swarnima000 / intervie-tech-final-project-employee-attrition Goto Github PK

Setup:

• Ensure Python is installed on your machine.
• Install the required libraries (numpy, pandas, seaborn, matplotlib) using pip.

Usage:

• Clone the repository to your local machine.
• Navigate to the project folder in the terminal.
• Run the Python script (FINAL_TASK.py) to execute the code.

Data Analysis:

• The code performs Exploratory Data Analysis (EDA) on the employee attrition dataset.
• It includes analysis of categorical columns with respect to the target column (Attrition).
• It also analyzes continuous and discrete data with respect to the target column.

Model Building:

• Two machine learning models are built for predicting employee attrition: Decision Tree Classifier and Random Forest Classifier.
• The SMOTE (Synthetic Minority Over-sampling Technique) is used to balance the target column (Attrition) to handle class imbalance.
• The models are trained on the balanced dataset and evaluated using classification reports and F1 scores.
• Random Forest demonstrated superior performance with an accuracy rate of 94%.