My Projects

• Welcome to My Projects Showcase

  This section highlights a selection of projects I’ve developed, practiced, and refined using diverse programming languages, powerful libraries,

  robust frameworks, and essential tools. Each project reflects my dedication to solving real-world problems and enhancing technical skills.

  Click on a project to view an overview and gain insights into its objectives, implementation, and outcomes. For a deeper dive into the code and

  additional details, follow the provided links to explore the complete repositories.

  My GitHub Stats

  Repositories

  Followers

  Following

  Contributions

• 1. Predicting Heart Disease Risk with Machine Learning Models in Python

  Predicting Heart Disease Risk with Machine Learning Models in Python

  This project leverages machine learning techniques to develop a predictive system for diagnosing heart disease based on key medical features. By utilizing a dataset of heart disease patients, the system employs a Random Forest classifier to predict whether an individual is at risk of heart disease. The model is designed to assist healthcare professionals by providing accurate, data-driven predictions that can aid in early diagnosis and decision-making.

  Achieved 99% accuracy in predicting heart disease risk with a Random Forest classifier on the heart.csv dataset (1026 individuals). Evaluated model performance using classification reports and a confusion matrix, and visualized feature importance (e.g., age, cholesterol, blood pressure). Also, created bar plots comparing prediction probabilities for "Heart Disease" vs. "No Heart Disease."

  Click here for the code and more info
• 2. Medical Image Analysis for Brain Tumor Detection Using Convolutional Neural Networks (CNN) in Python

  Medical Image Analysis for Brain Tumor Detection Using Convolutional Neural Networks (CNN) in Python

  This project focuses on medical image analysis for detecting brain tumors using deep learning techniques. The task is to classify brain MRI images into two categories: "Tumor" and "No Tumor." A Convolutional Neural Network (CNN) is used to extract features and perform classification. The dataset, sourced from Kaggle, consists of 253 images, with equal representation of both tumor and non-tumor cases.

  Developed a Convolutional Neural Network (CNN) to classify brain MRI images from a Kaggle brain tumor dataset of 253 images as "Tumor" or "No Tumor." Achieved 90.00% accuracy with 30 images and 86.27% accuracy with 51 images. Implemented data augmentation and handled class imbalance, improving model performance. Evaluated the model with confusion matrix, classification report, and ROC curve for enhanced diagnostic capability.

  Click here for the code and more info (Brain Tumor with 30 images)

  Click here for the code and more info (Brain Tumor with 51 images)
• 3. Drug Effectiveness and Side Effect Analysis Dashboard in R

  Drug Effectiveness and Side Effect Analysis Dashboard in R

  This interactive Shiny application enables an in-depth exploration of drug effectiveness and side effects using the UCI Drug Review dataset. It provides a range of analytical features, such as sentiment analysis of user reviews, visualization of drug ratings distribution, and a comparison of side effects via word clouds. Users can filter data by conditions and drugs, offering insights into the drug’s effectiveness, common side effects, and statistical trends across various conditions. The application aims to support healthcare professionals and researchers in evaluating drug performance based on real-world user feedback.

  Developed an interactive Shiny dashboard using the UCI ML Drug Review dataset (1,426 records) for analyzing drug effectiveness and side effects. The project included sentiment analysis, word cloud visualizations, and statistical insights, enabling users to filter data by condition and statistic for enhanced decision-making.

  Click here for the code and more info

  Shiny App Dashboard
• 4. Diabetes Prediction Analysis using Machine Learning Approach in R

  Diabetes Prediction Analysis using Machine Learning Approach in R

  The project, Diabetes Prediction Analysis Using Machine Learning in R, utilized the Pima Indians Diabetes dataset from Kaggle. This dataset consists of 765 rows and 9 features: pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, BMI, diabetes pedigree function, age, and the outcome variable indicating diabetes presence. The goal was to evaluate and compare the performance of various machine learning models for diabetes prediction and identify key predictive features.

  Developed a diabetes prediction analysis using the Pima Indians Diabetes dataset, containing 765 individuals and 9 features. Achieved 80% accuracy with SVM, 79.13% accuracy (AUC: 0.8436) with Logistic Regression, 74.78% accuracy with Random Forest, and 73.04% accuracy (AUC: 0.8124) with XGBoost. Glucose levels and BMI were identified as the most influential features, with performance evaluation through ROC curves highlighting SVM and Logistic Regression as the bestperforming models.

  Click here for the code and more info
• 5. Mathematical Visualizations in MATLAB

  Mathematical Visualizations in MATLAB

  The project focuses on creating various mathematical visualizations in MATLAB to represent and explore different mathematical concepts. The primary goal is to produce graphical representations that aid in understanding abstract mathematical principles such as 3D surfaces, vector fields, fractals, parametric curves, eigenvectors, and heatmaps. These visualizations are commonly employed in education, research, and industry to facilitate the comprehension of complex mathematical behaviors and phenomena. The project offers several examples, showing how MATLAB can generate these visualizations for various mathematical functions and concepts.

  Developed 7+ advanced mathematical visualizations in MATLAB, including 3D plots, vector fields, and fractals, to enhance understanding of complex concepts, shared on GitHub and MATLAB Central for educational and research purposes.

