Jan Farag - Task final

Task.py

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+import os
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression, LinearRegression
+from sklearn.metrics import (
+    confusion_matrix,
+    classification_report,
+    accuracy_score,
+    mean_absolute_error,
+    mean_squared_error,
+    r2_score
+)
+
+# Load and inspect dataset
+file_path = 'Attrition Dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv'
+data = pd.read_csv(file_path)
+data_info = data.info()
+data_description = data.describe(include='all')
+data_head = data.head()
+
+# Check for duplicates
+duplicate_count = data.duplicated().sum()
+
+# Visualize numerical data to find outliers
+num_cols = data.select_dtypes(include=['int64', 'float64']).columns
+plt.figure(figsize=(15, 10))
+for i, column in enumerate(num_cols, 1):
+    plt.subplot(6, 6, i)
+    sns.boxplot(data[column])
+    plt.title(column)
+
+plt.tight_layout()
+plt.savefig('results/boxplots.png')
+
+# Ensure 'Attrition' is present and convert to numerical
+if 'Attrition' in data.columns:
+    data['Attrition'] = data['Attrition'].apply(lambda x: 1 if x == 'Yes' else 0)
+
+# One-hot encode categorical variables
+cat_cols = data.select_dtypes(include=['object']).columns
+cat_cols = [col for col in cat_cols if col != 'Attrition']
+data_encoded = pd.get_dummies(data, columns=cat_cols, drop_first=True)
+data_encoded.to_csv('results/preprocessed_data.csv', index=False)
+
+# Logistic Regression model
+X = data_encoded.drop('Attrition', axis=1)
+y = data_encoded['Attrition']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+log_reg = LogisticRegression(max_iter=1000)
+log_reg.fit(X_train, y_train)
+y_pred = log_reg.predict(X_test)
+log_reg_metrics = {
+    "Confusion Matrix": confusion_matrix(y_test, y_pred),
+    "Classification Report": classification_report(y_test, y_pred),
+    "Accuracy": accuracy_score(y_test, y_pred)
+}
+
+# Random Forest Classifier model
+rf_clf = RandomForestClassifier(n_estimators=100, random_state=42)
+rf_clf.fit(X_train, y_train)
+y_pred_rf = rf_clf.predict(X_test)
+rf_clf_metrics = {
+    "Confusion Matrix": confusion_matrix(y_test, y_pred_rf),
+    "Classification Report": classification_report(y_test, y_pred_rf),
+    "Accuracy": accuracy_score(y_test, y_pred_rf)
+}
+
+# Linear Regression model
+X_reg = data_encoded.drop('DailyRate', axis=1)
+y_reg = data_encoded['DailyRate']
+X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)
+
+lin_reg = LinearRegression()
+lin_reg.fit(X_train_reg, y_train_reg)
+y_pred_reg = lin_reg.predict(X_test_reg)
+lin_reg_metrics = {
+    "Mean Absolute Error": mean_absolute_error(y_test_reg, y_pred_reg),
+    "Mean Squared Error": mean_squared_error(y_test_reg, y_pred_reg),
+    "R-squared": r2_score(y_test_reg, y_pred_reg)
+}
+
+# Random Forest Regressor model
+rf_reg = RandomForestRegressor(n_estimators=100, random_state=42)
+rf_reg.fit(X_train_reg, y_train_reg)
+y_pred_rf_reg = rf_reg.predict(X_test_reg)
+rf_reg_metrics = {
+    "Mean Absolute Error": mean_absolute_error(y_test_reg, y_pred_rf_reg),
+    "Mean Squared Error": mean_squared_error(y_test_reg, y_pred_rf_reg),
+    "R-squared": r2_score(y_test_reg, y_pred_rf_reg)
+}
+
+# Create results directory
+results_dir = 'results'
+if not os.path.exists(results_dir):
+    os.makedirs(results_dir)
+
+# Save results
+results_file = os.path.join(results_dir, 'results.txt')
+with open(results_file, 'w') as file:
+    file.write("### Dataset Overview ###\n")
+    file.write(f"Rows: {data.shape[0]}\n")
+    file.write(f"Columns: {data.shape[1]}\n\n")
+
+    file.write("### Duplicate Records ###\n")
+    file.write(f"Duplicate rows: {duplicate_count}\n\n")
+
+    file.write("### Logistic Regression Metrics ###\n")
+    file.write(f"Confusion Matrix:\n{log_reg_metrics['Confusion Matrix']}\n")
+    file.write(f"Classification Report:\n{log_reg_metrics['Classification Report']}\n")
+    file.write(f"Accuracy: {log_reg_metrics['Accuracy']}\n\n")
+
+    file.write("### Random Forest Classifier Metrics ###\n")
+    file.write(f"Confusion Matrix:\n{rf_clf_metrics['Confusion Matrix']}\n")
+    file.write(f"Classification Report:\n{rf_clf_metrics['Classification Report']}\n")
+    file.write(f"Accuracy: {rf_clf_metrics['Accuracy']}\n\n")
+
+    file.write("### Linear Regression Metrics ###\n")
+    file.write(f"Mean Absolute Error: {lin_reg_metrics['Mean Absolute Error']}\n")
+    file.write(f"Mean Squared Error: {lin_reg_metrics['Mean Squared Error']}\n")
+    file.write(f"R-squared: {lin_reg_metrics['R-squared']}\n\n")
+
+    file.write("### Random Forest Regressor Metrics ###\n")
+    file.write(f"Mean Absolute Error: {rf_reg_metrics['Mean Absolute Error']}\n")
+    file.write(f"Mean Squared Error: {rf_reg_metrics['Mean Squared Error']}\n")
+    file.write(f"R-squared: {rf_reg_metrics['R-squared']}\n")
+
+print("Results have been saved in the 'results/' directory.")