Gradient Boosting Classification.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"#Importing librairies\n\n%matplotlib notebook\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# import machine learning algorithms\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv(\"../input/ee-769-assignment1/train.csv\")\ntest = pd.read_csv(\"../input/ee-769-assignment1/test.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# delete columns that are not used as features for training and prediction\ncolumns_to_drop1 = [\"Attrition\", \"Department\", \"EducationField\", \"JobRole\"]\ncolumns_to_drop2 = [\"Department\", \"EducationField\", \"JobRole\"]\nX_train = train.drop(labels=columns_to_drop1, axis=1)\nX_test = test.drop(labels=columns_to_drop2, axis=1)\ny_train = train[\"Attrition\"]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# convert objects to numbers by pandas.get_dummies\nX_train = pd.get_dummies(X_train, columns=[\"Gender\", \"BusinessTravel\", \"OverTime\", \"MaritalStatus\"])\nX_test = pd.get_dummies(X_test, columns=[\"Gender\", \"BusinessTravel\", \"OverTime\", \"MaritalStatus\"])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# transform data\nscaler = MinMaxScaler()\nX_train_scale = scaler.fit_transform(X_train)\nX_test_scale = scaler.transform(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# split training feature and target sets into training and validation subsets\nX_train_sub, X_validation_sub, y_train_sub, y_validation_sub = train_test_split(X_train_scale, y_train, random_state=0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# train with Gradient Boosting algorithm\n# compute the accuracy scores on train and validation sets when training with different learning rates\n\nlearning_rates = [0.05, 0.1, 0.25, 0.5, 0.75, 1]\nfor learning_rate in learning_rates:\n    gb = GradientBoostingClassifier(n_estimators=20, learning_rate = learning_rate, max_features=2, max_depth = 2, random_state = 0)\n    gb.fit(X_train_sub, y_train_sub)\n    print(\"Learning rate: \", learning_rate)\n    print(\"Accuracy score (training): {0:.3f}\".format(gb.score(X_train_sub, y_train_sub)))\n    print(\"Accuracy score (validation): {0:.3f}\".format(gb.score(X_validation_sub, y_validation_sub)))\n    print()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Output confusion matrix and classification report of Gradient Boosting algorithm on validation set\n\ngb = GradientBoostingClassifier(n_estimators=20, learning_rate = 0.5, max_features=2, max_depth = 2, random_state = 0)\ngb.fit(X_train_sub, y_train_sub)\npredictions = gb.predict(X_validation_sub)\n\nprint(\"Confusion Matrix:\")\nprint(confusion_matrix(y_validation_sub, predictions))\nprint()\nprint(\"Classification Report\")\nprint(classification_report(y_validation_sub, predictions))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ROC curve and Area-Under-Curve (AUC)\n\ny_scores_gb = gb.decision_function(X_validation_sub)\nfpr_gb, tpr_gb, _ = roc_curve(y_validation_sub, y_scores_gb)\nroc_auc_gb = auc(fpr_gb, tpr_gb)\n\nprint(\"Area under ROC curve = {:0.2f}\".format(roc_auc_gb))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"gb = GradientBoostingClassifier(n_estimators=20, learning_rate = 0.25, max_features=4, max_depth = 2, random_state = 0)\ngb.fit(X_train, y_train)\ny_pred = gb.predict(X_test)\nres = pd.DataFrame({\"ID\": test[\"ID\"], \"Attrition\": y_pred})","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with open('output.csv', 'w') as csv_file:\n    res.to_csv(path_or_buf=csv_file, index = False)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}