playground-s3e12-xgboost-lgbm-catboost.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","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":[{"source":"<a href=\"https://www.kaggle.com/code/averma111/pg-s3e12-xgboost-lgbm-catboost-no-nn-used?scriptVersionId=125081846\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom scipy import stats\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split, cross_validate, KFold, GridSearchCV\nfrom sklearn.metrics import mean_squared_log_error\nfrom lightgbm import LGBMClassifier\nfrom xgboost import XGBClassifier\nfrom catboost import  CatBoostClassifier, Pool\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom IPython.display import display, HTML\n","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:40:39.557407Z","iopub.execute_input":"2023-04-08T22:40:39.558524Z","iopub.status.idle":"2023-04-08T22:40:39.566139Z","shell.execute_reply.started":"2023-04-08T22:40:39.558469Z","shell.execute_reply":"2023-04-08T22:40:39.564685Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"markdown","source":"## Cosmetic Changes","metadata":{}},{"cell_type":"code","source":"!wget http://bit.ly/3ZLyF82 -O CSS.css -q\n    \nfrom IPython.core.display import HTML\nwith open('./CSS.css', 'r') as file:\n    custom_css = file.read()\n\nHTML(custom_css)","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:40:39.605331Z","iopub.execute_input":"2023-04-08T22:40:39.605748Z","iopub.status.idle":"2023-04-08T22:41:00.674894Z","shell.execute_reply.started":"2023-04-08T22:40:39.605711Z","shell.execute_reply":"2023-04-08T22:41:00.67367Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":""},"metadata":{}}]},{"cell_type":"markdown","source":"## Reading the datasets","metadata":{}},{"cell_type":"code","source":"Root_path ='/kaggle/input/playground-series-s3e12'\nRoot_original ='/kaggle/input/kidney-stone-prediction-based-on-urine-analysis'\n\noriginal = pd.read_csv(Root_original+'/kindey stone urine analysis.csv')\ntrain = pd.read_csv(Root_path+'/train.csv')\ntest = pd.read_csv(Root_path+'/test.csv')","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:00.677452Z","iopub.execute_input":"2023-04-08T22:41:00.677802Z","iopub.status.idle":"2023-04-08T22:41:00.697375Z","shell.execute_reply.started":"2023-04-08T22:41:00.677765Z","shell.execute_reply":"2023-04-08T22:41:00.696381Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"train.head(5)","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:00.698704Z","iopub.execute_input":"2023-04-08T22:41:00.699156Z","iopub.status.idle":"2023-04-08T22:41:00.717522Z","shell.execute_reply.started":"2023-04-08T22:41:00.699092Z","shell.execute_reply":"2023-04-08T22:41:00.716186Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"   id  gravity    ph  osmo  cond  urea  calc  target\n0   0    1.013  6.19   443  14.8   124  1.45       0\n1   1    1.025  5.40   703  23.6   394  4.18       0\n2   2    1.009  6.13   371  24.5   159  9.04       0\n3   3    1.021  4.91   442  20.8   398  6.63       1\n4   4    1.021  5.53   874  17.8   385  2.21       1","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>id</th>\n      <th>gravity</th>\n      <th>ph</th>\n      <th>osmo</th>\n      <th>cond</th>\n      <th>urea</th>\n      <th>calc</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1.013</td>\n      <td>6.19</td>\n      <td>443</td>\n      <td>14.8</td>\n      <td>124</td>\n      <td>1.45</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1.025</td>\n      <td>5.40</td>\n      <td>703</td>\n      <td>23.6</td>\n      <td>394</td>\n      <td>4.18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>1.009</td>\n      <td>6.13</td>\n      <td>371</td>\n      <td>24.5</td>\n      <td>159</td>\n      <td>9.04</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>1.021</td>\n      <td>4.91</td>\n      <td>442</td>\n      <td>20.8</td>\n      <td>398</td>\n      <td>6.63</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>1.021</td>\n      <td>5.53</td>\n      <td>874</td>\n      <td>17.8</td>\n      <td>385</td>\n      <td>2.21</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test.head(5)","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:00.719596Z","iopub.execute_input":"2023-04-08T22:41:00.720251Z","iopub.status.idle":"2023-04-08T22:41:00.735427Z","shell.execute_reply.started":"2023-04-08T22:41:00.720196Z","shell.execute_reply":"2023-04-08T22:41:00.734179Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"    id  gravity    ph  osmo  cond  urea   calc\n0  414    1.017  5.24   345  11.5   152   1.16\n1  415    1.020  5.68   874  29.0   385   3.46\n2  416    1.024  5.36   698  19.5   354  13.00\n3  417    1.020  5.33   668  25.3   252   3.46\n4  418    1.011  5.87   567  29.0   457   2.36","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>id</th>\n      <th>gravity</th>\n      <th>ph</th>\n      <th>osmo</th>\n      <th>cond</th>\n      <th>urea</th>\n      <th>calc</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>414</td>\n      <td>1.017</td>\n      <td>5.24</td>\n      <td>345</td>\n      <td>11.5</td>\n      <td>152</td>\n      <td>1.16</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>415</td>\n      <td>1.020</td>\n      <td>5.68</td>\n      <td>874</td>\n      <td>29.0</td>\n      <td>385</td>\n      <td>3.46</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>416</td>\n      <td>1.024</td>\n      <td>5.36</td>\n      <td>698</td>\n      <td>19.5</td>\n      <td>354</td>\n      <td>13.00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>417</td>\n      <td>1.020</td>\n      <td>5.33</td>\n      <td>668</td>\n      <td>25.3</td>\n      <td>252</td>\n      <td>3.46</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>418</td>\n      <td>1.011</td>\n      <td>5.87</td>\n      <td>567</td>\n      <td>29.0</td>\n      <td>457</td>\n      <td>2.36</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Summary of the datasets","metadata":{}},{"cell_type":"code","source":"def summary(text, df):\n    print(f'{text} shape: {df.shape}')\n    summ = pd.DataFrame(df.dtypes, columns=['dtypes'])\n    summ['null'] = df.isnull().sum()\n    summ['unique'] = df.nunique()\n    summ['min'] = df.min()\n    summ['median'] = df.median()\n    summ['max'] = df.max()\n    summ['mean'] = df.mean()\n    summ['std'] = df.std()\n    return summ\n","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:00.739576Z","iopub.execute_input":"2023-04-08T22:41:00.740996Z","iopub.status.idle":"2023-04-08T22:41:00.750754Z","shell.execute_reply.started":"2023-04-08T22:41:00.740939Z","shell.execute_reply":"2023-04-08T22:41:00.749058Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"summary('test',test)","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:00.752777Z","iopub.execute_input":"2023-04-08T22:41:00.753228Z","iopub.status.idle":"2023-04-08T22:41:00.791434Z","shell.execute_reply.started":"2023-04-08T22:41:00.753179Z","shell.execute_reply":"2023-04-08T22:41:00.790096Z"},"trusted":true},"execution_count":37,"outputs":[{"name":"stdout","text":"test shape: (276, 7)\n","output_type":"stream"},{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"          dtypes  null  unique      min   median       max        mean  \\\nid         int64     0     276  414.000  551.500   689.000  551.500000   \ngravity  float64     0      27    1.005    1.019     1.034    1.018330   \nph       float64     0      62    4.900    5.730     7.940    5.939312   \nosmo       int64     0      75  187.000  658.000  1236.000  654.891304   \ncond     float64     0      58    5.100   23.600    38.000   21.886594   \nurea       int64     0      71   64.000  282.000   620.000  280.985507   \ncalc     float64     0      74    0.170    3.230    14.340    4.076848   \n\n                std  \nid        79.818544  \ngravity    0.006569  \nph         0.607143  \nosmo     