diff --git a/traditional_ML_algorithms/Cat Boost Regressor/ground-water-level.ipynb b/traditional_ML_algorithms/Cat Boost Regressor/ground-water-level.ipynb new file mode 100644 index 0000000..212fa8e --- /dev/null +++ b/traditional_ML_algorithms/Cat Boost Regressor/ground-water-level.ipynb @@ -0,0 +1,1709 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "064cf93b", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", + "execution": { + "iopub.execute_input": "2022-06-25T09:36:03.577996Z", + "iopub.status.busy": "2022-06-25T09:36:03.577283Z", + "iopub.status.idle": "2022-06-25T09:36:03.618823Z", + "shell.execute_reply": "2022-06-25T09:36:03.617727Z" + }, + "papermill": { + "duration": 0.051069, + "end_time": "2022-06-25T09:36:03.621189", + "exception": false, + "start_time": "2022-06-25T09:36:03.570120", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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STATE2010 level(m)2011 Level (m)2012 Level (m)2013 Level (m)2014 Level (m)2015 Level (m)2016 Level (m)2017 Level (m)2018 Level (m)2019 Level (m)2020 Level (m)
0UTTARAKHAND14.3718.8119.5419.0316.8514.4813.9421.2426.1118.0919.51
1ASSAM4.144.624.034.134.194.724.394.083.743.773.78
2Ladakh-----------
3GOA6.76.656.846.76.346.424.925.935.124.885.46
4LAKSHDWEEP-----------
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" + ], + "text/plain": [ + " STATE 2010 level(m) 2011 Level (m) 2012 Level (m) 2013 Level (m) \\\n", + "0 UTTARAKHAND 14.37 18.81 19.54 19.03 \n", + "1 ASSAM 4.14 4.62 4.03 4.13 \n", + "2 Ladakh - - - - \n", + "3 GOA 6.7 6.65 6.84 6.7 \n", + "4 LAKSHDWEEP - - - - \n", + "\n", + " 2014 Level (m) 2015 Level (m) 2016 Level (m) 2017 Level (m) 2018 Level (m) \\\n", + "0 16.85 14.48 13.94 21.24 26.11 \n", + "1 4.19 4.72 4.39 4.08 3.74 \n", + "2 - - - - - \n", + "3 6.34 6.42 4.92 5.93 5.12 \n", + "4 - - - - - \n", + "\n", + " 2019 Level (m) 2020 Level (m) \n", + "0 18.09 19.51 \n", + "1 3.77 3.78 \n", + "2 - - \n", + "3 4.88 5.46 \n", + "4 - - " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "df = pd.read_csv('../input/ground-water/groundwater_level.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3a05628d", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:03.632554Z", + "iopub.status.busy": "2022-06-25T09:36:03.632156Z", + "iopub.status.idle": "2022-06-25T09:36:03.655351Z", + "shell.execute_reply": "2022-06-25T09:36:03.654131Z" + }, + "papermill": { + "duration": 0.032139, + "end_time": "2022-06-25T09:36:03.658177", + "exception": false, + "start_time": "2022-06-25T09:36:03.626038", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 37 entries, 0 to 36\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 STATE 37 non-null object\n", + " 1 2010 level(m) 37 non-null object\n", + " 2 2011 Level (m) 37 non-null object\n", + " 3 2012 Level (m) 37 non-null object\n", + " 4 2013 Level (m) 37 non-null object\n", + " 5 2014 Level (m) 37 non-null object\n", + " 6 2015 Level (m) 37 non-null object\n", + " 7 2016 Level (m) 37 non-null object\n", + " 8 2017 Level (m) 37 non-null object\n", + " 9 2018 Level (m) 37 non-null object\n", + " 10 2019 Level (m) 37 non-null object\n", + " 11 2020 Level (m) 37 non-null object\n", + "dtypes: object(12)\n", + "memory usage: 