machine-lerning_project_simplilearn

##Machine learning course end project at simplilearn import numpy as np
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings("ignore") df=pd.read_csv("HR_comma_sep_1.csv") df.head() df.info() df.isnull().sum().sum() df.describe(exclude=np.number) hp = df.drop(columns = ['Department', 'salary']) sns.heatmap(hp.corr(), xticklabels=hp.columns, yticklabels=hp.columns) sns.displot(df.satisfaction_level, kde=True) sns.displot(df.last_evaluation, kde=True) sns.displot(df.average_montly_hours, kde=True) sns.barplot(x= df.left,y=df.number_project, hue=df.number_project) df_k = df.filter(['satisfaction_level', 'last_evaluation', 'left'],axis=1) df_k.info() df_k['satisfaction_level'].unique() len(df_k['satisfaction_level'].unique()) df_k['last_evaluation'].unique() len(df_k['last_evaluation'].unique()) df_k['left'].unique() array([1, 0], dtype=int64) len(df_k['left'].unique()) X = df_k y = df_k['left'] cols = X.columns from sklearn.preprocessing import MinMaxScaler ms = MinMaxScaler() X = ms.fit_transform(X) X = pd.DataFrame(X, columns=[cols]) X.head() from sklearn.cluster import KMeans kmeans = KMeans(n_clusters=3, random_state=1) kmeans.fit(X) kmeans.inertia_ kmeans.cluster_centers_ df_k['cluster'] = kmeans.labels_ labels = kmeans.labels_ correct_labels = sum(y == labels) print("Result: %d out of %d samples were correctly labeled." % (correct_labels, y.size)) print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size))) df1= df.drop(['salary', 'Department'], axis='columns') df1.info() c = df.filter(['salary', 'Department'],axis=1) c c = pd.get_dummies(c, columns=['salary', 'Department']) c.info() df1= df1.join(c, lsuffix='_caller', rsuffix='other') df1 df1.info() from sklearn.model_selection import train_test_split y = df1['left'] X = df1.drop(['left'],axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=123) print("Before OverSampling, counts of label '1': {}".format(sum(y_train == 1))) print("Before OverSampling, counts of label '0': {} \n".format(sum(y_train == 0))) Before OverSampling, counts of label '1': 2862 Before OverSampling, counts of label '0': 9137 from imblearn.over_sampling import SMOTE X_train.info() sm = SMOTE(random_state = 2) X_train_res, y_train_res = sm.fit_resample(X_train, y_train.ravel()) print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape)) print('After OverSampling, the shape of train_y: {} \n'.format(y_train_res.shape)) print("After OverSampling, counts of label '1': {}".format(sum(y_train_res == 1))) print("After OverSampling, counts of label '0': {}".format(sum(y_train_res == 0))) from sklearn.model_selection import KFold, StratifiedKFold, cross_val_score df_kf = df.copy() from sklearn.preprocessing import LabelEncoder Le = LabelEncoder() df_kf['salary'] = Le.fit_transform(df_kf['salary']) df_kf['Department'] = Le.fit_transform(df_kf['Department']) df_kf.info() X = df_kf.drop('left', axis = 1) y = df_kf['left'] kf =KFold(n_splits=5, shuffle=True, random_state=42) cnt = 1 for train_index, test_index in kf.split(X, y): print(f'Fold:{cnt}, Train set: {len(train_index)}, Test set:{len(test_index)}') cnt += 1 from sklearn import linear_model for score in ["roc_auc", "f1", "precision", "recall", "accuracy"]: cvs = cross_val_score(linear_model.LogisticRegression(random_state= 42), X, y, cv=kf, scoring="neg_mean_squared_error").mean() print(score + " : "+ str(cvs)) from sklearn import ensemble for score in ["roc_auc", "f1", "precision", "recall", "accuracy"]: cvs = cross_val_score(ensemble.RandomForestRegressor(random_state= 