236.361932  \ncond       7.346069  \nurea     134.877856  \ncalc       3.088524  ","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>dtypes</th>\n      <th>null</th>\n      <th>unique</th>\n      <th>min</th>\n      <th>median</th>\n      <th>max</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>id</th>\n      <td>int64</td>\n      <td>0</td>\n      <td>276</td>\n      <td>414.000</td>\n      <td>551.500</td>\n      <td>689.000</td>\n      <td>551.500000</td>\n      <td>79.818544</td>\n    </tr>\n    <tr>\n      <th>gravity</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>27</td>\n      <td>1.005</td>\n      <td>1.019</td>\n      <td>1.034</td>\n      <td>1.018330</td>\n      <td>0.006569</td>\n    </tr>\n    <tr>\n      <th>ph</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>62</td>\n      <td>4.900</td>\n      <td>5.730</td>\n      <td>7.940</td>\n      <td>5.939312</td>\n      <td>0.607143</td>\n    </tr>\n    <tr>\n      <th>osmo</th>\n      <td>int64</td>\n      <td>0</td>\n      <td>75</td>\n      <td>187.000</td>\n      <td>658.000</td>\n      <td>1236.000</td>\n      <td>654.891304</td>\n      <td>236.361932</td>\n    </tr>\n    <tr>\n      <th>cond</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>58</td>\n      <td>5.100</td>\n      <td>23.600</td>\n      <td>38.000</td>\n      <td>21.886594</td>\n      <td>7.346069</td>\n    </tr>\n    <tr>\n      <th>urea</th>\n      <td>int64</td>\n      <td>0</td>\n      <td>71</td>\n      <td>64.000</td>\n      <td>282.000</td>\n      <td>620.000</td>\n      <td>280.985507</td>\n      <td>134.877856</td>\n    </tr>\n    <tr>\n      <th>calc</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>74</td>\n      <td>0.170</td>\n      <td>3.230</td>\n      <td>14.340</td>\n      <td>4.076848</td>\n      <td>3.088524</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Merging orignal dataset with train dataset","metadata":{}},{"cell_type":"code","source":"df_full = pd.concat([train, original])\nfor col in original.columns:\n    df_full[col] = df_full[col].astype('float64')","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:00.793252Z","iopub.execute_input":"2023-04-08T22:41:00.793617Z","iopub.status.idle":"2023-04-08T22:41:00.806373Z","shell.execute_reply.started":"2023-04-08T22:41:00.79358Z","shell.execute_reply":"2023-04-08T22:41:00.805329Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"summary('full',df_full)","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:00.80767Z","iopub.execute_input":"2023-04-08T22:41:00.808845Z","iopub.status.idle":"2023-04-08T22:41:00.849259Z","shell.execute_reply.started":"2023-04-08T22:41:00.808802Z","shell.execute_reply":"2023-04-08T22:41:00.848005Z"},"trusted":true},"execution_count":39,"outputs":[{"name":"stdout","text":"full shape: (493, 8)\n","output_type":"stream"},{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"          dtypes  null  unique      min   median      max        mean  \\\nid       float64    79     414    0.000  206.500   413.00  206.500000   \ngravity  float64     0      29    1.005    1.018     1.04    1.017929   \nph       float64     0      75    4.760    5.770     7.94    5.967160   \nosmo     float64     0      80  187.000  668.000  1236.00  645.344828   \ncond     float64     0      67    5.100   21.900    38.00   21.337931   \nurea     float64     0      74   10.000  272.000   620.00  276.693712   \ncalc     float64     0      86    0.170    3.160    14.34    4.118540   \ntarget   float64     0       2    0.000    0.000     1.00    0.442191   \n\n                std  \nid       119.655756  \ngravity    0.006761  \nph         0.655861  \nosmo     235.320087  \ncond       7.579484  \nurea     135.568157  \ncalc       3.221149  \ntarget     0.497151  ","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>dtypes</th>\n      <th>null</th>\n      <th>unique</th>\n      <th>min</th>\n      <th>median</th>\n      <th>max</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>id</th>\n      <td>float64</td>\n      <td>79</td>\n      <td>414</td>\n      <td>0.000</td>\n      <td>206.500</td>\n      <td>413.00</td>\n      <td>206.500000</td>\n      <td>119.655756</td>\n    </tr>\n    <tr>\n      <th>gravity</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>29</td>\n      <td>1.005</td>\n      <td>1.018</td>\n      <td>1.04</td>\n      <td>1.017929</td>\n      <td>0.006761</td>\n    </tr>\n    <tr>\n      <th>ph</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>75</td>\n      <td>4.760</td>\n      <td>5.770</td>\n      <td>7.94</td>\n      <td>5.967160</td>\n      <td>0.655861</td>\n    </tr>\n    <tr>\n      <th>osmo</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>80</td>\n      <td>187.000</td>\n      <td>668.000</td>\n      <td>1236.00</td>\n      <td>645.344828</td>\n      <td>235.320087</td>\n    </tr>\n    <tr>\n      <th>cond</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>67</td>\n      <td>5.100</td>\n      <td>21.900</td>\n      <td>38.00</td>\n      <td>21.337931</td>\n      <td>7.579484</td>\n    </tr>\n    <tr>\n      <th>urea</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>74</td>\n      <td>10.000</td>\n      <td>272.000</td>\n      <td>620.00</td>\n      <td>276.693712</td>\n      <td>135.568157</td>\n    </tr>\n    <tr>\n      <th>calc</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>86</td>\n      <td>0.170</td>\n      <td>3.160</td>\n      <td>14.34</td>\n      <td>4.118540</td>\n      <td>3.221149</td>\n    </tr>\n    <tr>\n      <th>target</th>\n      <td>float64</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>1.00</td>\n      <td>0.442191</td>\n      <td>0.497151</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"print(f' Row => {df_full.shape[0]}, Columns => {df_full.shape[1]}')","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:00.850809Z","iopub.execute_input":"2023-04-08T22:41:00.85158Z","iopub.status.idle":"2023-04-08T22:41:00.862159Z","shell.execute_reply.started":"2023-04-08T22:41:00.851538Z","shell.execute_reply":"2023-04-08T22:41:00.860773Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stdout","text":" Row => 493, Columns => 8\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Density of the various features in the train data\n","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(14,10))\nfor i in range(1, len(df_full.columns)):\n    plt.subplot(4,4,i)\n    sns.kdeplot(x=train[train.columns[i]], label='Full Dataset')\n    plt.tight_layout()","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:00.863676Z","iopub.execute_input":"2023-04-08T22:41:00.864035Z","iopub.status.idle":"2023-04-08T22:41:02.559872Z","shell.execute_reply.started":"2023-04-08T22:41:00.863991Z","shell.execute_reply":"2023-04-08T22:41:02.5585Z"},"trusted":true},"execution_count":41,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1400x1000 with 7 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"## Correlation of features within dataset","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(12,8))\nmask = np.triu(np.ones_like(df_full.corr()))\nsns.heatmap(train.corr(), mask=mask, annot=True, fmt='.2f')","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:02.561248Z","iopub.execute_input":"2023-04-08T22:41:02.561583Z","iopub.status.idle":"2023-04-08T22:41:03.074745Z","shell.execute_reply.started":"2023-04-08T22:41:02.561551Z","shell.execute_reply":"2023-04-08T22:41:03.073798Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x800 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"df_full.columns","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:03.076037Z","iopub.execute_input":"2023-04-08T22:41:03.076597Z","iopub.status.idle":"2023-04-08T22:41:03.08402Z","shell.execute_reply.started":"2023-04-08T22:41:03.076563Z","shell.execute_reply":"2023-04-08T22:41:03.082811Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"Index(['id', 