Car Purchase Price Prediction.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":8002323,"sourceType":"datasetVersion","datasetId":4712534}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Imports","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport xgboost as xgb\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split, RandomizedSearchCV\nfrom sklearn.metrics import r2_score, mean_absolute_error\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import PolynomialFeatures\n\n# DNN Imports\nimport os\nimport torch\nfrom tqdm import tqdm\nimport torch.utils.data\nfrom torch.utils.data import DataLoader, Dataset\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim.lr_scheduler import OneCycleLR\nfrom sklearn.preprocessing import StandardScaler","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2025-01-03T12:10:23.233687Z","iopub.execute_input":"2025-01-03T12:10:23.234001Z","iopub.status.idle":"2025-01-03T12:10:27.875926Z","shell.execute_reply.started":"2025-01-03T12:10:23.233975Z","shell.execute_reply":"2025-01-03T12:10:27.875238Z"},"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"markdown","source":"# Data Preprocessing","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/sales-prediction-dataset/car_purchasing.csv', encoding='latin1')","metadata":{"execution":{"iopub.status.busy":"2025-01-03T12:10:27.877490Z","iopub.execute_input":"2025-01-03T12:10:27.878354Z","iopub.status.idle":"2025-01-03T12:10:27.904134Z","shell.execute_reply.started":"2025-01-03T12:10:27.878322Z","shell.execute_reply":"2025-01-03T12:10:27.903571Z"},"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2025-01-03T12:10:30.824352Z","iopub.execute_input":"2025-01-03T12:10:30.825242Z","iopub.status.idle":"2025-01-03T12:10:30.847544Z","shell.execute_reply.started":"2025-01-03T12:10:30.825200Z","shell.execute_reply":"2025-01-03T12:10:30.846481Z"},"trusted":true},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"     customer name                                    customer e-mail  \\\n0    Martina Avila  cubilia.Curae.Phasellus@quisaccumsanconvallis.edu   \n1    Harlan Barnes                                eu.dolor@diam.co.uk   \n2  Naomi Rodriquez  vulputate.mauris.sagittis@ametconsectetueradip...   \n3  Jade Cunningham                            malesuada@dignissim.com   \n4     Cedric Leach     felis.ullamcorper.viverra@egetmollislectus.net   \n\n        country  gender        age  annual Salary  credit card debt  \\\n0      Bulgaria       0  41.851720    62812.09301      11609.380910   \n1        Belize       0  40.870623    66646.89292       9572.957136   \n2       Algeria       1  43.152897    53798.55112      11160.355060   \n3  Cook Islands       1  58.271369    79370.03798      14426.164850   \n4        Brazil       1  57.313749    59729.15130       5358.712177   \n\n     net worth  car purchase amount  \n0  238961.2505          35321.45877  \n1  530973.9078          45115.52566  \n2  638467.1773          42925.70921  \n3  548599.0524          67422.36313  \n4  560304.0671          55915.46248  ","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>customer name</th>\n      <th>customer e-mail</th>\n      <th>country</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>annual Salary</th>\n      <th>credit card debt</th>\n      <th>net worth</th>\n      <th>car purchase amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Martina Avila</td>\n      <td>cubilia.Curae.Phasellus@quisaccumsanconvallis.edu</td>\n      <td>Bulgaria</td>\n      <td>0</td>\n      <td>41.851720</td>\n      <td>62812.09301</td>\n      <td>11609.380910</td>\n      <td>238961.2505</td>\n      <td>35321.45877</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Harlan Barnes</td>\n      <td>eu.dolor@diam.co.uk</td>\n      <td>Belize</td>\n      <td>0</td>\n      <td>40.870623</td>\n      <td>66646.89292</td>\n      <td>9572.957136</td>\n      <td>530973.9078</td>\n      <td>45115.52566</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Naomi Rodriquez</td>\n      <td>vulputate.mauris.sagittis@ametconsectetueradip...</td>\n      <td>Algeria</td>\n      <td>1</td>\n      <td>43.152897</td>\n      <td>53798.55112</td>\n      <td>11160.355060</td>\n      <td>638467.1773</td>\n      <td>42925.70921</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Jade Cunningham</td>\n      <td>malesuada@dignissim.com</td>\n      <td>Cook Islands</td>\n      <td>1</td>\n      <td>58.271369</td>\n      <td>79370.03798</td>\n      <td>14426.164850</td>\n      <td>548599.0524</td>\n      <td>67422.36313</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Cedric Leach</td>\n      <td>felis.ullamcorper.viverra@egetmollislectus.net</td>\n      <td>Brazil</td>\n      <td>1</td>\n      <td>57.313749</td>\n      <td>59729.15130</td>\n      <td>5358.712177</td>\n      <td>560304.0671</td>\n      <td>55915.46248</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2025-01-03T12:10:31.768114Z","iopub.execute_input":"2025-01-03T12:10:31.768817Z","iopub.status.idle":"2025-01-03T12:10:31.793264Z","shell.execute_reply.started":"2025-01-03T12:10:31.768788Z","shell.execute_reply":"2025-01-03T12:10:31.792451Z"},"trusted":true},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"           gender         age  annual Salary  credit card debt  \\\ncount  500.000000  500.000000     500.000000        500.000000   \nmean     0.506000   46.241674   62127.239608       9607.645049   \nstd      0.500465    7.978862   11703.378228       3489.187973   \nmin      0.000000   20.000000   20000.000000        100.000000   \n25%      0.000000   40.949969   54391.977195       7397.515792   \n50%      1.000000   46.049901   62915.497035       9655.035568   \n75%      1.000000   51.612263   70117.862005      11798.867487   \nmax      1.000000   70.000000  100000.000000      20000.000000   \n\n            net worth  car purchase amount  \ncount      500.000000           500.000000  \nmean    431475.713625         44209.799218  \nstd     173536.756340         10773.178744  \nmin      20000.000000          9000.000000  \n25%     299824.195900         37629.896040  \n50%     426750.120650         43997.783390  \n75%     557324.478725         51254.709517  \nmax    1000000.000000         80000.000000  ","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>gender</th>\n      <th>age</th>\n      <th>annual Salary</th>\n      <th>credit card debt</th>\n      <th>net worth</th>\n      <th>car purchase amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>500.000000</td>\n      <td>500.000000</td>\n      <td>500.000000</td>\n      <td>500.000000</td>\n      <td>500.000000</td>\n      <td>500.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.506000</td>\n      <td>46.241674</td>\n      <td>62127.239608</td>\n      <td>9607.645049</td>\n      <td>431475.713625</td>\n      <td>44209.799218</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.500465</td>\n      <td>7.978862</td>\n      <td>11703.378228</td>\n      <td>3489.187973</td>\n      <td>173536.756340</td>\n      <td>10773.178744</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>20.000000</td>\n      <td>20000.000000</td>\n      <td>100.000000</td>\n      <td>20000.000000</td>\n      <td>9000.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>40.949969</td>\n      <td>54391.977195</td>\n      <td>7397.515792</td>\n      <td>299824.195900</td>\n      <td>37629.896040</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>46.049901</td>\n      <td>62915.497035</td>\n      <td>9655.035568</td>\n      <td>426750.120650</td>\n      <td>43997.783390</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>51.612263</td>\n      <td>70117.862005</td>\n      <td>11798.867487</td>\n      <td>557324.478725</td>\n      <td>51254.709517</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>70.000000</td>\n      <td>100000.000000</td>\n      <td>20000.000000</td>\n      <td>1000000.000000</td>\n      <td>80000.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":4},{"cell_type":"markdown","source":"## Scatter plot for car purchase amount vs. age","metadata":{}},{"cell_type":"code","source":"sns.scatterplot(x='age', y='car purchase amount', data=df)\nplt.title('Car Purchase Amount vs. Age')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2025-01-03T12:10:33.461318Z","iopub.execute_input":"2025-01-03T12:10:33.462262Z","iopub.status.idle":"2025-01-03T12:10:33.748299Z","shell.execute_reply.started":"2025-01-03T12:10:33.462213Z","shell.execute_reply":"2025-01-03T12:10:33.747475Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":5},{"cell_type":"markdown","source":"## Boxplot for car purchase amount by gender","metadata":{}},{"cell_type":"code","source":"sns.boxplot(x='gender', y='car purchase amount', data=df)\nplt.title('Car Purchase Amount by Gender')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2025-01-03T12:10:34.644672Z","iopub.execute_input":"2025-01-03T12:10:34.645018Z","iopub.status.idle":"2025-01-03T12:10:34.875270Z","shell.execute_reply.started":"2025-01-03T12:10:34.644991Z","shell.execute_reply":"2025-01-03T12:10:34.874484Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":6},{"cell_type":"markdown","source":"## Cross Validation","metadata":{}},{"cell_type":"markdown","source":"From previous runs I learned 'credit card debt' doesn't help predictions.","metadata":{}},{"cell_type":"code","source":"X = df[['age', 'annual Salary', 'net worth']]\ny = df['car purchase amount']","metadata":{"execution":{"iopub.status.busy":"2025-01-03T12:10:37.054573Z","iopub.execute_input":"2025-01-03T12:10:37.054893Z","iopub.status.idle":"2025-01-03T12:10:37.060936Z","shell.execute_reply.started":"2025-01-03T12:10:37.054868Z","shell.execute_reply":"2025-01-03T12:10:37.059934Z"},"trusted":true},"outputs":[],"execution_count":7},{"cell_type":"markdown","source":"### Additional Feature Engineering","metadata":{}},{"cell_type":"code","source":"def