     ,   4.6       , ...,  18.5       ,\n         22.        , 113.8545976 ],\n       [ 18.392     ,  26.05      ,   2.3       , ...,  16.4       ,\n         26.        ,  78.28073088],\n       [ 88.094     ,  11.295     ,   2.4       , ...,  15.9       ,\n         25.        ,  63.31372783]])"},"metadata":{}}]},{"cell_type":"code","source":"# put the result in dataframe \n\ndf_tr = pd.DataFrame(X, columns = df_num.columns, index = df_num.index)\ndf_tr.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.156608Z","iopub.execute_input":"2023-06-06T13:35:15.156889Z","iopub.status.idle":"2023-06-06T13:35:15.177110Z","shell.execute_reply.started":"2023-06-06T13:35:15.156857Z","shell.execute_reply":"2023-06-06T13:35:15.176051Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"     Sales_in_thousands  __year_resale_value  Engine_size  Horsepower  \\\n136             142.535               10.025          1.8       120.0   \n55              220.650                7.850          2.5       119.0   \n124               8.982               41.250          2.7       217.0   \n84               11.337               19.125          3.5       200.0   \n105              42.574               17.810          3.3       170.0   \n\n     Wheelbase  Width  Length  Curb_weight  Fuel_capacity  Fuel_efficiency  \\\n136       97.0   66.7   174.0        2.420           13.2             33.0   \n55       117.5   69.4   200.7        3.086           20.0             23.0   \n124       95.2   70.1   171.0        2.778           17.0             22.0   \n84       107.3   69.9   186.6        4.520           24.3             18.0   \n105      106.3   71.7   182.6        3.947           21.0             19.0   \n\n     Power_perf_factor  \n136          47.968972  \n55           47.389531  \n124          93.437331  \n84           83.920815  \n105          72.290355  ","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>Sales_in_thousands</th>\n      <th>__year_resale_value</th>\n      <th>Engine_size</th>\n      <th>Horsepower</th>\n      <th>Wheelbase</th>\n      <th>Width</th>\n      <th>Length</th>\n      <th>Curb_weight</th>\n      <th>Fuel_capacity</th>\n      <th>Fuel_efficiency</th>\n      <th>Power_perf_factor</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>136</th>\n      <td>142.535</td>\n      <td>10.025</td>\n      <td>1.8</td>\n      <td>120.0</td>\n      <td>97.0</td>\n      <td>66.7</td>\n      <td>174.0</td>\n      <td>2.420</td>\n      <td>13.2</td>\n      <td>33.0</td>\n      <td>47.968972</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>220.650</td>\n      <td>7.850</td>\n      <td>2.5</td>\n      <td>119.0</td>\n      <td>117.5</td>\n      <td>69.4</td>\n      <td>200.7</td>\n      <td>3.086</td>\n      <td>20.0</td>\n      <td>23.0</td>\n      <td>47.389531</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>8.982</td>\n      <td>41.250</td>\n      <td>2.7</td>\n      <td>217.0</td>\n      <td>95.2</td>\n      <td>70.1</td>\n      <td>171.0</td>\n      <td>2.778</td>\n      <td>17.0</td>\n      <td>22.0</td>\n      <td>93.437331</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>11.337</td>\n      <td>19.125</td>\n      <td>3.5</td>\n      <td>200.0</td>\n      <td>107.3</td>\n      <td>69.9</td>\n      <td>186.6</td>\n      <td>4.520</td>\n      <td>24.3</td>\n      <td>18.0</td>\n      <td>83.920815</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>42.574</td>\n      <td>17.810</td>\n      <td>3.3</td>\n      <td>170.0</td>\n      <td>106.3</td>\n      <td>71.7</td>\n      <td>182.6</td>\n      <td>3.947</td>\n      <td>21.0</td>\n      <td>19.0</td>\n      <td>72.290355</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Working With Categorical Data","metadata":{}},{"cell_type":"code","source":"# we will use one-hot encoding to convert our categorical date to numbers\n\ndf_cat = X_train[[\"Manufacturer\", \"Model\"]]\ndf_cat","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.178202Z","iopub.execute_input":"2023-06-06T13:35:15.178531Z","iopub.status.idle":"2023-06-06T13:35:15.195411Z","shell.execute_reply.started":"2023-06-06T13:35:15.178504Z","shell.execute_reply":"2023-06-06T13:35:15.194599Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"    Manufacturer       Model\n136       Toyota     Corolla\n55          Ford      Ranger\n124      Porsche      Boxter\n84    Mitsubishi     Montero\n105       Nissan  Pathfinder\n..           ...         ...\n71         Lexus       GS300\n106       Nissan      Xterra\n14      Cadillac     DeVille\n92    Mercedes-B     C-Class\n102       Nissan      Altima\n\n[125 rows x 2 columns]","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>Manufacturer</th>\n      <th>Model</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>136</th>\n      <td>Toyota</td>\n      <td>Corolla</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Ford</td>\n      <td>Ranger</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>Porsche</td>\n      <td>Boxter</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>Mitsubishi</td>\n      <td>Montero</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>Nissan</td>\n      <td>Pathfinder</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>Lexus</td>\n      <td>GS300</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>Nissan</td>\n      <td>Xterra</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Cadillac</td>\n      <td>DeVille</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>Mercedes-B</td>\n      <td>C-Class</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>Nissan</td>\n      <td>Altima</td>\n    </tr>\n  </tbody>\n</table>\n<p>125 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import OneHotEncoder\n\ncat_encoder = OneHotEncoder()\ndf_cat_1hot = cat_encoder.fit_transform(df_cat)\ndf_cat_1hot","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.196248Z","iopub.execute_input":"2023-06-06T13:35:15.196516Z","iopub.status.idle":"2023-06-06T13:35:15.211012Z","shell.execute_reply.started":"2023-06-06T13:35:15.196494Z","shell.execute_reply":"2023-06-06T13:35:15.210314Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"<125x153 sparse matrix of type '<class 'numpy.float64'>'\n\twith 250 stored elements in Compressed Sparse Row format>"},"metadata":{}}]},{"cell_type":"code","source":"df_cat_1hot.toarray()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.212414Z","iopub.execute_input":"2023-06-06T13:35:15.212996Z","iopub.status.idle":"2023-06-06T13:35:15.229063Z","shell.execute_reply.started":"2023-06-06T13:35:15.212968Z","shell.execute_reply":"2023-06-06T13:35:15.227968Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"array([[0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       ...,\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.],\n       [0., 0., 0., ..., 0., 0., 0.]])"