42), X, y, cv=kf, scoring="neg_mean_squared_error").mean() print(score + " : "+ str(cvs)) for score in ["roc_auc", "f1", "precision", "recall", "accuracy"]: cvs = cross_val_score(ensemble.GradientBoostingClassifier(random_state= 42), X, y, cv=kf, scoring="neg_mean_squared_error").mean() print(score + " : "+ str(cvs)) df_roc = df.copy() Le = LabelEncoder() df_roc['salary'] = Le.fit_transform(df_roc['salary']) df_roc['Department'] = Le.fit_transform(df_roc['Department']) X = df_roc.drop('left', axis = 1) y = df_roc['left'] X_train, X_test, y_train, y_test = train_test_split( X, y, test_size = 0.2, random_state=42) logr = linear_model.LogisticRegression() logr.fit(X_train,y_train) LogisticRegression() y_pred_logr = logr.predict(X_test) from sklearn.metrics import roc_auc_score roc_auc_logr = roc_auc_score(y_test, y_pred_logr) roc_auc_logr from sklearn.metrics import RocCurveDisplay RocCurveDisplay.from_estimator(logr, X_test, y_test) plt.show() from sklearn.metrics import classification_report print(classification_report(y_test, y_pred_logr)) from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay cm = confusion_matrix(y_test, y_pred_logr) disp = ConfusionMatrixDisplay(confusion_matrix=cm) disp.plot() plt.show() from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier() rfc.fit(X_train,y_train) RandomForestClassifier() y_pred_rfc = rfc.predict(X_test) roc_auc_rfc = roc_auc_score(y_test, y_pred_rfc) roc_auc_rfc RocCurveDisplay.from_estimator(rfc, X_test, y_test) plt.show() print(classification_report(y_test, y_pred_rfc)) cm2 = confusion_matrix(y_test, y_pred_rfc) disp = ConfusionMatrixDisplay(confusion_matrix=cm2) disp.plot() plt.show() from sklearn.ensemble import GradientBoostingClassifier gbc = GradientBoostingClassifier() gbc.fit(X_train,y_train) y_pred_gbc = gbc.predict(X_test) roc_auc_gbc = roc_auc_score(y_test, y_pred_gbc) roc_auc_gbc RocCurveDisplay.from_estimator(gbc, X_test, y_test) plt.show() print(classification_report(y_test, y_pred_gbc)) cm3 = confusion_matrix(y_test, y_pred_gbc) disp = ConfusionMatrixDisplay(confusion_matrix=cm3) disp.plot() plt.show() from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_curve logit_roc_auc = roc_auc_score(y_test, y_pred_logr) fpr, tpr, thresholds = roc_curve(y_test, logr.predict_proba(X_test)[:,1]) rf_roc_auc = roc_auc_score(y_test, y_pred_rfc) rf_fpr, rf_tpr, rf_thresholds = roc_curve(y_test, rfc.predict_proba(X_test)[:,1]) gbc_roc_auc = roc_auc_score(y_test, y_pred_gbc) gbc_fpr, gbc_tpr, gbc_thresholds = roc_curve(y_test, gbc.predict_proba(X_test)[:,1]) plt.figure() plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc) plt.plot(rf_fpr, rf_tpr, label='Random Forest (area = %0.2f)' % rf_roc_auc) plt.plot(gbc_fpr, gbc_tpr, label='GradientBoostingClassifier (area = %0.2f)' % gbc_roc_auc) plt.plot([0, 1], [0, 1], [0, 1],'r--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic') plt.legend(loc="lower right") plt.savefig('ROC') plt.show() feature_labels = np.array(['satisfaction_level', 'last_evaluation', 'time_spend_company', 'Work_accident', 'promotion_last_5years', 'department_RandD', 'department_hr', 'department_management', 'salary_high', 'salary_low']) importance = rfc.feature_importances feature_indexes_by_importance = importance.argsort() for index in feature_indexes_by_importance: print('{}-{:.2f}%'.format(feature_labels[index], (importance[index] *100.0))) department_hr-0.10% department_RandD-0.69% salary_high-0.78% department_management-1.22% last_evaluation-12.13% Work_accident-14.11% promotion_last_5years-18.33% time_spend_company-18.51% satisfaction_level-34.13%