load_poly_features(df_train, cols):\n\n    # Select the specified columns for polynomial features\n    poly_features = df_train[cols]\n    \n    # Impute missing values\n    imputer = SimpleImputer(strategy='median')\n    poly_features = imputer.fit_transform(poly_features)\n    \n    # Create and fit polynomial features\n    poly_transformer = PolynomialFeatures(degree=3)\n    poly_features = poly_transformer.fit_transform(poly_features)\n    \n    # Convert back to a dataframe with proper column names\n    df_poly_features = pd.DataFrame(poly_features, columns=poly_transformer.get_feature_names_out(cols))\n\n    return df_poly_features","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:10:40.479179Z","iopub.execute_input":"2025-01-03T12:10:40.479776Z","iopub.status.idle":"2025-01-03T12:10:40.484739Z","shell.execute_reply.started":"2025-01-03T12:10:40.479748Z","shell.execute_reply":"2025-01-03T12:10:40.483803Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"correlated_cols = ['age', 'annual Salary', 'net worth']\ndf_train_poly =  load_poly_features(X, cols=correlated_cols)\nprint(f\"Shape train: {df_train_poly.shape}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:10:41.826854Z","iopub.execute_input":"2025-01-03T12:10:41.827443Z","iopub.status.idle":"2025-01-03T12:10:41.838997Z","shell.execute_reply.started":"2025-01-03T12:10:41.827417Z","shell.execute_reply":"2025-01-03T12:10:41.838082Z"}},"outputs":[{"name":"stdout","text":"Shape train: (500, 20)\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"X = X.merge(right=df_train_poly.reset_index(drop=True))\nX.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:10:42.854643Z","iopub.execute_input":"2025-01-03T12:10:42.855210Z","iopub.status.idle":"2025-01-03T12:10:42.883142Z","shell.execute_reply.started":"2025-01-03T12:10:42.855182Z","shell.execute_reply":"2025-01-03T12:10:42.882294Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"         age  annual Salary    net worth    1        age^2  age annual Salary  \\\n0  41.851720    62812.09301  238961.2505  1.0  1751.566450       2.628794e+06   \n1  40.870623    66646.89292  530973.9078  1.0  1670.407853       2.723900e+06   \n2  43.152897    53798.55112  638467.1773  1.0  1862.172560       2.321563e+06   \n3  58.271369    79370.03798  548599.0524  1.0  3395.552498       4.625001e+06   \n4  57.313749    59729.15130  560304.0671  1.0  3284.865876       3.423302e+06   \n\n   age net worth  annual Salary^2  annual Salary net worth   net worth^2  \\\n0   1.000094e+07     3.945359e+09             1.500966e+10  5.710248e+10   \n1   2.170123e+07     4.441808e+09             3.538776e+10  2.819333e+11   \n2   2.755171e+07     2.894284e+09             3.434861e+10  4.076403e+11   \n3   3.196762e+07     6.299603e+09             4.354233e+10  3.009609e+11   \n4   3.211313e+07     3.567572e+09             3.346649e+10  3.139406e+11   \n\n           age^3  age^2 annual Salary  age^2 net worth  age annual Salary^2  \\\n0   73306.068286         1.100196e+08     4.185565e+08         1.651201e+11   \n1   68270.610202         1.113275e+08     8.869430e+08         1.815395e+11   \n2   80358.141556         1.001822e+08     1.188936e+09         1.248967e+11   \n3  197863.494073         2.695051e+08     1.862797e+09         3.670865e+11   \n4  188267.979795         1.962023e+08     1.840524e+09         2.044709e+11   \n\n   age annual Salary net worth  age net worth^2  annual Salary^3  \\\n0                 6.281799e+11     2.389837e+12     2.478163e+14   \n1                 1.446320e+12     1.152279e+13     2.960327e+14   \n2                 1.482242e+12     1.759086e+13     1.557083e+14   \n3                 2.537271e+12     1.753740e+13     4.999997e+14   \n4                 1.918090e+12     1.799312e+13     2.130880e+14   \n\n   annual Salary^2 net worth  annual Salary net worth^2   net worth^3  \n0               9.427879e+14               3.586726e+15  1.364528e+16  \n1               2.358484e+15               1.878998e+16  1.496992e+17  \n2               1.847905e+15               2.193046e+16  2.602650e+17  \n3               3.455956e+15               2.388728e+16  1.651069e+17  \n4               1.998925e+15               1.875141e+16  1.759022e+17  ","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>age</th>\n      <th>annual Salary</th>\n      <th>net worth</th>\n      <th>1</th>\n      <th>age^2</th>\n      <th>age annual Salary</th>\n      <th>age net worth</th>\n      <th>annual Salary^2</th>\n      <th>annual Salary net worth</th>\n      <th>net worth^2</th>\n      <th>age^3</th>\n      <th>age^2 annual Salary</th>\n      <th>age^2 net worth</th>\n      <th>age annual Salary^2</th>\n      <th>age annual Salary net worth</th>\n      <th>age net worth^2</th>\n      <th>annual Salary^3</th>\n      <th>annual Salary^2 net worth</th>\n      <th>annual Salary net worth^2</th>\n      <th>net worth^3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>41.851720</td>\n      <td>62812.09301</td>\n      <td>238961.2505</td>\n      <td>1.0</td>\n      <td>1751.566450</td>\n      <td>2.628794e+06</td>\n      <td>1.000094e+07</td>\n      <td>3.945359e+09</td>\n      <td>1.500966e+10</td>\n      <td>5.710248e+10</td>\n      <td>73306.068286</td>\n      <td>1.100196e+08</td>\n      <td>4.185565e+08</td>\n      <td>1.651201e+11</td>\n      <td>6.281799e+11</td>\n      <td>2.389837e+12</td>\n      <td>2.478163e+14</td>\n      <td>9.427879e+14</td>\n      <td>3.586726e+15</td>\n      <td>1.364528e+16</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>40.870623</td>\n      <td>66646.89292</td>\n      <td>530973.9078</td>\n      <td>1.0</td>\n      <td>1670.407853</td>\n      <td>2.723900e+06</td>\n      <td>2.170123e+07</td>\n      <td>4.441808e+09</td>\n      <td>3.538776e+10</td>\n      <td>2.819333e+11</td>\n      <td>68270.610202</td>\n      <td>1.113275e+08</td>\n      <td>8.869430e+08</td>\n      <td>1.815395e+11</td>\n      <td>1.446320e+12</td>\n      <td>1.152279e+13</td>\n      <td>2.960327e+14</td>\n      <td>2.358484e+15</td>\n      <td>1.878998e+16</td>\n      <td>1.496992e+17</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>43.152897</td>\n      <td>53798.55112</td>\n      <td>638467.1773</td>\n      <td>1.0</td>\n      <td>1862.172560</td>\n      <td>2.321563e+06</td>\n      <td>2.755171e+07</td>\n      <td>2.894284e+09</td>\n      <td>3.434861e+10</td>\n      <td>4.076403e+11</td>\n      <td>80358.141556</td>\n      <td>1.001822e+08</td>\n      <td>1.188936e+09</td>\n      <td>1.248967e+11</td>\n      <td>1.482242e+12</td>\n      <td>1.759086e+13</td>\n      <td>1.557083e+14</td>\n      <td>1.847905e+15</td>\n      <td>2.193046e+16</td>\n      <td>2.602650e+17</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>58.271369</td>\n      <td>79370.03798</td>\n      <td>548599.0524</td>\n      <td>1.0</td>\n      <td>3395.552498</td>\n      <td>4.625001e+06</td>\n      <td>3.196762e+07</td>\n      <td>6.299603e+09</td>\n      <td>4.354233e+10</td>\n      <td>3.009609e+11</td>\n      <td>197863.494073</td>\n      <td>2.695051e+08</td>\n      <td>1.862797e+09</td>\n      <td>3.670865e+11</td>\n      <td>2.537271e+12</td>\n      <td>1.753740e+13</td>\n      <td>4.999997e+14</td>\n      <td>3.455956e+15</td>\n      <td>2.388728e+16</td>\n      <td>1.651069e+17</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>57.313749</td>\n      <td>59729.15130</td>\n      <td>560304.0671</td>\n      <td>1.0</td>\n      <td>3284.865876</td>\n      <td>3.423302e+06</td>\n      <td>3.211313e+07</td>\n      <td>3.567572e+09</td>\n      <td>3.346649e+10</td>\n      <td>3.139406e+11</td>\n      <td>188267.979795</td>\n      <td>1.962023e+08</td>\n      <td>1.840524e+09</td>\n      <td>2.044709e+11</td>\n      <td>1.918090e+12</td>\n      <td>1.799312e+13</td>\n      <td>2.130880e+14</td>\n      <td>1.998925e+15</td>\n      <td>1.875141e+16</td>\n      <td>1.759022e+17</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":10},{"cell_type":"markdown","source":"From previous runs I learned that these columns don't help predictions.","metadata":{}},{"cell_type":"code","source":"# List of columns to drop\ncolumns_to_drop = ['annual Salary^2 net worth', 'age net worth^2',\n       'annual Salary net worth', 'age net worth', 'age^3', 'annual Salary',\n       'net worth^2', 'age', 'age^2', 'net worth', 'annual Salary^2',\n       'net worth^3', 'annual Salary^3', '1']\n\nX = X.drop(columns=columns_to_drop, errors='ignore')\n\nX.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:10:48.945926Z","iopub.execute_input":"2025-01-03T12:10:48.946714Z","iopub.status.idle":"2025-01-03T12:10:48.959292Z","shell.execute_reply.started":"2025-01-03T12:10:48.946688Z","shell.execute_reply":"2025-01-03T12:10:48.958418Z"}},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"   age annual Salary  age^2 annual Salary  age^2 net worth  \\\n0       2.628794e+06         1.100196e+08     4.185565e+08   \n1       2.723900e+06         1.113275e+08     8.869430e+08   \n2       2.321563e+06         1.001822e+08     1.188936e+09   \n3       4.625001e+06         2.695051e+08     1.862797e+09   \n4       3.423302e+06         1.962023e+08     1.840524e+09   \n\n   age annual Salary^2  age annual Salary net worth  annual Salary net worth^2  \n0         1.651201e+11                 6.281799e+11               3.586726e+15  \n1         1.815395e+11                 1.446320e+12               1.878998e+16  \n2         1.248967e+11                 1.482242e+12               2.193046e+16  \n3         3.670865e+11                 2.537271e+12               2.388728e+16  \n4         2.044709e+11                 1.918090e+12               1.875141e+16  ","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>age annual Salary</th>\n      <th>age^2 annual Salary</th>\n      <th>age^2 net worth</th>\n      <th>age annual Salary^2</th>\n      <th>age annual Salary net worth</th>\n      <th>annual Salary net worth^2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.628794e+06</td>\n      <td>1.100196e+08</td>\n      <td>4.185565e+08</td>\n      <td>1.651201e+11</td>\n      <td>6.281799e+11</td>\n      <td>3.586726e+15</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.723900e+06</td>\n      <td>1.113275e+08</td>\n      <td>8.869430e+08</td>\n      <td>1.815395e+11</td>\n      <td>1.446320e+12</td>\n      <td>1.878998e+16</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.321563e+06</td>\n      <td>1.001822e+08</td>\n      <td>1.188936e+09</td>\n      <td>1.248967e+11</td>\n      <td>1.482242e+12</td>\n      <td>2.193046e+16</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.625001e+06</td>\n      <td>2.695051e+08</td>\n      <td>1.862797e+09</td>\n      <td>3.670865e+11</td>\n      <td>2.537271e+12</td>\n      <td>2.388728e+16</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.423302e+06</td>\n      <td>1.962023e+08</td>\n      <td>1.840524e+09</td>\n      <td>2.044709e+11</td>\n      <td>1.918090e+12</td>\n      <td>1.875141e+16</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nprint(\"Length of X_train:\", len(X_train))\nprint(\"Length of X_test:\", len(X_test))","metadata":{"execution":{"iopub.status.busy":"2025-01-03T12:10:55.541532Z","iopub.execute_input":"2025-01-03T12:10:55.542287Z","iopub.status.idle":"2025-01-03T12:10:55.550276Z","shell.execute_reply.started":"2025-01-03T12:10:55.542247Z","shell.execute_reply":"2025-01-03T12:10:55.549442Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Length of X_train: 400\nLength of X_test: 100\n","output_type":"stream"}],"execution_count":12},{"cell_type":"markdown","source":"# Training Models","metadata":{}},{"cell_type":"markdown","source":"## Randomized Search On Hyper Parameters ","metadata":{}},{"cell_type":"code","source":"param_dist_rf = {\n    'n_estimators': [50, 100, 200, 300, 500],\n    'max_depth': [10, 20, 30, 50, None],\n    'min_samples_split': [2, 5, 10, 15],\n    'min_samples_leaf': [1, 2, 4, 6, 8],\n    'max_features': [None, 'sqrt', 'log2', 0.2, 0.5],\n    'bootstrap': [True, False],\n}\n\ntmp_rf_model = RandomForestRegressor()\n\nparam_dist_xgb = {\n    'n_estimators': [50, 100, 200, 300, 500],\n    'max_depth': [3, 6, 10, None],\n    'learning_rate': [0.01, 0.05, 0.1, 0.2],\n    'subsample': [0.6, 0.8, 1.0],\n    'colsample_bytree': [0.6, 0.8, 1.0],\n    'min_child_weight': [1, 3, 5]\n}\n\ntmp_xgb_model = xgb.XGBRegressor(objective='reg:squarederror', random_state=42)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:19:53.199650Z","iopub.execute_input":"2025-01-03T12:19:53.200275Z","iopub.status.idle":"2025-01-03T12:19:53.206667Z","shell.execute_reply.started":"2025-01-03T12:19:53.200242Z","shell.execute_reply":"2025-01-03T12:19:53.205745Z"}},"outputs":[],"execution_count":35},{"cell_type":"code","source":"def perform_random_search(tmp_model, param_dist, n_iter=50, random_state=42):\n\n    random_search = RandomizedSearchCV(\n        estimator=tmp_model,\n        param_distributions=param_dist,\n        n_iter=n_iter,\n        cv=3,\n        random_state=random_state,\n        n_jobs=-1,\n        verbose=0\n    )\n\n    # Fit the model to the training data\n    random_search.fit(X_train, y_train)\n\n    # Retrieve the best parameters and the best model\n    best_params = random_search.best_params_\n    model = random_search.best_estimator_\n\n    # Print the best parameters and model name\n    print(f\"Best {model.__class__.__name__} parameters found:\\n{best_params}\")\n\n    return model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:17:27.069719Z","iopub.execute_input":"2025-01-03T12:17:27.070046Z","iopub.status.idle":"2025-01-03T12:17:27.075279Z","shell.execute_reply.started":"2025-01-03T12:17:27.070011Z","shell.execute_reply":"2025-01-03T12:17:27.074457Z"}},"outputs":[],"execution_count":32},{"cell_type":"markdown","source":"## Training Ensemble Models","metadata":{}},{"cell_type":"code","source":"rf_model = perform_random_search(tmp_rf_model, param_dist_rf)\nxgb_model = perform_random_search(tmp_xgb_model, param_dist_xgb)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:17:29.655494Z","iopub.execute_input":"2025-01-03T12:17:29.655830Z","iopub.status.idle":"2025-01-03T12:18:14.453252Z","shell.execute_reply.started":"2025-01-03T12:17:29.655807Z","shell.execute_reply":"2025-01-03T12:18:14.452507Z"}},"outputs":[{"name":"stdout","text":"Best RandomForestRegressor parameters found:\n{'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 0.5, 'max_depth': 30, 'bootstrap': False}\nBest XGBRegressor parameters found:\n{'subsample': 0.6, 'n_estimators': 500, 'min_child_weight': 3, 'max_depth': 10, 'learning_rate': 0.05, 'colsample_bytree': 0.6}\n","output_type":"stream"}],"execution_count":33},{"cell_type":"markdown","source":"## Training DNN","metadata":{}},{"cell_type":"code","source":"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nworkers = os.cpu_count()\nprint(device)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:12:17.255856Z","iopub.execute_input":"2025-01-03T12:12:17.256219Z","iopub.status.idle":"2025-01-03T12:12:17.263972Z","shell.execute_reply.started":"2025-01-03T12:12:17.256183Z","shell.execute_reply":"2025-01-03T12:12:17.263130Z"}},"outputs":[{"name":"stdout","text":"cuda\n","output_type":"stream"}],"execution_count":16},{"cell_type":"code","source":"class DNNDataset(Dataset):\n    def __init__(self, X, Y, scale_columns=False):\n        # Initialize scalers\n        self.input_scaler = None\n        self.target_scaler = None\n\n        if scale_columns:\n            # Scale inputs\n            self.input_scaler = StandardScaler()\n            scaled_X = self.input_scaler.fit_transform(X)\n            self.inputs = torch.tensor(scaled_X, dtype=torch.float32)\n            \n            # Scale targets\n            self.target_scaler = StandardScaler()\n            scaled_Y = self.target_scaler.fit_transform(Y.values.reshape(-1, 1))\n            self.targets = torch.tensor(scaled_Y, dtype=torch.float32)\n        else:\n            # Use raw inputs and targets\n            self.inputs = torch.tensor(X.values, dtype=torch.float32)\n            self.targets = torch.tensor(Y.values, dtype=torch.float32).unsqueeze(1)\n\n    def __len__(self):\n        return len(self.inputs)\n\n    def __getitem__(self, idx):\n        return self.inputs[idx], self.targets[idx]\n\n    def scale_values(self, values, is_input=True):\n        scaler = self.input_scaler if is_input else self.target_scaler\n        if scaler:\n            return scaler.transform(values)\n        return values\n\n    def inverse_transform_values(self, scaled_values, is_input=True):\n        scaler = self.input_scaler if is_input else self.target_scaler\n        if scaler:\n            return scaler.inverse_transform(scaled_values)\n        return scaled_values","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:12:18.127191Z","iopub.execute_input":"2025-01-03T12:12:18.127547Z","iopub.status.idle":"2025-01-03T12:12:18.135362Z","shell.execute_reply.started":"2025-01-03T12:12:18.127520Z","shell.execute_reply":"2025-01-03T12:12:18.134458Z"}},"outputs":[],"execution_count":17},{"cell_type":"code","source":"train_dataset = DNNDataset(X_train, y_train, scale_columns=True)\nvalidation_dataset = DNNDataset(X_test, y_test, scale_columns=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:12:20.302715Z","iopub.execute_input":"2025-01-03T12:12:20.303033Z","iopub.status.idle":"2025-01-03T12:12:20.337741Z","shell.execute_reply.started":"2025-01-03T12:12:20.303010Z","shell.execute_reply":"2025-01-03T12:12:20.337118Z"}},"outputs":[],"execution_count":18},{"cell_type":"code","source":"# Define batch size\nbatch_size = 5\n\nval_loader = DataLoader(validation_dataset, batch_size=batch_size, num_workers=workers, pin_memory=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:12:21.790723Z","iopub.execute_input":"2025-01-03T12:12:21.791038Z","iopub.status.idle":"2025-01-03T12:12:21.795602Z","shell.execute_reply.started":"2025-01-03T12:12:21.791013Z","shell.execute_reply":"2025-01-03T12:12:21.794751Z"}},"outputs":[],"execution_count":19},{"cell_type":"code","source":"# Get the image paths from both datasets\ntrain_inputs = set(train_dataset.inputs)\nval_inputs = set(validation_dataset.inputs)\n\n# Check for any common paths\ncommon_data = train_inputs.intersection(val_inputs)\n\n# Print out the results\nif common_data:\n    print(f\"Found {len(common_data)} common data between training and validation datasets:\")\n    for data in common_data:\n        print(data)\nelse:\n    print(\"No common found between training and validation datasets.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:12:22.694287Z","iopub.execute_input":"2025-01-03T12:12:22.694959Z","iopub.status.idle":"2025-01-03T12:12:22.708040Z","shell.execute_reply.started":"2025-01-03T12:12:22.694931Z","shell.execute_reply":"2025-01-03T12:12:22.707174Z"}},"outputs":[{"name":"stdout","text":"No common found between training and validation datasets.\n","output_type":"stream"}],"execution_count":20},{"cell_type":"code","source":"def train_and_validate(model, train_dataset, validation_loader, criterion, optimizer, epochs=5, train_loss_threshold=None):\n    train_running_loss_history = []\n    validation_running_loss_history = []\n    lr_history = []\n    prev_val_loss = float('inf')\n    early_stop = False\n\n    # Calculate the total number of steps in one cycle (total batches across all epochs)\n    num_batches = (len(train_dataset) + batch_size - 1) // batch_size\n    total_steps = epochs * num_batches\n    \n    # Define the OneCycleLR scheduler\n    init_lr = optimizer.param_groups[0]['lr']\n    scheduler = OneCycleLR(optimizer, max_lr=init_lr * 10, total_steps=total_steps)\n\n    # Use tqdm as a context manager for epochs\n    with tqdm(total=epochs, desc=\"Training