This project applies Logistic Regression to predict employee attrition using a real-world HR dataset. It includes full data preprocessing, model training, and classifier evaluation using multiple metrics.
- Data Preprocessing
- Handled categorical variables using
pd.get_dummies() - Scaled numerical features using
StandardScaler
- Handled categorical variables using
- Model Training
- Applied Logistic Regression with to train the model on the processed data
- Model Evaluation
- Generated a confusion matrix and visualized it using a heatmap with
Seaborn - Calculated key classification metrics:
- Precision: Measures the accuracy of positive predictions
- Recall: Measures how well the model identifies positive cases
- F1 Score: Harmonic mean of precision and recall
- Generated a confusion matrix and visualized it using a heatmap with
- Displayed a classification report with precision, recall, F1-score, and support for each class
- pandas
- scikit-learn
- seaborn
- matplotlib