Joyal
John

Data-Driven HR Insights & Employee Attrition Prediction

Conducted EDA to uncover patterns in employee demographics. Built and evaluated logistic regression and decision tree models to predict the likelihood of employees leaving the company, achieving strong performance through feature engineering and model tuning. Visualised critical insights such as attrition by department, and relationships between average_monthly_hours and promotion_last_5years; using bar plots and heatmaps to support business decision-making.

Concluding the project, I provided actionable recommendations to improve employee retention strategies based on my findings.

California Housing Price Prediction Machine-Learning Model

Identified, implemented, and optimized machine learning algorithms to exhibit high predictive accuracy in forecasting median house values within California location. Used Python Notebook, pandas, numpy, tensorflow, keras, matplotlib and seaborn. Dataset from Kaggle.

Companies House Application

Work-in-progress command-line application that allows users with their own Companies House API key to gather various data of any company in the UK. Data fetched include title, address, number, status, creation, type, and address. Written in Rust for faster compilation.