3.6+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9ab05400", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:03.669932Z", + "iopub.status.busy": "2022-06-25T09:36:03.669548Z", + "iopub.status.idle": "2022-06-25T09:36:03.675767Z", + "shell.execute_reply": "2022-06-25T09:36:03.674488Z" + }, + "papermill": { + "duration": 0.015194, + "end_time": "2022-06-25T09:36:03.678293", + "exception": false, + "start_time": "2022-06-25T09:36:03.663099", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df=df.replace(\"-\",np.nan)\n", + "#df = df.drop(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7f9bb5ef", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:03.689914Z", + "iopub.status.busy": "2022-06-25T09:36:03.689499Z", + "iopub.status.idle": "2022-06-25T09:36:03.705234Z", + "shell.execute_reply": "2022-06-25T09:36:03.703620Z" + }, + "papermill": { + "duration": 0.024668, + "end_time": "2022-06-25T09:36:03.707904", + "exception": false, + "start_time": "2022-06-25T09:36:03.683236", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 37 entries, 0 to 36\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 STATE 37 non-null object\n", + " 1 2010 level(m) 31 non-null object\n", + " 2 2011 Level (m) 30 non-null object\n", + " 3 2012 Level (m) 32 non-null object\n", + " 4 2013 Level (m) 32 non-null object\n", + " 5 2014 Level (m) 32 non-null object\n", + " 6 2015 Level (m) 32 non-null object\n", + " 7 2016 Level (m) 32 non-null object\n", + " 8 2017 Level (m) 31 non-null object\n", + " 9 2018 Level (m) 31 non-null object\n", + " 10 2019 Level (m) 31 non-null object\n", + " 11 2020 Level (m) 30 non-null object\n", + "dtypes: object(12)\n", + "memory usage: 3.6+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "290b7663", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:03.719165Z", + "iopub.status.busy": "2022-06-25T09:36:03.718796Z", + "iopub.status.idle": "2022-06-25T09:36:03.729051Z", + "shell.execute_reply": "2022-06-25T09:36:03.728297Z" + }, + "papermill": { + "duration": 0.018255, + "end_time": "2022-06-25T09:36:03.731088", + "exception": false, + "start_time": "2022-06-25T09:36:03.712833", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "STATE 0\n", + "2010 level(m) 6\n", + "2011 Level (m) 7\n", + "2012 Level (m) 5\n", + "2013 Level (m) 5\n", + "2014 Level (m) 5\n", + "2015 Level (m) 5\n", + "2016 Level (m) 5\n", + "2017 Level (m) 6\n", + "2018 Level (m) 6\n", + "2019 Level (m) 6\n", + "2020 Level (m) 7\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0f652c27", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:03.742949Z", + "iopub.status.busy": "2022-06-25T09:36:03.742568Z", + "iopub.status.idle": "2022-06-25T09:36:03.759945Z", + "shell.execute_reply": "2022-06-25T09:36:03.758733Z" + }, + "papermill": { + "duration": 0.026267, + "end_time": "2022-06-25T09:36:03.762356", + "exception": false, + "start_time": "2022-06-25T09:36:03.736089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df.dropna()\n", + "df['2010 level(m)'] = df['2010 level(m)'].astype(float)\n", + "df['2011 Level (m)'] = df['2011 Level (m)'].astype(float)\n", + "df['2012 Level (m)'] = df['2012 Level (m)'].astype(float)\n", + "df['2013 Level (m)'] = df['2013 Level (m)'].astype(float)\n", + "df['2014 Level (m)'] = df['2014 Level (m)'].astype(float)\n", + "df['2015 Level (m)'] = df['2015 Level (m)'].astype(float)\n", + "df['2016 Level (m)'] = df['2016 Level (m)'].astype(float)\n", + "df['2017 Level (m)'] = df['2017 Level (m)'].astype(float)\n", + "df['2018 Level (m)'] = df['2018 Level (m)'].astype(float)\n", + "df['2019 Level (m)'] = df['2019 Level (m)'].astype(float)\n", + "df['2020 Level (m)'] = df['2020 Level (m)'].