1. Setup:

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

2. 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.

3. 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.

4. 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%.