},"metadata":{}}]},{"cell_type":"markdown","source":"### Creating Transformation Pipelines","metadata":{}},{"cell_type":"code","source":"# for preprocessing the numerical attributes\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler   # feature scaling the data for same scales\n\nnum_pipeline = Pipeline([\n    ('imputer', SimpleImputer(strategy=\"median\")),\n    ('std_scaler', StandardScaler()),\n])\n\ndf_num_tr = num_pipeline.fit_transform(df_num)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.229981Z","iopub.execute_input":"2023-06-06T13:35:15.230981Z","iopub.status.idle":"2023-06-06T13:35:15.247325Z","shell.execute_reply.started":"2023-06-06T13:35:15.230946Z","shell.execute_reply":"2023-06-06T13:35:15.245556Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"df_num_tr","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.248980Z","iopub.execute_input":"2023-06-06T13:35:15.249799Z","iopub.status.idle":"2023-06-06T13:35:15.256944Z","shell.execute_reply.started":"2023-06-06T13:35:15.249753Z","shell.execute_reply":"2023-06-06T13:35:15.256099Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"array([[ 1.58045528, -0.75726741, -1.19420575, ..., -1.19835338,\n         2.04470207, -1.13177747],\n       [ 2.94486697, -1.03684466, -0.50720376, ...,  0.51286102,\n        -0.19926421, -1.15635988],\n       [-0.75227563,  3.25643344, -0.31091747, ..., -0.24208651,\n        -0.42366083,  0.79718828],\n       ...,\n       [ 0.20397409,  0.84949834,  1.55380222, ...,  0.13538726,\n        -0.42366083,  1.66337765],\n       [-0.58791393,  1.30260628, -0.70349004, ..., -0.39307601,\n         0.47392568,  0.15417932],\n       [ 0.62955037, -0.59402001, -0.6053469 , ..., -0.5189006 ,\n         0.24952905, -0.48078612]])"},"metadata":{}}]},{"cell_type":"code","source":"# dealing with numerical and categorical attributes at the same time\n\nfrom sklearn.compose import ColumnTransformer\n\nnum_attributes = list(df_num)\ncat_attributes = [\"Manufacturer\", \"Model\"]\n\nfull_pipeline = ColumnTransformer([\n    (\"num\", num_pipeline, num_attributes),\n    (\"cat\", OneHotEncoder(), cat_attributes),\n])\n\nX_train = full_pipeline.fit_transform(X_train)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.258502Z","iopub.execute_input":"2023-06-06T13:35:15.258780Z","iopub.status.idle":"2023-06-06T13:35:15.294508Z","shell.execute_reply.started":"2023-06-06T13:35:15.258756Z","shell.execute_reply":"2023-06-06T13:35:15.293730Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"markdown","source":"#### Fixing the missing values in our labels","metadata":{}},{"cell_type":"code","source":"y_train.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.295644Z","iopub.execute_input":"2023-06-06T13:35:15.296876Z","iopub.status.idle":"2023-06-06T13:35:15.304615Z","shell.execute_reply.started":"2023-06-06T13:35:15.296842Z","shell.execute_reply":"2023-06-06T13:35:15.303368Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}]},{"cell_type":"code","source":"# filling the missing values \ntrain_set.fillna(train_set[\"Price_in_thousands\"].median(), inplace=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.306470Z","iopub.execute_input":"2023-06-06T13:35:15.306888Z","iopub.status.idle":"2023-06-06T13:35:15.318611Z","shell.execute_reply.started":"2023-06-06T13:35:15.306850Z","shell.execute_reply":"2023-06-06T13:35:15.317470Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"y_train = train_set[\"Price_in_thousands\"].copy() # something I should have done earlier\ny_train","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.320142Z","iopub.execute_input":"2023-06-06T13:35:15.320522Z","iopub.status.idle":"2023-06-06T13:35:15.334091Z","shell.execute_reply.started":"2023-06-06T13:35:15.320496Z","shell.execute_reply":"2023-06-06T13:35:15.332944Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"136    13.108\n55     12.050\n124    41.430\n84     31.807\n105    29.299\n        ...  \n71     37.805\n106    22.799\n14     39.895\n92     31.750\n102    20.390\nName: Price_in_thousands, Length: 125, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"y_train.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.335861Z","iopub.execute_input":"2023-06-06T13:35:15.336620Z","iopub.status.idle":"2023-06-06T13:35:15.344001Z","shell.execute_reply.started":"2023-06-06T13:35:15.336584Z","shell.execute_reply":"2023-06-06T13:35:15.342634Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"Now we have X_train and y_train. Let's train the model. Let's first check their shapes.  ","metadata":{}},{"cell_type":"code","source":"X_train.shape, y_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.345589Z","iopub.execute_input":"2023-06-06T13:35:15.346319Z","iopub.status.idle":"2023-06-06T13:35:15.358115Z","shell.execute_reply.started":"2023-06-06T13:35:15.346278Z","shell.execute_reply":"2023-06-06T13:35:15.357079Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"((125, 164), (125,))"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Let's keep a small data size for validation purpose","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train_modified, X_val, y_train_modified, y_val = train_test_split(X_train, y_train, test_size=0.05, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.359393Z","iopub.execute_input":"2023-06-06T13:35:15.359742Z","iopub.status.idle":"2023-06-06T13:35:15.368727Z","shell.execute_reply.started":"2023-06-06T13:35:15.359711Z","shell.execute_reply":"2023-06-06T13:35:15.368094Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"X_train_modified.shape, y_train_modified.shape, X_val.shape, y_val.shape","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.369725Z","iopub.execute_input":"2023-06-06T13:35:15.370470Z","iopub.status.idle":"2023-06-06T13:35:15.386632Z","shell.execute_reply.started":"2023-06-06T13:35:15.370447Z","shell.execute_reply":"2023-06-06T13:35:15.385981Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"((118, 164), (118,), (7, 164), (7,))"},"metadata":{}}]},{"cell_type":"markdown","source":"## Training Our Models","metadata":{}},{"cell_type":"markdown","source":"### Linear Regression","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression \n\nlin_reg = LinearRegression()\nlin_reg.fit(X_train_modified, y_train_modified)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.388479Z","iopub.execute_input":"2023-06-06T13:35:15.389355Z","iopub.status.idle":"2023-06-06T13:35:15.414642Z","shell.execute_reply.started":"2023-06-06T13:35:15.389311Z","shell.execute_reply":"2023-06-06T13:35:15.413694Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"LinearRegression()","text/html":"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label 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center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"# let's check the prediction on val set\nlin_prediction = lin_reg.predict(X_val)\nprint(lin_prediction)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.415842Z","iopub.execute_input":"2023-06-06T13:35:15.416574Z","iopub.status.idle":"2023-06-06T13:35:15.422554Z","shell.execute_reply.started":"2023-06-06T13:35:15.416528Z","shell.execute_reply":"2023-06-06T13:35:15.421613Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stdout","text":"[10.5598097  26.55708552 30.79293386 20.77999153 32.22314216 45.86751559\n 25.77923997]\n","output_type":"stream"}]},{"cell_type":"code","source":"# let's compare the output with y labels in y_val\ncolumns = {\n    \"Predicted Values\" : lin_prediction,\n    \"Original Values\": y_val,\n    \"Difference\": y_val - lin_prediction,\n}\n\ndf_lin_compared = pd.DataFrame(columns)\n\ndf_lin_compared","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.424088Z","iopub.execute_input":"2023-06-06T13:35:15.425184Z","iopub.status.idle":"2023-06-06T13:35:15.444085Z","shell.execute_reply.started":"2023-06-06T13:35:15.425146Z","shell.execute_reply":"2023-06-06T13:35:15.442306Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"     Predicted Values  Original Values  Difference\n79          10.559810           13.987    3.427190\n153         26.557086           27.500    0.942914\n112         30.792934           31.598    0.805066\n83          20.779992           25.450    4.670008\n61          32.223142           26.000   -6.223142\n100         45.867516           35.300  -10.567516\n104         25.779240           26.399    0.619760","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>Predicted Values</th>\n      <th>Original Values</th>\n      <th>Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>79</th>\n      <td>10.559810</td>\n      <td>13.987</td>\n      <td>3.427190</td>\n    </tr>\n    <tr>\n      <th>153</th>\n      <td>26.557086</td>\n      <td>27.500</td>\n      <td>0.942914</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>30.792934</td>\n      <td>31.598</td>\n      <td>0.805066</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>20.779992</td>\n      <td>25.450</td>\n      <td>4.670008</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>32.223142</td>\n      <td>26.000</td>\n      <td>-6.223142</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>45.867516</td>\n      <td>35.300</td>\n      <td>-10.567516</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>25.779240</td>\n      <td>26.399</td>\n      <td>0.619760</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"Notice the above index are coming from our main datasets. You can check the difference in our price. ","metadata":{}},{"cell_type":"code","source":"# let's check for the evaluation metrics \n\nfrom sklearn.metrics import mean_squared_error\n\n# notice we are use root mean squared error so we need to take the square root of mse\nlin_rmse = np.sqrt(mean_squared_error(y_val, lin_prediction))\n\nprint(\"Error is:\", lin_rmse)\n","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.445487Z","iopub.execute_input":"2023-06-06T13:35:15.446666Z","iopub.status.idle":"2023-06-06T13:35:15.460383Z","shell.execute_reply.started":"2023-06-06T13:35:15.446622Z","shell.execute_reply":"2023-06-06T13:35:15.459412Z"},"trusted":true},"execution_count":52,"outputs":[{"name":"stdout","text":"Error is: 5.153027103656421\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Decision Trees","metadata":{}},{"cell_type":"code","source":"# let's try another model and see if we can improve our error \n\nfrom sklearn.tree import DecisionTreeRegressor\n\ntree_reg = DecisionTreeRegressor(random_state=42)\ntree_reg.fit(X_train_modified, y_train_modified)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.461641Z","iopub.execute_input":"2023-06-06T13:35:15.462888Z","iopub.status.idle":"2023-06-06T13:35:15.518697Z","shell.execute_reply.started":"2023-06-06T13:35:15.462842Z","shell.execute_reply":"2023-06-06T13:35:15.517693Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"DecisionTreeRegressor(random_state=42)","text/html":"<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(random_state=42)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"# let's check the model prediction \ntree_prediction = tree_reg.predict(X_val)\nprint(tree_prediction)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.519741Z","iopub.execute_input":"2023-06-06T13:35:15.520725Z","iopub.status.idle":"2023-06-06T13:35:15.527502Z","shell.execute_reply.started":"2023-06-06T13:35:15.520670Z","shell.execute_reply":"2023-06-06T13:35:15.525963Z"},"trusted":true},"execution_count":54,"outputs":[{"name":"stdout","text":"[13.108 28.8   31.93  22.799 31.01  22.605 22.527]\n","output_type":"stream"}]},{"cell_type":"code","source":"# let's compared with actual one\ncolumns = {\n    \"Predicted Values\" : tree_prediction,\n    \"Original Values\" : y_val,\n    \"Difference\" : y_val - tree_prediction\n}\n\ndf_tree_compared = pd.DataFrame(columns)\ndf_tree_compared","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.529192Z","iopub.execute_input":"2023-06-06T13:35:15.529761Z","iopub.status.idle":"2023-06-06T13:35:15.547734Z","shell.execute_reply.started":"2023-06-06T13:35:15.529730Z","shell.execute_reply":"2023-06-06T13:35:15.546380Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"     Predicted Values  Original Values  Difference\n79             13.108           13.987       0.879\n153            28.800           27.500      -1.300\n112            31.930           31.598      -0.332\n83             22.799           25.450       2.651\n61             31.010           26.000      -5.010\n100            22.605           35.300      12.695\n104            22.527           26.399       3.872","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>Predicted Values</th>\n      <th>Original Values</th>\n      <th>Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>79</th>\n      <td>13.108</td>\n      <td>13.987</td>\n      <td>0.879</td>\n    </tr>\n    <tr>\n      <th>153</th>\n      <td>28.800</td>\n      <td>27.500</td>\n      <td>-1.300</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>31.930</td>\n      <td>31.598</td>\n      <td>-0.332</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>22.799</td>\n      <td>25.450</td>\n      <td>2.651</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>31.010</td>\n      <td>26.000</td>\n      <td>-5.010</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>22.605</td>\n      <td>35.300</td>\n      <td>12.695</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>22.527</td>\n      <td>26.399</td>\n      <td>3.872</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# let's check for error \n\ntree_rmse = np.sqrt(mean_squared_error(y_val, tree_prediction))\nprint(tree_rmse)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.549783Z","iopub.execute_input":"2023-06-06T13:35:15.550156Z","iopub.status.idle":"2023-06-06T13:35:15.561894Z","shell.execute_reply.started":"2023-06-06T13:35:15.550128Z","shell.execute_reply":"2023-06-06T13:35:15.560762Z"},"trusted":true},"execution_count":56,"outputs":[{"name":"stdout","text":"5.488379607341834\n","output_type":"stream"}]},{"cell_type":"markdown","source":"As we can see the error seems to have increased","metadata":{}},{"cell_type":"markdown","source":"Let's try another model ","metadata":{}},{"cell_type":"markdown","source":"### RandomForestRegressor","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestRegressor\n\nforest_reg = RandomForestRegressor(n_estimators=100, random_state=42)\nforest_reg.fit(X_train_modified, y_train_modified)\n","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.563302Z","iopub.execute_input":"2023-06-06T13:35:15.563635Z","iopub.status.idle":"2023-06-06T13:35:15.891912Z","shell.execute_reply.started":"2023-06-06T13:35:15.563609Z","shell.execute_reply":"2023-06-06T13:35:15.890892Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"RandomForestRegressor(random_state=42)","text/html":"<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"forest_prediction = forest_reg.predict(X_val)\nprint(forest_prediction)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.895392Z","iopub.execute_input":"2023-06-06T13:35:15.895736Z","iopub.status.idle":"2023-06-06T13:35:15.916228Z","shell.execute_reply.started":"2023-06-06T13:35:15.895708Z","shell.execute_reply":"2023-06-06T13:35:15.914866Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"[12.37466 23.99223 27.80518 22.14663 26.52174 28.13135 23.16176]\n","output_type":"stream"}]},{"cell_type":"code","source":"# let's compare\ncolumns = {\n    \"Predicted Values\" : forest_prediction,\n    \"Original Values\" : y_val,\n    \"Difference\" : y_val - forest_prediction\n}\n\ndf_forest_compared = pd.DataFrame(columns)\ndf_forest_compared","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.917665Z","iopub.execute_input":"2023-06-06T13:35:15.917983Z","iopub.status.idle":"2023-06-06T13:35:15.935499Z","shell.execute_reply.started":"2023-06-06T13:35:15.917957Z","shell.execute_reply":"2023-06-06T13:35:15.934338Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"     Predicted Values  Original Values  Difference\n79           12.37466           13.987     1.61234\n153          23.99223           27.500     3.50777\n112          27.80518           31.598     3.79282\n83           22.14663           25.450     3.30337\n61           26.52174           26.000    -0.52174\n100          28.13135           35.300     7.16865\n104          23.16176           26.399     3.23724","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>Predicted Values</th>\n      <th>Original Values</th>\n      <th>Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>79</th>\n      <td>12.37466</td>\n      <td>13.987</td>\n      <td>1.61234</td>\n    </tr>\n    <tr>\n      <th>153</th>\n      <td>23.99223</td>\n      <td>27.500</td>\n      <td>3.50777</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>27.80518</td>\n      <td>31.598</td>\n      <td>3.79282</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>22.14663</td>\n      <td>25.450</td>\n      <td>3.30337</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>26.52174</td>\n      <td>26.000</td>\n      <td>-0.52174</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>28.13135</td>\n      <td>35.300</td>\n      <td>7.16865</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>23.16176</td>\n      <td>26.399</td>\n      <td>3.23724</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# let's check for error \nforest_rmse = np.sqrt(mean_squared_error(y_val, forest_prediction))\nprint(forest_rmse)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:35:15.936558Z","iopub.execute_input":"2023-06-06T13:35:15.936828Z","iopub.status.idle":"2023-06-06T13:35:15.955386Z","shell.execute_reply.started":"2023-06-06T13:35:15.936803Z","shell.execute_reply":"2023-06-06T13:35:15.953796Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"3.823672614226504\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Support Vector Regressor\n","metadata":{}},{"cell_type":"code","source":"from sklearn.svm import SVR\n\nsvm_reg = SVR(kernel=\"linear\")\nsvm_reg.fit(X_train_modified, y_train_modified)\n","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:38:01.756540Z","iopub.execute_input":"2023-06-06T13:38:01.756862Z","iopub.status.idle":"2023-06-06T13:38:01.768574Z","shell.execute_reply.started":"2023-06-06T13:38:01.756833Z","shell.execute_reply":"2023-06-06T13:38:01.767140Z"},"trusted":true},"execution_count":62,"outputs":[{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"SVR(kernel='linear')","text/html":"<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(kernel=&#x27;linear&#x27;)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"svm_prediction = svm_reg.predict(X_val)\nsvm_prediction","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:39:09.305088Z","iopub.execute_input":"2023-06-06T13:39:09.306246Z","iopub.status.idle":"2023-06-06T13:39:09.315748Z","shell.execute_reply.started":"2023-06-06T13:39:09.306210Z","shell.execute_reply":"2023-06-06T13:39:09.314478Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"array([11.65491272, 24.85486031, 28.01501547, 22.96432271, 30.66947891,\n       34.76897898, 24.03678817])"},"metadata":{}}]},{"cell_type":"code","source":"svm_rmse = np.sqrt(mean_squared_error(y_val, svm_prediction))\nsvm_rmse","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:40:07.770995Z","iopub.execute_input":"2023-06-06T13:40:07.771380Z","iopub.status.idle":"2023-06-06T13:40:07.779403Z","shell.execute_reply.started":"2023-06-06T13:40:07.771355Z","shell.execute_reply":"2023-06-06T13:40:07.777863Z"},"trusted":true},"execution_count":64,"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"2.9060976813184483"},"metadata":{}}]},{"cell_type":"markdown","source":"This seem to be perfoming better","metadata":{}},{"cell_type":"code","source":"# let's compare\ncolumns = {\n    \"Predicted Values\" : svm_prediction,\n    \"Original Values\" : y_val,\n    \"Difference\" : y_val - svm_prediction\n}\n\ndf_svm_compared = pd.DataFrame(columns)\ndf_svm_compared","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:41:03.130745Z","iopub.execute_input":"2023-06-06T13:41:03.131132Z","iopub.status.idle":"2023-06-06T13:41:03.143580Z","shell.execute_reply.started":"2023-06-06T13:41:03.131105Z","shell.execute_reply":"2023-06-06T13:41:03.142337Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"     Predicted Values  Original Values  Difference\n79          11.654913           13.987    2.332087\n153         24.854860           27.500    2.645140\n112         28.015015           31.598    3.582985\n83          22.964323           25.450    2.485677\n61          30.669479           26.000   -4.669479\n100         34.768979           35.300    0.531021\n104         24.036788           26.399    2.362212","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>Predicted Values</th>\n      <th>Original Values</th>\n      <th>Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>79</th>\n      <td>11.654913</td>\n      <td>13.987</td>\n      <td>2.332087</td>\n    </tr>\n    <tr>\n      <th>153</th>\n      <td>24.854860</td>\n      <td>27.500</td>\n      <td>2.645140</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>28.015015</td>\n      <td>31.598</td>\n      <td>3.582985</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>22.964323</td>\n      <td>25.450</td>\n      <td>2.485677</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>30.669479</td>\n      <td>26.000</td>\n      <td>-4.669479</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>34.768979</td>\n      <td>35.300</td>\n      <td>0.531021</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>24.036788</td>\n      <td>26.399</td>\n      <td>2.362212</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"Well, the error has gone down. Let's see what we can do with cross validation techniques. ","metadata":{}},{"cell_type":"code","source":"# A fucntion to show scores\n\ndef display_scores(scores):\n    print(\"Scores:\", scores)\n    print(\"Mean:\", scores.mean())\n    print(\"Standard Devidation:\", scores.std())","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:44:23.430695Z","iopub.execute_input":"2023-06-06T13:44:23.431097Z","iopub.status.idle":"2023-06-06T13:44:23.436096Z","shell.execute_reply.started":"2023-06-06T13:44:23.431070Z","shell.execute_reply":"2023-06-06T13:44:23.435178Z"},"trusted":true},"execution_count":67,"outputs":[]},{"cell_type":"markdown","source":"#### Cross Validation: Linear Regression","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_score\n\nscores = cross_val_score(lin_reg, X_train, y_train,\n                         scoring=\"neg_mean_squared_error\", cv=5)\nlin_rmse_scores = np.sqrt(-scores)\n\ndisplay_scores(lin_rmse_scores)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:48:49.396721Z","iopub.execute_input":"2023-06-06T13:48:49.397070Z","iopub.status.idle":"2023-06-06T13:48:49.444335Z","shell.execute_reply.started":"2023-06-06T13:48:49.397042Z","shell.execute_reply":"2023-06-06T13:48:49.443445Z"},"trusted":true},"execution_count":71,"outputs":[{"name":"stdout","text":"Scores: [4.47868724 4.50112258 4.60031063 3.81874233 8.86161182]\nMean: 5.252094919859788\nStandard Devidation: 1.8259273554602318\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Cross Validation: Decision Trees","metadata":{}},{"cell_type":"code","source":"scores = cross_val_score(tree_reg, X_train, y_train,\n                         scoring=\"neg_mean_squared_error\", cv=5)\ntree_rmse_scores = np.sqrt(-scores)\n\ndisplay_scores(tree_rmse_scores)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:48:43.961287Z","iopub.execute_input":"2023-06-06T13:48:43.961608Z","iopub.status.idle":"2023-06-06T13:48:43.988966Z","shell.execute_reply.started":"2023-06-06T13:48:43.961585Z","shell.execute_reply":"2023-06-06T13:48:43.987942Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"Scores: [ 4.85070211  7.0511423   7.07547966  7.30532732 10.12812738]\nMean: 7.282155753872056\nStandard Devidation: 1.6797702510605832\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Cross Validation: Random Forest Regressor","metadata":{}},{"cell_type":"code","source":"scores = cross_val_score(forest_reg, X_train, y_train,\n                         scoring=\"neg_mean_squared_error\", cv=5)\nforest_rmse_scores = np.sqrt(-scores)\n\ndisplay_scores(forest_rmse_scores)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:50:32.550331Z","iopub.execute_input":"2023-06-06T13:50:32.550676Z","iopub.status.idle":"2023-06-06T13:50:33.593992Z","shell.execute_reply.started":"2023-06-06T13:50:32.550650Z","shell.execute_reply":"2023-06-06T13:50:33.593100Z"},"trusted":true},"execution_count":72,"outputs":[{"name":"stdout","text":"Scores: [3.97737775 5.29996465 4.61493472 3.85036648 8.5622612 ]\nMean: 5.260980960349737\nStandard Devidation: 1.7294402027240972\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Cross Validation: SVR","metadata":{}},{"cell_type":"code","source":"scores = cross_val_score(svm_reg, X_train, y_train,\n                         scoring=\"neg_mean_squared_error\", cv=5)\nsvm_rmse_scores = np.sqrt(-scores)\n\ndisplay_scores(svm_rmse_scores)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T13:51:01.395679Z","iopub.execute_input":"2023-06-06T13:51:01.395981Z","iopub.status.idle":"2023-06-06T13:51:01.423356Z","shell.execute_reply.started":"2023-06-06T13:51:01.395959Z","shell.execute_reply":"2023-06-06T13:51:01.421922Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"Scores: [3.15610534 3.62537883 4.63262131 3.41457572 9.80260393]\nMean: 4.9262570281608395\nStandard Devidation: 2.489011149882269\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Well, it seems like SVR is still perfoming better than others. We can try more models here. Maybe we can add more data and check for improvement. Let's hypertune the parameters","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import GridSearchCV\n\nparam_grid = [\n        {'kernel': ['linear'], 'C': [10., 30., 100., 300., 1000., 3000., 10000., 30000.0]},\n        {'kernel': ['rbf'], 'C': [1.0, 3.0, 10., 30., 100., 300., 1000.0],\n         'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]},\n    ]\n\nsvm_reg = SVR()\ngrid_search = GridSearchCV(svm_reg, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2)\ngrid_search.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:01:33.376132Z","iopub.execute_input":"2023-06-06T14:01:33.376483Z","iopub.status.idle":"2023-06-06T14:01:34.174053Z","shell.execute_reply.started":"2023-06-06T14:01:33.376457Z","shell.execute_reply":"2023-06-06T14:01:34.172997Z"},"trusted":true},"execution_count":74,"outputs":[{"name":"stdout","text":"Fitting 5 folds for each of 50 candidates, totalling 250 fits\n[CV] END ..............................C=10.0, kernel=linear; total time=   0.0s\n[CV] END ..............................C=10.0, kernel=linear; total time=   0.0s\n[CV] END ..............................C=10.0, kernel=linear; total time=   0.0s\n[CV] END ..............................C=10.0, kernel=linear; total time=   0.0s\n[CV] END ..............................C=10.0, kernel=linear; total time=   0.0s\n[CV] END ..............................C=30.0, kernel=linear; total time=   0.0s\n[CV] END ..............................C=30.0, kernel=linear; total time=   0.0s\n[CV] END ..............................C=30.0, kernel=linear; total time=   0.0s\n[CV] END ..............................C=30.0, kernel=linear; total time=   0.0s\n[CV] END ..............................C=30.0, kernel=linear; total time=   0.0s\n[CV] END .............................C=100.0, kernel=linear; total time=   0.0s\n[CV] END .............................C=100.0, kernel=linear; total time=   0.0s\n[CV] END .............................C=100.0, kernel=linear; total time=   0.0s\n[CV] END .............................C=100.0, kernel=linear; total time=   0.0s\n[CV] END .............................C=100.0, kernel=linear; total time=   0.0s\n[CV] END .............................C=300.0, kernel=linear; total time=   0.0s\n[CV] END .............................C=300.0, kernel=linear; total time=   0.0s\n[CV] END .............................C=300.0, kernel=linear; total time=   0.0s\n[CV] END .............................C=300.0, kernel=linear; total time=   0.0s\n[CV] END .............................C=300.0, kernel=linear; total time=   0.0s\n[CV] END ............................C=1000.0, kernel=linear; total time=   0.0s\n[CV] END ............................C=1000.0, kernel=linear; total time=   0.0s\n[CV] END ............................C=1000.0, kernel=linear; total time=   0.0s\n[CV] END ............................C=1000.0, kernel=linear; total time=   0.0s\n[CV] END ............................C=1000.0, kernel=linear; total time=   0.0s\n[CV] END ............................C=3000.0, kernel=linear; total time=   0.0s\n[CV] END ............................C=3000.0, kernel=linear; total time=   0.0s\n[CV] END ............................C=3000.0, kernel=linear; total time=   0.0s\n[CV] END ............................C=3000.0, kernel=linear; total time=   0.0s\n[CV] END ............................C=3000.0, kernel=linear; total time=   0.0s\n[CV] END ...........................C=10000.0, kernel=linear; total time=   0.0s\n[CV] END ...........................C=10000.0, kernel=linear; total time=   0.0s\n[CV] END ...........................C=10000.0, kernel=linear; total time=   0.0s\n[CV] END ...........................C=10000.0, kernel=linear; total time=   0.0s\n[CV] END ...........................C=10000.0, kernel=linear; total time=   0.0s\n[CV] END ...........................C=30000.0, kernel=linear; total time=   0.0s\n[CV] END ...........................C=30000.0, kernel=linear; total time=   0.0s\n[CV] END ...........................C=30000.0, kernel=linear; total time=   0.0s\n[CV] END ...........................C=30000.0, kernel=linear; total time=   0.0s\n[CV] END ...........................C=30000.0, kernel=linear; total time=   0.0s\n[CV] END ......................C=1.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ......................C=1.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ......................C=1.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ......................C=1.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ......................C=1.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ......................C=1.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ......................C=1.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ......................C=1.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ......................C=1.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ......................C=1.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=1.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=3.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ......................C=3.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ......................C=3.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ......................C=3.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ......................C=3.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ......................C=3.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ......................C=3.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ......................C=3.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ......................C=3.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ......................C=3.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .......................C=3.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=10.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END .....................C=10.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END .....................C=10.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END .....................C=10.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END .....................C=10.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END .....................C=10.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .....................C=10.