Developed a full-stack predictive analytics platform using Flask, SQLAlchemy, and scikit-learn to analyze and visualize university student dropout risks. The system uses a Random Forest Classifier to assess dropout likelihood based on academic, behavioral, mental health, and financial factors. 🔹 Implemented Monte Carlo simulation for synthetic data generation (1000+ students) 🔹 Engineered features such as GPA, attendance, last activity, mental health score, etc. 🔹 Built scheduled retraining with APScheduler, and visualized performance with Matplotlib and Seaborn 🔹 Achieved dynamic user roles (Admin, Student), risk dashboard, and RESTful API endpoints 🔹 Persisted predictions and model metrics to SQLite, enabling detailed student-level risk factor analysis Tech stack: Python, Flask, SQLite, Pandas, scikit-learn, Matplotlib, HTML/CSS, REST API
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Analyzed Amazon reviews using Python and NLP to extract insights on customer sentiment, rating trends, and review themes. Built interactive dashboards with Plotly and maintained version control via GitLab. Focused on three products, showcasing data cleaning, EDA, and VADER sentiment analysis. .
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