Project Overview

My mission is to predict employee attrition and help organizations retain their valuable talent! 💼 By harnessing the power of machine learning, I aim to make employee retention more data-driven and effective.

Exploratory Data Analysis (EDA)

EDA helps uncover hidden insights in the data! Captivating count plots, mesmerizing histograms, and intriguing box plots were used to better understand the data. 📊

Data Preprocessing

Data preprocessing is the key to a successful model! Outliers were treated with magic ✨, and data was transformed using the Yeo-Johnson method. Categorical features were encoded with labels for the model’s delight! 🧙‍♂️

Feature Engineering

Some feature engineering magic was sprinkled to make the model even more powerful! 🪄 New features were created and existing ones modified to enhance predictions.

Model Development

An army of models was trained to predict employee attrition! From Logistic Regression 📈 to XGBoost 🚀, the model lineup is unstoppable!

Model Evaluation

The models are as good as they come! They were evaluated using a barrage of metrics, including accuracy, precision, recall, F1-score, and ROC AUC.

Feature Selection

To make the model even more focused, Recursive Feature Elimination (RFE) was employed to choose the best features.

Hyperparameter Tuning

The models were fine-tuned using GridSearchCV to unlock their full potential!

Scaling

Standard scaling is the secret weapon! It ensures that all features are on the same playing field, with mean 0 and standard deviation 1.

Technologies Used

Exploratory Data Analysis, Data Visualization, Statistics, Machine Learning, Supervised Learning Classification, Ensemble Techniques

GitHub Repository Link (Click Here)