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .....................C=10.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .....................C=10.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .....................C=10.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=10.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=30.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END .....................C=30.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END .....................C=30.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END .....................C=30.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END .....................C=30.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END .....................C=30.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .....................C=30.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .....................C=30.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .....................C=30.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .....................C=30.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ......................C=30.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=100.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ....................C=100.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ....................C=100.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ....................C=100.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ....................C=100.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ....................C=100.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ....................C=100.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ....................C=100.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ....................C=100.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ....................C=100.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=100.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=300.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ....................C=300.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ....................C=300.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ....................C=300.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ....................C=300.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ....................C=300.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ....................C=300.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ....................C=300.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ....................C=300.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ....................C=300.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END .....................C=300.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ...................C=1000.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ...................C=1000.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ...................C=1000.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ...................C=1000.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ...................C=1000.0, gamma=0.01, kernel=rbf; total time=   0.0s\n[CV] END ...................C=1000.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ...................C=1000.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ...................C=1000.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ...................C=1000.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ...................C=1000.0, gamma=0.03, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=0.1, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=0.3, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=1.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=3.0, kernel=rbf; total time=   0.0s\n[CV] END ....................C=1000.0, gamma=3.0, kernel=rbf; total time=   0.0s\n","output_type":"stream"},{"execution_count":74,"output_type":"execute_result","data":{"text/plain":"GridSearchCV(cv=5, estimator=SVR(),\n             param_grid=[{'C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n                                10000.0, 30000.0],\n                          'kernel': ['linear']},\n                         {'C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0],\n                          'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n                          'kernel': ['rbf']}],\n             scoring='neg_mean_squared_error', verbose=2)","text/html":"<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVR(),\n             param_grid=[{&#x27;C&#x27;: [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n                                10000.0, 30000.0],\n                          &#x27;kernel&#x27;: [&#x27;linear&#x27;]},\n                         {&#x27;C&#x27;: [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0],\n                          &#x27;gamma&#x27;: [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}],\n             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=2)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=SVR(),\n             param_grid=[{&#x27;C&#x27;: [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n                                10000.0, 30000.0],\n                          &#x27;kernel&#x27;: [&#x27;linear&#x27;]},\n                         {&#x27;C&#x27;: [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0],\n                          &#x27;gamma&#x27;: [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}],\n             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"negative_mse = grid_search.best_score_\nrmse = np.sqrt(-negative_mse)\nrmse","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:02:01.755696Z","iopub.execute_input":"2023-06-06T14:02:01.756079Z","iopub.status.idle":"2023-06-06T14:02:01.763852Z","shell.execute_reply.started":"2023-06-06T14:02:01.756045Z","shell.execute_reply":"2023-06-06T14:02:01.762294Z"},"trusted":true},"execution_count":75,"outputs":[{"execution_count":75,"output_type":"execute_result","data":{"text/plain":"5.335441140664488"},"metadata":{}}]},{"cell_type":"code","source":"grid_search.best_params_","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:02:28.081329Z","iopub.execute_input":"2023-06-06T14:02:28.081664Z","iopub.status.idle":"2023-06-06T14:02:28.088612Z","shell.execute_reply.started":"2023-06-06T14:02:28.081639Z","shell.execute_reply":"2023-06-06T14:02:28.087390Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"{'C': 10.0, 'kernel': 'linear'}"},"metadata":{}}]},{"cell_type":"markdown","source":"Well let's try Randomized Search CV","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import RandomizedSearchCV\nfrom scipy.stats import expon, reciprocal\n \n\nparam_distribs = {\n        'kernel': ['linear', 'rbf'],\n        'C': reciprocal(20, 200000),\n        'gamma': expon(scale=1.0),\n    }\n\nsvm_reg = SVR()\nrnd_search = RandomizedSearchCV(svm_reg, param_distributions=param_distribs,\n                                n_iter=20, cv=5, scoring='neg_mean_squared_error', random_state=42)\n\nrnd_search.