'gravity', 'ph', 'osmo', 'cond', 'urea', 'calc', 'target'], dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## Featuring Engineering","metadata":{}},{"cell_type":"code","source":"features = ['gravity',\n            'ph', \n            'osmo', \n            'cond',\n            'urea', \n            'calc']","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:03.085543Z","iopub.execute_input":"2023-04-08T22:41:03.085861Z","iopub.status.idle":"2023-04-08T22:41:03.093124Z","shell.execute_reply.started":"2023-04-08T22:41:03.08583Z","shell.execute_reply":"2023-04-08T22:41:03.092182Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"code","source":"X = df_full[features].copy()\ny = df_full['target'].copy()","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:03.096723Z","iopub.execute_input":"2023-04-08T22:41:03.097337Z","iopub.status.idle":"2023-04-08T22:41:03.10877Z","shell.execute_reply.started":"2023-04-08T22:41:03.0973Z","shell.execute_reply":"2023-04-08T22:41:03.107505Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"markdown","source":"## Splitting the datasets","metadata":{}},{"cell_type":"code","source":"X_train, X_val, y_train, y_val = train_test_split(X,y,test_size=0.2, random_state=52)","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:03.110076Z","iopub.execute_input":"2023-04-08T22:41:03.110442Z","iopub.status.idle":"2023-04-08T22:41:03.120645Z","shell.execute_reply.started":"2023-04-08T22:41:03.110408Z","shell.execute_reply":"2023-04-08T22:41:03.11945Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"code","source":"model_dict = {\n    'Light GBM': LGBMClassifier(),\n    'XG Boost' : XGBClassifier(),\n    'Cat Boost' : CatBoostClassifier()\n}","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:03.122335Z","iopub.execute_input":"2023-04-08T22:41:03.122674Z","iopub.status.idle":"2023-04-08T22:41:03.13183Z","shell.execute_reply.started":"2023-04-08T22:41:03.122641Z","shell.execute_reply":"2023-04-08T22:41:03.130702Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"markdown","source":"## Model Evluation ","metadata":{}},{"cell_type":"code","source":"def evaluation(model_str, y_pred, y_pred_train):\n    results = {\n        'model': model_str,\n        'rmsleval': mean_squared_log_error(y_val, y_pred, squared=True),\n        'rmsle_train': mean_squared_log_error(y_train, y_pred_train, squared=True)\n    }\n    return results","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:03.13361Z","iopub.execute_input":"2023-04-08T22:41:03.134069Z","iopub.status.idle":"2023-04-08T22:41:03.142489Z","shell.execute_reply.started":"2023-04-08T22:41:03.134021Z","shell.execute_reply":"2023-04-08T22:41:03.141052Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"markdown","source":"## Traning and Fitting the models","metadata":{}},{"cell_type":"code","source":"result_list = []\nfor model in model_dict:\n    model_dict[model].fit(X_train, y_train,verbose=True)\n    y_pred = model_dict[model].predict(X_val)\n    y_pred_train = model_dict[model].predict(X_train)\n    result = evaluation(model, y_pred, y_pred_train)\n    result_list.append(result)\ndf_eval = pd.DataFrame(result_list)\ndf_eval","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:03.144232Z","iopub.execute_input":"2023-04-08T22:41:03.144595Z","iopub.status.idle":"2023-04-08T22:41:04.531257Z","shell.execute_reply.started":"2023-04-08T22:41:03.14456Z","shell.execute_reply":"2023-04-08T22:41:04.530252Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stdout","text":"Learning rate set to 0.006922\n0:\tlearn: 0.6904713\ttotal: 1.52ms\tremaining: 1.52s\n1:\tlearn: 0.6874771\ttotal: 2.49ms\tremaining: 1.24s\n2:\tlearn: 0.6850308\ttotal: 3.34ms\tremaining: 1.11s\n3:\tlearn: 0.6825314\ttotal: 4.2ms\tremaining: 1.04s\n4:\tlearn: 0.6797638\ttotal: 5.04ms\tremaining: 1s\n5:\tlearn: 0.6771521\ttotal: 5.91ms\tremaining: 979ms\n6:\tlearn: 