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Summary
Built a housing price prediction model (Linear Regression + Random Forest) on the Boston Housing dataset, achieving R² = 0.87 on the test set. Conducted exploratory data analysis on a real-world dataset (~50K rows) using pandas, and visualized key patterns with matplotlib/seaborn. Competed in the Kaggle Titanic challenge, placing in the top 30% by applying feature engineering and model ensembling. Developed an automated data cleaning and preprocessing script (pandas) that reduced manual processing time from 23 to 5 minutes.Experiences
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