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:11:53.366020Z","iopub.execute_input":"2023-06-06T14:11:53.366426Z","iopub.status.idle":"2023-06-06T14:11:53.762748Z","shell.execute_reply.started":"2023-06-06T14:11:53.366397Z","shell.execute_reply":"2023-06-06T14:11:53.761875Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"RandomizedSearchCV(cv=5, estimator=SVR(), n_iter=20,\n                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7a43bd1604c0>,\n                                        'gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7a43bcde0df0>,\n                                        'kernel': ['linear', 'rbf']},\n                   random_state=42, scoring='neg_mean_squared_error')","text/html":"<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=SVR(), n_iter=20,\n                   param_distributions={&#x27;C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7a43bd1604c0&gt;,\n                                        &#x27;gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7a43bcde0df0&gt;,\n                                        &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]},\n                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=SVR(), n_iter=20,\n                   param_distributions={&#x27;C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7a43bd1604c0&gt;,\n                                        &#x27;gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7a43bcde0df0&gt;,\n                                        &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]},\n                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"negative_mse = rnd_search.best_score_\nrmse = np.sqrt(-negative_mse)\nrmse","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:12:07.286356Z","iopub.execute_input":"2023-06-06T14:12:07.286684Z","iopub.status.idle":"2023-06-06T14:12:07.292840Z","shell.execute_reply.started":"2023-06-06T14:12:07.286659Z","shell.execute_reply":"2023-06-06T14:12:07.292002Z"},"trusted":true},"execution_count":78,"outputs":[{"execution_count":78,"output_type":"execute_result","data":{"text/plain":"5.5783093645202975"},"metadata":{}}]},{"cell_type":"code","source":"rnd_search.best_params_","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:12:18.606415Z","iopub.execute_input":"2023-06-06T14:12:18.606754Z","iopub.status.idle":"2023-06-06T14:12:18.613901Z","shell.execute_reply.started":"2023-06-06T14:12:18.606728Z","shell.execute_reply":"2023-06-06T14:12:18.612292Z"},"trusted":true},"execution_count":79,"outputs":[{"execution_count":79,"output_type":"execute_result","data":{"text/plain":"{'C': 629.782329591372, 'gamma': 3.010121430917521, 'kernel': 'linear'}"},"metadata":{}}]},{"cell_type":"markdown","source":"Well, it seems like Grid Search CV is slightly better so let's go with that now. Additionally, our model's perfomance has reduced as compared what we were getting normally that means we can further improve our model by adding more data or try other models and see what we can get. Also, for this project we will go with the grid search cv value but do note that error in range of 5.57 which can be a significant difference from actual value. ","metadata":{}},{"cell_type":"markdown","source":"### Evaluating Our System On Test Set","metadata":{}},{"cell_type":"code","source":"test_set.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:20:51.025430Z","iopub.execute_input":"2023-06-06T14:20:51.025734Z","iopub.status.idle":"2023-06-06T14:20:51.045374Z","shell.execute_reply.started":"2023-06-06T14:20:51.025710Z","shell.execute_reply":"2023-06-06T14:20:51.044584Z"},"trusted":true},"execution_count":82,"outputs":[{"execution_count":82,"output_type":"execute_result","data":{"text/plain":"    Manufacturer           Model  Sales_in_thousands  __year_resale_value  \\\n126      Porsche  Carrera Cabrio               1.866               67.550   \n45         Dodge         Caravan             181.749               12.025   \n133       Saturn              LS              49.989                  NaN   \n138       Toyota          Avalon              63.849               18.140   \n111   Oldsmobile          Aurora              14.690               19.890   \n\n     Price_in_thousands  Engine_size  Horsepower  Wheelbase  Width  Length  \\\n126              74.970          3.4       300.0       92.6   69.5   174.5   \n45               19.565          2.4       150.0      113.3   76.8   186.3   \n133              15.010          2.2       137.0      106.5   69.0   190.4   \n138              25.545          3.0       210.0      107.1   71.7   191.9   \n111              36.229          4.0       250.0      113.8   74.4   205.4   \n\n     Curb_weight  Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n126        3.075           17.0             23.0         135.914710  \n45         3.533           20.0             24.0          61.227000  \n133        2.910           13.1             28.0          54.819728  \n138        3.417           18.5             26.0          84.911898  \n111        3.967           18.5             22.0         103.441693  ","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>Manufacturer</th>\n      <th>Model</th>\n      <th>Sales_in_thousands</th>\n      <th>__year_resale_value</th>\n      <th>Price_in_thousands</th>\n      <th>Engine_size</th>\n      <th>Horsepower</th>\n      <th>Wheelbase</th>\n      <th>Width</th>\n      <th>Length</th>\n      <th>Curb_weight</th>\n      <th>Fuel_capacity</th>\n      <th>Fuel_efficiency</th>\n      <th>Power_perf_factor</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>126</th>\n      <td>Porsche</td>\n      <td>Carrera Cabrio</td>\n      <td>1.866</td>\n      <td>67.550</td>\n      <td>74.970</td>\n      <td>3.4</td>\n      <td>300.0</td>\n      <td>92.6</td>\n      <td>69.5</td>\n      <td>174.5</td>\n      <td>3.075</td>\n      <td>17.0</td>\n      <td>23.0</td>\n      <td>135.914710</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>Dodge</td>\n      <td>Caravan</td>\n      <td>181.749</td>\n      <td>12.025</td>\n      <td>19.565</td>\n      <td>2.4</td>\n      <td>150.0</td>\n      <td>113.3</td>\n      <td>76.8</td>\n      <td>186.3</td>\n      <td>3.533</td>\n      <td>20.0</td>\n      <td>24.0</td>\n      <td>61.227000</td>\n    </tr>\n    <tr>\n      <th>133</th>\n      <td>Saturn</td>\n      <td>LS</td>\n      <td>49.989</td>\n      <td>NaN</td>\n      <td>15.010</td>\n      <td>2.2</td>\n      <td>137.0</td>\n      <td>106.5</td>\n      <td>69.0</td>\n      <td>190.4</td>\n      <td>2.910</td>\n      <td>13.1</td>\n      <td>28.0</td>\n      <td>54.819728</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>Toyota</td>\n      <td>Avalon</td>\n      <td>63.849</td>\n      <td>18.140</td>\n      <td>25.545</td>\n      <td>3.0</td>\n      <td>210.0</td>\n      <td>107.1</td>\n      <td>71.7</td>\n      <td>191.9</td>\n      <td>3.417</td>\n      <td>18.5</td>\n      <td>26.0</td>\n      <td>84.911898</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>Oldsmobile</td>\n      <td>Aurora</td>\n      <td>14.690</td>\n      <td>19.890</td>\n      <td>36.229</td>\n      <td>4.0</td>\n      <td>250.0</td>\n      <td>113.8</td>\n      <td>74.4</td>\n      <td>205.4</td>\n      <td>3.967</td>\n      <td>18.5</td>\n      <td>22.0</td>\n      <td>103.441693</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"final_model = grid_search.best_estimator_\n\nX_test = test_set.drop(\"Price_in_thousands\", axis=1)\ny_test = test_set[\"Price_in_thousands\"].copy()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:25:41.340095Z","iopub.execute_input":"2023-06-06T14:25:41.340447Z","iopub.status.idle":"2023-06-06T14:25:41.347285Z","shell.execute_reply.started":"2023-06-06T14:25:41.340421Z","shell.execute_reply":"2023-06-06T14:25:41.345863Z"},"trusted":true},"execution_count":89,"outputs":[]},{"cell_type":"code","source":"X_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:25:51.335655Z","iopub.execute_input":"2023-06-06T14:25:51.335944Z","iopub.status.idle":"2023-06-06T14:25:51.353993Z","shell.execute_reply.started":"2023-06-06T14:25:51.335921Z","shell.execute_reply":"2023-06-06T14:25:51.352960Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"    Manufacturer           Model  Sales_in_thousands  __year_resale_value  \\\n126      Porsche  Carrera