Progress\", leave=True, dynamic_ncols=True) as progress_bar:\n        for e in range(epochs):\n            train_running_loss = 0.0\n            validation_running_loss = 0.0\n            train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True)\n            \n            if early_stop:\n                break\n\n            model.train()\n            # Training loop\n            for ith_batch, sample_batched in enumerate(train_loader):\n                x_train, y_train = sample_batched[:2]\n                X_train = x_train.to(device)\n                Y_train = y_train.to(device)\n\n                optimizer.zero_grad()\n                y_pred = model(X_train)\n\n                # Calculate losses\n                loss = criterion(y_pred, Y_train)\n\n                # Backward and optimize\n                loss.backward()\n                optimizer.step()\n\n                train_running_loss += loss.item()\n                \n                # Step the scheduler\n                scheduler.step()\n                lr_history.append(optimizer.param_groups[0]['lr'])\n\n            # Validation loop\n            with torch.no_grad():\n                model.eval()\n                for ith_batch, sample_batched in enumerate(validation_loader):\n                    x_val, y_val = sample_batched[:2]\n                    X_val = x_val.to(device)\n                    Y_val = y_val.to(device)\n\n                    pred_val = model(X_val)\n                    \n                    # Calculate losses\n                    val_loss = criterion(pred_val, Y_val)\n                    \n                    validation_running_loss += val_loss.item()\n\n                train_epoch_loss = train_running_loss / len(train_loader)\n                validation_epoch_loss = validation_running_loss / len(validation_loader)\n\n                train_running_loss_history.append(train_epoch_loss)\n                validation_running_loss_history.append(validation_epoch_loss)\n                \n                # Update tqdm with train and validation loss for the current epoch\n                progress_bar.set_description(f\"Epoch {e+1}: Train Loss = {train_epoch_loss:.5f}, Val Loss = {validation_epoch_loss:.5f}\")\n                progress_bar.update(1)\n\n                if train_loss_threshold is not None:\n                    if train_epoch_loss < train_loss_threshold and validation_epoch_loss >= prev_val_loss:\n                        progress_bar.set_description(f\"Stopping early: Train Loss = {train_epoch_loss:.5f}, Val Loss = {validation_epoch_loss:.5f}\")\n                        early_stop = True\n                    prev_val_loss = validation_epoch_loss\n\n            torch.cuda.empty_cache()\n\n    # Plot loss history\n    plt.figure(figsize=(12, 5))\n\n    # Plot loss\n    plt.subplot(1, 2, 1)\n    plt.plot(train_running_loss_history, label='Train Loss')\n    plt.plot(validation_running_loss_history, label='Validation Loss')\n    plt.xlabel('Epoch')\n    plt.ylabel('Loss')\n    plt.title('Training and Validation Loss')\n    plt.legend()\n\n    # Plot learning rate\n    plt.subplot(1, 2, 2)\n    plt.plot(lr_history, label='Learning Rate')\n    plt.xlabel('Step')\n    plt.ylabel('Learning Rate')\n    plt.title('Learning Rate Schedule')\n    plt.legend()\n\n    plt.tight_layout()\n    plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:12:23.422468Z","iopub.execute_input":"2025-01-03T12:12:23.423107Z","iopub.status.idle":"2025-01-03T12:12:23.435642Z","shell.execute_reply.started":"2025-01-03T12:12:23.423079Z","shell.execute_reply":"2025-01-03T12:12:23.434764Z"}},"outputs":[],"execution_count":21},{"cell_type":"code","source":"class SalesDNN(nn.Module):\n    def __init__(self, input_size, output_size=1):\n        super(SalesDNN, self).__init__()\n        self.model = nn.Sequential(\n            nn.Linear(input_size, 128),\n            nn.ReLU(),\n            nn.Linear(128, 64),\n            nn.ReLU(),\n            nn.Linear(64, 32),\n            nn.ReLU(),\n            nn.Linear(32, output_size)\n        )\n    \n    def forward(self, x):\n        return self.model(x)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:12:25.166155Z","iopub.execute_input":"2025-01-03T12:12:25.167003Z","iopub.status.idle":"2025-01-03T12:12:25.171887Z","shell.execute_reply.started":"2025-01-03T12:12:25.166973Z","shell.execute_reply":"2025-01-03T12:12:25.171070Z"}},"outputs":[],"execution_count":22},{"cell_type":"code","source":"# Define your hyperparameters\nepochs = 25\nlearning_rate = 0.005\nweight_decay = 0.0005\ninput_size = X.shape[1]\n\n# Define loss\ncriterion = nn.MSELoss()\n\n# Load pre-trained model\ndnn_model = SalesDNN(input_size).to(device)\n\nfull_opt = optim.AdamW(dnn_model.parameters(), lr=learning_rate/10, weight_decay=weight_decay)\n\ntrain_and_validate(dnn_model, train_dataset, val_loader, criterion, full_opt, epochs)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:12:26.902161Z","iopub.execute_input":"2025-01-03T12:12:26.902499Z","iopub.status.idle":"2025-01-03T12:12:41.321275Z","shell.execute_reply.started":"2025-01-03T12:12:26.902473Z","shell.execute_reply":"2025-01-03T12:12:41.320279Z"}},"outputs":[{"name":"stderr","text":"Epoch 