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "68c6d1e2", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:03.773799Z", + "iopub.status.busy": "2022-06-25T09:36:03.773413Z", + "iopub.status.idle": "2022-06-25T09:36:03.788490Z", + "shell.execute_reply": "2022-06-25T09:36:03.787339Z" + }, + "papermill": { + "duration": 0.024755, + "end_time": "2022-06-25T09:36:03.792093", + "exception": false, + "start_time": "2022-06-25T09:36:03.767338", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 37 entries, 0 to 36\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 STATE 37 non-null object \n", + " 1 2010 level(m) 31 non-null float64\n", + " 2 2011 Level (m) 30 non-null float64\n", + " 3 2012 Level (m) 32 non-null float64\n", + " 4 2013 Level (m) 32 non-null float64\n", + " 5 2014 Level (m) 32 non-null float64\n", + " 6 2015 Level (m) 32 non-null float64\n", + " 7 2016 Level (m) 32 non-null float64\n", + " 8 2017 Level (m) 31 non-null float64\n", + " 9 2018 Level (m) 31 non-null float64\n", + " 10 2019 Level (m) 31 non-null float64\n", + " 11 2020 Level (m) 30 non-null float64\n", + "dtypes: float64(11), object(1)\n", + "memory usage: 3.6+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3170197e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:03.805191Z", + "iopub.status.busy": "2022-06-25T09:36:03.804837Z", + "iopub.status.idle": "2022-06-25T09:36:04.948634Z", + "shell.execute_reply": "2022-06-25T09:36:04.947418Z" + }, + "papermill": { + "duration": 1.153196, + "end_time": "2022-06-25T09:36:04.951133", + "exception": false, + "start_time": "2022-06-25T09:36:03.797937", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((29, 11), (29,), (8, 11), (8,))" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X = df.drop('STATE', axis=1)\n", + "y = df['STATE']\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "X_train.shape, y_train.shape, X_test.shape, y_test.shape," + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "411f204f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:04.962774Z", + "iopub.status.busy": "2022-06-25T09:36:04.962384Z", + "iopub.status.idle": "2022-06-25T09:36:04.967114Z", + "shell.execute_reply": "2022-06-25T09:36:04.966337Z" + }, + "papermill": { + "duration": 0.012866, + "end_time": "2022-06-25T09:36:04.969019", + "exception": false, + "start_time": "2022-06-25T09:36:04.956153", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def fitAndPredict(model):\n", + " model.fit(X_train, y_train)\n", + " prediction = model.predict(X_test)\n", + " return accuracy_score(y_test, prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5c05bab3", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:04.981888Z", + "iopub.status.busy": "2022-06-25T09:36:04.981308Z", + "iopub.status.idle": "2022-06-25T09:36:06.422228Z", + "shell.execute_reply": "2022-06-25T09:36:06.421060Z" + }, + "papermill": { + "duration": 