  Click here for the code and more info

  MATLAB Central Community
• 6. Clustering Algorithms on the Iris Dataset in MATLAB

  Clustering Algorithms on the Iris Dataset in MATLAB

  This project focuses on applying and comparing different clustering algorithms on the famous Iris dataset. The dataset contains 150 samples from three different species of Iris flowers, with four features: sepal length, sepal width, petal length, and petal width. The main goal of this project is to apply three clustering algorithms to the Iris dataset, evaluate their performance using various metrics, and visualize the results.

  Implemented K-Means, Hierarchical, and DBSCAN clustering algorithms on the Iris dataset, Achieved an average Silhouette Score of 0.65 for K-Means and 0.47 for DBSCAN, indicating good cluster separation. Attained Davies-Bouldin Index of 0.83 for K-Means and 0.33 for DBSCAN, with Adjusted Rand Index of 0.82 and 0.75, demonstrating high clustering accuracy.

  Click here for the code and more info

  MATLAB Central Community
• 7. Predicting Titanic Survival with XGBoost and Feature Engineering

  Predicting Titanic Survival with XGBoost and Feature Engineering

  This script preprocesses the Titanic dataset, balances classes with SMOTE, and trains an XGBoost model using optimized hyperparameters. It includes feature engineering, scaling, validation, and threshold adjustment for predictions, outputting final results in adjusted_submission.csv.

  Click here for the code and more info
• 8. Random Forest Temperature Forecasting

  Random Forest Temperature Forecasting

  This script loads a temperature dataset, creates lag features, trains a Random Forest model to predict future temperatures, evaluates the model, and forecasts the next 7 days of temperatures.

  Click here for the code and more info
• 9. Weather App with Forecast & History

  Weather App with Forecast & History

  This weather application, built using Tkinter, provides real-time weather updates and a 5-day forecast by utilizing the OpenWeatherMap API. Users can enter a city name to view detailed information, including temperature, humidity, wind speed, and weather conditions, accompanied by a weather icon. The app also features a search history that dynamically stores the last five searched cities, ensuring quick access to recent queries. With a clean and interactive interface, it delivers accurate weather data and an enhanced user experience.

  Click here for the code and more info
• 10. Scientific Calculator

  Scientific Calculator

  This is a Tkinter-based scientific calculator with a dynamic button layout. It allows users to perform basic arithmetic operations and advanced functions like square root, logarithm, factorial, and modulo. The calculator includes a result display and supports clear functionality, making it user-friendly and efficient for mathematical computations.

  Click here for the code and more info
• 11. Virtual Study Assistant Using Jav

  Virtual Study Assistant

  The Virtual Study Assistant is a Java-based desktop application designed to help users manage their study

  tasks effectively. With features such as task management, overdue notifications, and completed/pending task views, this tool simplifies task organization for students and professionals.

  Click here for the code and more info
• 12. Personal Finance Tracker Using Java

  Personal Finance Tracker

  A Java-based desktop application to help you track your income and expenses. This app provides an

  intuitiveGUI for managing your financial transactions, generating reports, and exporting data to a CSV file.

  Click here for the code and more info
• 13. Student-Grade-Calculator using C++

  Student-Grade-Calculator

  The Student Grade Calculator program allows users to input student data, calculate their grades based

  on marks, and store/display the results. The program supports operations such as adding student data,

  viewing all records, saving data to a file, and loading data from a file.

  Click here for the code and more info
• 14. Bank Management System Using C++

  Bank Management System

  The Bank Management System allows users to manage bank accounts by performing basic operations like

  creating an account, depositing money, withdrawing money, viewing account details, and saving/loading

  data to/from a file.

  Click here for the code and more info
• 15. Compiler Using C++

  Compiler

  This project implements an arithmetic expression evaluator that can parse and evaluate expressions

  involving addition, subtraction, multiplication, division, and parentheses. The program also includes

  a basic code generator that simulates generating assembly-like code for the evaluation process.

  Click here for the code and more info
• 16. Hospital Management System Using C

  Hospital Management System

  The Hospital Management System is a simple C program that allows the management of patients in a hospital.

  Click here for the code and more info
• 17. Mini Interpreter Compiler for Simple Expressions Using C

  Mini Interpreter Compiler for Simple Expressions

  This program is a simple command-line-based interpreter that evaluates basic mathematical expressions

  and supports variable assignments. It uses basic operators (+, -, *, /) and allows users to assign and

  reuse variables in subsequent commands.

  Click here for the code and more info
• 18. Text Editor and File Compression Tool Using C

  Text Editor and File Compression Tool

  Text Editor

  This program provides a basic text editor functionality using the C programming language. It allows

  users to: Create a new file and write content to it, Read the content of an existing file. Append

  content to an existing file. Exit the program.

  File Compression Tool

  This C program provides a simple file compression and decompression tool using Run-Length Encoding

  (RLE). It allows users to compress files to reduce their size and decompress files back to their original content.

  Click here for the code and more info
• 19. Snake Game and Tic Tac Toe Game Using C

  Snake Game and Tic Tac Toe Game

  Snake Game

  This is a simple implementation of the classic Snake game written in the C programming language.

  The game runs in the console and features basic functionality such as movement, score tracking, and

  growing the snake when it eats a fruit.

  Tic Tac Toe Game

  This is a simple implementation of the classic Tic Tac Toe game written in the C programming language.

  It features a Player vs AI mode, where Player 1 uses X and the AI (Player 2) uses O.

  Click here for the code and more info