25: Train Loss = 0.00022, Val Loss = 0.00148: 100%|██████████| 25/25 [00:11<00:00,  2.16it/s]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x500 with 2 Axes>","image/png":"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"},"metadata":{}}],"execution_count":23},{"cell_type":"markdown","source":"# Evaluation","metadata":{}},{"cell_type":"code","source":"def compare_models(models):\n    mae_scores = []\n    r2_scores = []\n\n    # Loop through the models, make predictions, and calculate scores\n    for model in models:\n\n        if isinstance(model, SalesDNN):\n            model.eval()\n            all_predictions = []\n            all_targets = []\n    \n            with torch.no_grad():\n                for inputs, targets in val_loader:\n                    inputs, targets = inputs.to(device), targets.to(device)\n                    \n                    predictions = model(inputs)\n                    \n                    # Collect predictions and targets\n                    all_predictions.extend(predictions.cpu().numpy())\n                    all_targets.extend(targets.cpu().numpy())\n                    \n            # Convert predictions and targets back to the original scale\n            all_predictions_original = validation_dataset.inverse_transform_values(\n                np.array(all_predictions).reshape(-1, 1), is_input=False)\n            all_targets_original = validation_dataset.inverse_transform_values(\n                np.array(all_targets).reshape(-1, 1), is_input=False)\n\n            mae = mean_absolute_error(all_targets_original, all_predictions_original)\n            r2 = r2_score(all_targets_original, all_predictions_original)\n                \n        else:\n            y_pred = model.predict(X_test)\n            mae = mean_absolute_error(y_test, y_pred)\n            r2 = r2_score(y_test, y_pred)\n        \n        mae_scores.append(mae)\n        r2_scores.append(r2)\n\n    # Plot the comparison of MAE and R² scores\n    fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n\n    # Plot MAE scores\n    ax[0].bar([model.__class__.__name__ for model in models], mae_scores, color='skyblue')\n    ax[0].set_title(\"Mean Absolute Error Comparison\")\n    ax[0].set_ylabel(\"Mean Absolute Error\")\n\n    # Plot R² scores\n    ax[1].bar([model.__class__.__name__ for model in models], r2_scores, color='lightcoral')\n    ax[1].set_title(\"R² Score Comparison\")\n    ax[1].set_ylabel(\"R² Score\")\n\n    plt.tight_layout()\n    plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:12:51.985285Z","iopub.execute_input":"2025-01-03T12:12:51.985845Z","iopub.status.idle":"2025-01-03T12:12:51.995614Z","shell.execute_reply.started":"2025-01-03T12:12:51.985808Z","shell.execute_reply":"2025-01-03T12:12:51.994726Z"}},"outputs":[],"execution_count":24},{"cell_type":"code","source":"compare_models([rf_model, xgb_model, dnn_model])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-03T12:19:04.905700Z","iopub.execute_input":"2025-01-03T12:19:04.906014Z","iopub.status.idle":"2025-01-03T12:19:05.446616Z","shell.execute_reply.started":"2025-01-03T12:19:04.905992Z","shell.execute_reply":"2025-01-03T12:19:05.445637Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x600 with 2 Axes>","image/png":"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Get feature names and importances\nfeatures = X.columns\nimportances = xgb_model.feature_importances_\n\n# Sort features by importance\nsorted_indices = np.argsort(importances)[::-1]\nsorted_features = features[sorted_indices]\nsorted_importances = importances[sorted_indices]\n\n# Plot the feature importances\nplt.figure(figsize=(8, 5))\nplt.barh(sorted_features, sorted_importances, color='skyblue')\nplt.xlabel('Importance')\nplt.title('Feature Importances')\nplt.gca().invert_yaxis()  # To display the highest importance at the top\nplt.show()\n\nlow_importance = 0.05\n\n# Get the names of features with low importance\nlow_importance_features = sorted_features[sorted_importances <= low_importance]\n\n# Print the names of the low-importance features\nprint(f\"Features with importance less than or equal to {low_importance}:\")\nprint(low_importance_features.tolist())","metadata":{"execution":{"iopub.status.busy":"2025-01-03T12:13:40.637012Z","iopub.execute_input":"2025-01-03T12:13:40.637357Z","iopub.status.idle":"2025-01-03T12:13:40.868428Z","shell.execute_reply.started":"2025-01-03T12:13:40.637323Z","shell.execute_reply":"2025-01-03T12:13:40.867552Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"Features with importance less than or equal to 0.05:\n['annual Salary net worth^2']\n","output_type":"stream"}],"execution_count":26},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}