Technologies: Core Java, Object-Oriented Programming (OOP), Java Collections Framework, Java File Handling (I/O Streams), Java Time API (LocalDate, DateTimeFormatter), Console-Based User Interface, Exception Handling, JDK (Java Development Kit)
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Designed and implemented a Core Java banking application addressing secure user registration, authentication, and dynamic account number generation to streamline banking operations
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Utilized object-oriented principles and Java Collections Framework to manage customer data efficiently, incorporating file handling for persistent storage and exception handling to ensure robust transaction processing
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Delivered a console-based user interface simulating real-world banking workflows, enabling deposit, withdrawal, balance inquiry, and account management with improved data integrity and user experience
Heart Disease Prediction using the classification Alogorithms
Technologies: Python, scikit-learn, pandas, NumPy, Matplotlib, Seaborn, Jupyter Notebook, GridSearchCV, Machine Learning, Data Preprocessing, Classification Algorithms, Model Evaluation
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Developed a machine learning pipeline to predict heart disease risk by preprocessing and cleaning a diverse patient dataset of over 1,000 records, ensuring data quality and feature relevance through exploratory data analysis and feature engineering
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Implemented and compared multiple classification algorithms including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest using Python and scikit-learn, optimizing hyperparameters via grid search to achieve a best model accuracy of 87%
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Delivered a predictive model that outputs both binary classification and probability scores, enabling clinicians to assess patient risk levels early, improving preventive healthcare decisions and potentially reducing heart disease incidence through timely intervention