Global Temperature Anomalies Predictor

Built an end-to-end Machine Learning forecasting system to predict global temperature anomalies using advanced regression and time-series techniques.

Developed an interactive Streamlit dashboard with model comparison, hyperparameter tuning, and confidence interval estimation.

Improved forecast reliability through lag feature engineering and residual analysis.

Demonstrates strong expertise in predictive modeling, data preprocessing, and deploying production-ready ML solutions.