Build AI models to reduce employees hiring and training costs by predicting which employees might leave the company
Developed machine learning models to predict which employees are more likely to quit.
Performed exploratory data analysis and visualized employees dataset using histogram, heatmap, boxplot, countplot, and Kernel Density Estimate (KDE) with Seaborn and Matplotlib to gain insight of what makes employees want to leave or stay.
Engineered features using Pandas and Scikit-Learn (one-hot encoder, data normalization) to feed the data into machine learning models.
Optimized logistic regression, random forest classifier, and artificial neural network to reach the best model for such an unbalanced dataset.