Employee Attrition & Performance Analytics

Project Overview

This project aims to uncover the factors that contribute to employee attrition and analyze performance-related aspects within an organization. The analysis involves exploring the dataset, performing data preprocessing, visualizing key insights, and building a predictive model to understand and predict employee attrition.

Dataset

The dataset used for this analysis contains information about employees, including various features such as age, department, job role, performance metrics, and whether the employee has left the organization (attrition).

Key Tasks

1. Exploratory Data Analysis (EDA):

   • Load the dataset and explore its structure.
   • Examine data distributions, identify patterns, and understand relationships between variables.

2. Data Preprocessing:

   • Handle missing values, if any.
   • Perform feature engineering if needed.
   • Prepare the data for modeling.

3. Data Visualization:

   • Visualize key metrics and relationships using tools like Matplotlib and Seaborn.
   • Explore factors contributing to attrition visually.

4. Feature Selection:

   • Identify important features that impact employee attrition.
   • Utilize correlation matrices and other methods for feature selection.

5. Model Building:

   • Implement machine learning models to predict employee attrition.
   • Evaluate model performance using relevant metrics.

6. Results and Insights:

   • Interpret the results obtained from the analysis.
   • Provide insights into the key factors influencing attrition.

Technologies Used

• Python
• Pandas, NumPy for data manipulation
• Matplotlib, Seaborn for data visualization
• Scikit-learn for machine learning models