My Projects & Portfolio Work
A selection of my best work demonstrating technical skill, attention to detail, and comprehensive problem-solving capabilities across various domains.
Featured Projects
Virtual Hand Gesture Controller
A hands-free computer interaction system built with OpenCV, MediaPipe, and PyAutoGUI. Detects real-time hand gestures via webcam to control scrolling, slide navigation, and program states. Features custom gestures (thumbs up/down, left/right) for precise control, ideal for presentations or touchless navigation.
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Home Garden Management Database
A structured SQLite database for managing a home garden. Tracks plants, seeds, planting history, sections, and germination progress. Designed to help gardeners make informed, data-driven decisions about care schedules, growth tracking, and optimal planting conditions.
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Tea Leaf Disease Identification: CNN Deep Learning Trained Model
A Deep Learning classification model built with MobileNetV2 Transfer Learning. Detects and accurately classifies 8 distinct tea leaf conditions (seven diseases and the healthy state) from image input.This project delivers a ready-to-deploy Keras asset and the complete training notebook, providing a robust, automated screening solution for tea crop health and agricultural monitoring.
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R Package for Data Cleaning & Inspection
A beginner-friendly R package for cleaning and inspecting datasets. Includes functions to clean column names, check missing values, and detect outliers, making exploratory data analysis faster and easier. Ideal for students, analysts, and data enthusiasts working with small to medium datasets.
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Customer Churn Prediction for a Subscription Service
A machine learning project that predicts whether a customer will cancel their subscription based on behavioral and demographic data. It uses classification models such as Random Forest and SVM to identify key churn factors and help improve customer retention strategies.
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Image Classifier
A deep learning project that classifies images into different categories by learning visual patterns. It involves data preprocessing, scaling pixel values (dividing by 255), model training, and evaluation using techniques like CNNs or SVM for image recognition.
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