0.6747332\ttotal: 6.76ms\tremaining: 960ms\n7:\tlearn: 0.6719011\ttotal: 7.55ms\tremaining: 936ms\n8:\tlearn: 0.6693580\ttotal: 8.36ms\tremaining: 921ms\n9:\tlearn: 0.6670242\ttotal: 9.15ms\tremaining: 906ms\n10:\tlearn: 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0.2606087\ttotal: 746ms\tremaining: 1.49ms\n998:\tlearn: 0.2603436\ttotal: 747ms\tremaining: 747us\n999:\tlearn: 0.2601700\ttotal: 747ms\tremaining: 0us\n","output_type":"stream"},{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"       model  rmsleval  rmsle_train\n0  Light GBM  0.126180     0.004878\n1   XG Boost  0.126180     0.002439\n2  Cat Boost  0.121327     0.035363","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>model</th>\n      <th>rmsleval</th>\n      <th>rmsle_train</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Light GBM</td>\n      <td>0.126180</td>\n      <td>0.004878</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>XG Boost</td>\n      <td>0.126180</td>\n      <td>0.002439</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Cat Boost</td>\n      <td>0.121327</td>\n      <td>0.035363</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"X_test = test[features].copy()","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:04.532606Z","iopub.execute_input":"2023-04-08T22:41:04.532904Z","iopub.status.idle":"2023-04-08T22:41:04.539026Z","shell.execute_reply.started":"2023-04-08T22:41:04.532875Z","shell.execute_reply":"2023-04-08T22:41:04.537839Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"markdown","source":"## Defining the hyper parameters","metadata":{}},{"cell_type":"code","source":"   xgb_params={ 'seed': 42,\n    'objective': 'binary:logistic',\n    'eval_metric': 'auc',\n    'tree_method' : 'exact',\n    'n_jobs' : -1,\n    'max_depth' : 2,\n    'eta' : .01,\n    'n_estimators' : 100}","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:04.540523Z","iopub.execute_input":"2023-04-08T22:41:04.540855Z","iopub.status.idle":"2023-04-08T22:41:04.549207Z","shell.execute_reply.started":"2023-04-08T22:41:04.540817Z","shell.execute_reply":"2023-04-08T22:41:04.548023Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"markdown","source":"## Executing the model on test data and generating the submission file","metadata":{}},{"cell_type":"code","source":"model = XGBClassifier(**xgb_params)\nmodel.fit(X, y)\ntarget = model.predict(X_test)\ndf_test = pd.DataFrame(data={'id': test['id'],'target': target})\ndf_test.to_csv('submission.csv', index=False)\ndf_test","metadata":{"execution":{"iopub.status.busy":"2023-04-08T22:41:04.550505Z","iopub.execute_input":"2023-04-08T22:41:04.551046Z","iopub.status.idle":"2023-04-08T22:41:04.792512Z","shell.execute_reply.started":"2023-04-08T22:41:04.551008Z","shell.execute_reply":"2023-04-08T22:41:04.791618Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"      id  target\n0    414       0\n1    415       0\n2    416       1\n3    417       0\n4    418       0\n..   ...     ...\n271  685       1\n272  686       0\n273  687       1\n274  688       0\n275  689       0\n\n[276 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>id</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>414</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>415</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>416</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>417</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>418</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>271</th>\n      <td>685</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>272</th>\n      <td>686</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>273</th>\n      <td>687</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>274</th>\n      <td>688</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>275</th>\n      <td>689</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>276 rows × 2 columns</p>\n</div>"},"metadata":{}}]}]}