Student Score Prediction
Developed end‑to‑end ML system predicting student performance, achieving R² = 0.89, with modular pipeline, deployment, and measurable impact.
I'm a Machine Learning and Data Science enthusiast with a strong foundation in Python, SQL, and modern ML frameworks. I enjoy building end to end solutions. From data preprocessing and feature engineering to model development, visualization, and deployment using tools like Scikit-learn, Power BI, Flask, and Streamlit.
Through projects such as student performance prediction, sales analytics dashboards, and interactive ML apps, I've gained hands on experience in turning raw data into actionable insights. My goal is to apply analytics and machine learning to solve real world problems and drive data-driven decisions.
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Developed end‑to‑end ML system predicting student performance, achieving R² = 0.89, with modular pipeline, deployment, and measurable impact.
Analyzed sports industry market trends and performance metrics using Power BI to identify key business opportunities and consumer patterns.
Built Streamlit app classifying Iris species using sepal and petal features, enabling real‑time predictions with Scikit‑learn models.
Created interactive Power BI dashboards to visualize Swiggy's sales data, tracking order trends, revenue patterns, and customer behavior insights.