1.45071, + "end_time": "2022-06-25T09:36:06.425058", + "exception": false, + "start_time": "2022-06-25T09:36:04.974348", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import roc_auc_score\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LinearRegression\n", + "from scipy.stats import boxcox\n", + "from xgboost import XGBClassifier \n", + "from catboost import CatBoostClassifier\n", + "from lightgbm import LGBMClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3830584b", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:06.438043Z", + "iopub.status.busy": "2022-06-25T09:36:06.437620Z", + "iopub.status.idle": "2022-06-25T09:36:10.021580Z", + "shell.execute_reply": "2022-06-25T09:36:10.020376Z" + }, + "papermill": { + "duration": 3.593746, + "end_time": "2022-06-25T09:36:10.024359", + "exception": false, + "start_time": "2022-06-25T09:36:06.430613", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate set to 0.066055\n", + "0:\tlearn: 3.3491953\ttotal: 60.8ms\tremaining: 1m\n", + "1:\tlearn: 3.3309464\ttotal: 64.3ms\tremaining: 32.1s\n", + "2:\tlearn: 3.3152574\ttotal: 67.6ms\tremaining: 22.5s\n", + "3:\tlearn: 3.2973248\ttotal: 71.5ms\tremaining: 17.8s\n", + "4:\tlearn: 3.2801427\ttotal: 74.8ms\tremaining: 14.9s\n", + "5:\tlearn: 3.2628097\ttotal: 78ms\tremaining: 12.9s\n", + "6:\tlearn: 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"execute_result" + } + ], + "source": [ + "model =CatBoostClassifier(random_state=42)\n", + "fitAndPredict(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e672b591", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:10.046595Z", + "iopub.status.busy": "2022-06-25T09:36:10.045792Z", + "iopub.status.idle": "2022-06-25T09:36:10.055197Z", + "shell.execute_reply": "2022-06-25T09:36:10.053600Z" + }, + "papermill": { + "duration": 0.023323, + "end_time": "2022-06-25T09:36:10.057916", + "exception": false, + "start_time": "2022-06-25T09:36:10.034593", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "input = [[5056,4556,3678,23424,4625623,1313,45356,55,56466,4535,46]]\n", + "output = model.predict(input)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e1998034", + "metadata": { + "execution": { + "iopub.execute_input": "2022-06-25T09:36:10.077801Z", + "iopub.status.busy": "2022-06-25T09:36:10.077176Z", + "iopub.status.idle": "2022-06-25T09:36:10.083429Z", + "shell.execute_reply": "2022-06-25T09:36:10.081768Z" + }, + "papermill": { + "duration": 0.020297, + "end_time": "2022-06-25T09:36:10.086830", + "exception": false, + "start_time": "2022-06-25T09:36:10.066533", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted State ['RAJASTHAN']\n" + ] + } + ], + "source": [ + "print(\"Predicted State\",output[0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + }, + "papermill": { + "default_parameters": {}, + "duration": 17.686379, + "end_time": "2022-06-25T09:36:10.917912", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2022-06-25T09:35:53.231533", + "version": "2.3.