Cabrio               1.866               67.550   \n45         Dodge         Caravan             181.749               12.025   \n133       Saturn              LS              49.989                  NaN   \n138       Toyota          Avalon              63.849               18.140   \n111   Oldsmobile          Aurora              14.690               19.890   \n\n     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n126          3.4       300.0       92.6   69.5   174.5        3.075   \n45           2.4       150.0      113.3   76.8   186.3        3.533   \n133          2.2       137.0      106.5   69.0   190.4        2.910   \n138          3.0       210.0      107.1   71.7   191.9        3.417   \n111          4.0       250.0      113.8   74.4   205.4        3.967   \n\n     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n126           17.0             23.0         135.914710  \n45            20.0             24.0          61.227000  \n133           13.1             28.0          54.819728  \n138           18.5             26.0          84.911898  \n111           18.5             22.0         103.441693  ","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>Manufacturer</th>\n      <th>Model</th>\n      <th>Sales_in_thousands</th>\n      <th>__year_resale_value</th>\n      <th>Engine_size</th>\n      <th>Horsepower</th>\n      <th>Wheelbase</th>\n      <th>Width</th>\n      <th>Length</th>\n      <th>Curb_weight</th>\n      <th>Fuel_capacity</th>\n      <th>Fuel_efficiency</th>\n      <th>Power_perf_factor</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>126</th>\n      <td>Porsche</td>\n      <td>Carrera Cabrio</td>\n      <td>1.866</td>\n      <td>67.550</td>\n      <td>3.4</td>\n      <td>300.0</td>\n      <td>92.6</td>\n      <td>69.5</td>\n      <td>174.5</td>\n      <td>3.075</td>\n      <td>17.0</td>\n      <td>23.0</td>\n      <td>135.914710</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>Dodge</td>\n      <td>Caravan</td>\n      <td>181.749</td>\n      <td>12.025</td>\n      <td>2.4</td>\n      <td>150.0</td>\n      <td>113.3</td>\n      <td>76.8</td>\n      <td>186.3</td>\n      <td>3.533</td>\n      <td>20.0</td>\n      <td>24.0</td>\n      <td>61.227000</td>\n    </tr>\n    <tr>\n      <th>133</th>\n      <td>Saturn</td>\n      <td>LS</td>\n      <td>49.989</td>\n      <td>NaN</td>\n      <td>2.2</td>\n      <td>137.0</td>\n      <td>106.5</td>\n      <td>69.0</td>\n      <td>190.4</td>\n      <td>2.910</td>\n      <td>13.1</td>\n      <td>28.0</td>\n      <td>54.819728</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>Toyota</td>\n      <td>Avalon</td>\n      <td>63.849</td>\n      <td>18.140</td>\n      <td>3.0</td>\n      <td>210.0</td>\n      <td>107.1</td>\n      <td>71.7</td>\n      <td>191.9</td>\n      <td>3.417</td>\n      <td>18.5</td>\n      <td>26.0</td>\n      <td>84.911898</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>Oldsmobile</td>\n      <td>Aurora</td>\n      <td>14.690</td>\n      <td>19.890</td>\n      <td>4.0</td>\n      <td>250.0</td>\n      <td>113.8</td>\n      <td>74.4</td>\n      <td>205.4</td>\n      <td>3.967</td>\n      <td>18.5</td>\n      <td>22.0</td>\n      <td>103.441693</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"X_test.shape, y_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:26:20.374736Z","iopub.execute_input":"2023-06-06T14:26:20.375167Z","iopub.status.idle":"2023-06-06T14:26:20.381844Z","shell.execute_reply.started":"2023-06-06T14:26:20.375139Z","shell.execute_reply":"2023-06-06T14:26:20.381041Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":"((32, 13), (32,))"},"metadata":{}}]},{"cell_type":"code","source":"X_test.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:28:53.726554Z","iopub.execute_input":"2023-06-06T14:28:53.726945Z","iopub.status.idle":"2023-06-06T14:28:53.735726Z","shell.execute_reply.started":"2023-06-06T14:28:53.726917Z","shell.execute_reply":"2023-06-06T14:28:53.734407Z"},"trusted":true},"execution_count":99,"outputs":[{"execution_count":99,"output_type":"execute_result","data":{"text/plain":"Manufacturer           0\nModel                  0\nSales_in_thousands     0\n__year_resale_value    8\nEngine_size            0\nHorsepower             0\nWheelbase              0\nWidth                  0\nLength                 0\nCurb_weight            1\nFuel_capacity          0\nFuel_efficiency        0\nPower_perf_factor      0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_test.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:28:40.384965Z","iopub.execute_input":"2023-06-06T14:28:40.385341Z","iopub.status.idle":"2023-06-06T14:28:40.392467Z","shell.execute_reply.started":"2023-06-06T14:28:40.385316Z","shell.execute_reply":"2023-06-06T14:28:40.391153Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":" X_test.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:27:38.344824Z","iopub.execute_input":"2023-06-06T14:27:38.345168Z","iopub.status.idle":"2023-06-06T14:27:38.352614Z","shell.execute_reply.started":"2023-06-06T14:27:38.345146Z","shell.execute_reply":"2023-06-06T14:27:38.351776Z"},"trusted":true},"execution_count":94,"outputs":[{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"Manufacturer            object\nModel                   object\nSales_in_thousands     float64\n__year_resale_value    float64\nEngine_size            float64\nHorsepower             float64\nWheelbase              float64\nWidth                  float64\nLength                 float64\nCurb_weight            float64\nFuel_capacity          float64\nFuel_efficiency        float64\nPower_perf_factor      float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"X_test_transformed = full_pipeline.transform(X_test)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:40:06.406525Z","iopub.execute_input":"2023-06-06T14:40:06.406922Z","iopub.status.idle":"2023-06-06T14:40:06.417570Z","shell.execute_reply.started":"2023-06-06T14:40:06.406886Z","shell.execute_reply":"2023-06-06T14:40:06.416830Z"},"trusted":true},"execution_count":107,"outputs":[]},{"cell_type":"code","source":"X_test_transformed.shape, y_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:40:10.585743Z","iopub.execute_input":"2023-06-06T14:40:10.586105Z","iopub.status.idle":"2023-06-06T14:40:10.593530Z","shell.execute_reply.started":"2023-06-06T14:40:10.586078Z","shell.execute_reply":"2023-06-06T14:40:10.592003Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"((32, 63), (32,))"},"metadata":{}}]},{"cell_type":"code","source":"## final prediction \ntry:\n    \n    final_predictions = final_model.predict(X_test_transformed)\n    \nexcept ValueError:\n    \n    print(\"X has 63 features, but SVR is expecting 164 features as input\")\nelse: \n    print(final_predictions)","metadata":{"execution":{"iopub.status.busy":"2023-06-06T14:47:46.492423Z","iopub.execute_input":"2023-06-06T14:47:46.492765Z","iopub.status.idle":"2023-06-06T14:47:46.498474Z","shell.execute_reply.started":"2023-06-06T14:47:46.492737Z","shell.execute_reply":"2023-06-06T14:47:46.497674Z"},"trusted":true},"execution_count":111,"outputs":[{"name":"stdout","text":"X has 63 features, but SVR is expecting 164 features as input\n","output_type":"stream"}]},{"cell_type":"markdown","source":"We are getting this error because we don't have the same dimensionality of test data as traing data which is something what SVM expects.Dimensionality should be same in both the case which we dont have so we need to collect more data for the same. \n\nCheck this answer for the complete detail: \nhttps://stackoverflow.com/a/22592063/18304884\n\nWe will have to end this project or pause here and maybe work with our team to collect more data so that our dimensionality can match in test set as well, ","metadata":{}}]}