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/traditional_ML_algorithms/Cat Boost Regressor/groundwater_level.csv b/traditional_ML_algorithms/Cat Boost Regressor/groundwater_level.csv new file mode 100644 index 0000000..f3dc3ac --- /dev/null +++ b/traditional_ML_algorithms/Cat Boost Regressor/groundwater_level.csv @@ -0,0 +1,38 @@ +STATE,2010 level(m),2011 Level (m),2012 Level (m),2013 Level (m),2014 Level (m),2015 Level (m),2016 Level (m),2017 Level (m),2018 Level (m),2019 Level (m),2020 Level (m) +UTTARAKHAND,14.37,18.81,19.54,19.03,16.85,14.48,13.94,21.24,26.11,18.09,19.51 +ASSAM,4.14,4.62,4.03,4.13,4.19,4.72,4.39,4.08,3.74,3.77,3.78 +Ladakh,-,-,-,-,-,-,-,-,-,-,- +GOA,6.7,6.65,6.84,6.7,6.34,6.42,4.92,5.93,5.12,4.88,5.46 +LAKSHDWEEP,-,-,-,-,-,-,-,-,-,-,- +PONDICHERRY,5.14,6.13,3.48,14.68,15.72,11.43,13.98,2.52,13.73,11.49,11.3 +DELHI,14.91,15.81,16.9,16.61,16.85,17.62,19.71,18.51,16.65,16.27,14.88 +HIMACHAL PRADESH,7.35,7.41,6.89,6.26,5.82,7.46,7.04,7.14,6.88,5.51,6.54 +TRIPURA,4.59,4.8,4.96,7.08,4.73,4.6,3.75,4.46,5.59,5.62,5.44 +ANDHRA PRADESH,8.66,11.31,12.06,11.96,14.45,13.15,16.52,12.34,10.9,13.77,8.82 +TAMILNADU,6.74,4.5,8.41,9.95,11.52,12.62,10.55,9.65,10.9,7.18,5.19 +GUJARAT,13.9,16.35,15.83,16.99,16.87,15.7,16.4,18.51,17.51,13.16,12.72 +SIKKIM,-,-,-,-,-,-,-,-,-,-,- +BIHAR,6.06,5.71,5.58,5.39,5.39,6,5.56,5.86,6.27,4.19,4.22 +WEST BENGAL,7.65,7.2,8.18,7.83,7.67,7.78,7.39,7.36,8,6.72,6.85 +KARNATAKA,8.21,9.66,12.7,9.57,9.22,8.6,8.92,9.33,12.46,4.64,6.27 +ODISHA,6.55,7.12,6.84,6.62,5.24,5.76,5.93,5.37,4.5,3.67,3.91 +PUNJAB,13.66,12.78,15.47,14.9,15.48,15.92,17.41,18.08,17.32,16.99,148.09 +CHHATTISGARH,7.53,6.86,5.4,6.77,7.91,7.97,8.69,7.79,8.23,5.12,5.73 +JAMMU & KASHMIR,3.14,4.13,3.4,3.07,2.91,4.33,3.56,4.77,3.82,2.66,2.14 +DAMAN & DIU,5.59,5.6,6.32,7.18,8.04,7,6.34,6.77,5.76,4.25,4.09 +RAJASTHAN,32.03,31.44,31.19,31.65,25.9,25.53,25.18,26.59,27.37,26.05,26.02 +DADRA AND NAGAR HAV,6.2,6.77,4.68,6.86,8.44,8.87,8.45,8.17,10.31,4.1,4.51 +TELANGANA,3.98,4.78,4.45,4.25,7.26,10.81,9.08,11.19,13.21,8.95,5.61 +UTTAR PRADESH,7.32,7.7,7.34,7.14,7.09,8.53,8.25,8.68,17.97,15.06,4.11 +MEGHALAYA,2.91,2.99,2.86,3.73,2.68,2.78,2.59,2.84,3.1,2.66,2.87 +KERALA,5.07,8.5,8.02,3.97,6.79,7.37,7.67,7.35,7.72,10.12,6.08 +HARYANA,12.93,16.24,15.44,15.59,16.31,15.31,16.33,17.16,16.86,15.4,17.08 +ANDAMAN & NICOBAR,-,-,2.17,3.36,2.72,3.57,2.93,1.55,2.04,2.34,- +JHARKHAND,8.87,8.18,7.67,7.09,6.11,7.83,8.06,7.6,8.09,4.87,5.37 +MAHARASHTRA,7.88,8.52,9.2,7.87,8.6,10.21,9.2,8.98,10.52,5.86,5.88 +NAGALAND,5.53,-,5.64,8.17,4.61,5.12,2.84,-,-,-,- +MADHYA PRADESH,11.91,11.17,10.93,9.8,10.5,10.75,10.84,11.23,11.02,7.09,7.96 +ARUNACHAL PRADESH,5.95,5.84,4.69,6.25,6.56,6.33,7.03,7.06,6.04,5.71,5.34 +MIZORAM,-,-,-,-,-,-,-,-,-,-,- +CHANDIGARH,19.3,16.94,23.31,20.78,17.81,15.72,26.4,31.75,22.27,25.46,21.97 +MANIPUR,-,-,-,-,-,-,-,-,-,-,- diff --git a/traditional_ML_algorithms/Cat Boost Regressor/readme.md b/traditional_ML_algorithms/Cat Boost Regressor/readme.md new file mode 100644 index 0000000..8e4e54a --- /dev/null +++ b/traditional_ML_algorithms/Cat Boost Regressor/readme.md @@ -0,0 +1,9 @@ +# CatBoost Regressor + +Packages used:
+`Numpy` : 1.19.5
+`Pandas` : 1.2.4
+ +CatBoost builds upon the theory of decision trees and gradient boosting. The main idea of boosting is to sequentially combine many weak models (a model performing slightly better than random chance) and thus through greedy search create a strong competitive predictive model. + +Documentation: [CatBoost Regressor](https://towardsdatascience.com/catboost-regression-in-6-minutes-3487f3e5b329)