Hi, I'm <Vishnu Vardhan>

Analyzed a dataset containing various chronic disease indicators to uncover patterns and insights that aid in disease prevention. The project involved data preprocessing, exploratory data analysis (EDA), and applying machine learning algorithms to predict the likelihood of chronic diseases. Key insights were derived using statistical methods and visualizations, helping to identify risk factors and correlations between diseases and lifestyle choices. Click here to reach Github for more information.

Tech Stack Used:-

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Machine Learning Algorithms: Logistic Regression, Random Forest, Decision Trees
• Tools: Jupyter Notebook, GitHub

Developed a dynamic web-based analytics system to analyze and optimize campus placement strategies. The project aimed to identify key factors influencing student placements by leveraging data-driven insights. The system enables students, faculty, and placement officers to explore historical placement trends, company hiring patterns, and skill requirements. Click here to reach Github for more information.

Tech Stack Used:-

• Frontend: HTML, CSS, JavaScript
• Backend: Flask, Python
• Database: SQL (MySQL/PostgreSQL)
• Machine Learning Algorithms: Random Forest, Decision Trees, Logistic Regression
• Visualization Tools: Matplotlib, Seaborn, Chart.js

Developed a machine learning model to predict house prices based on various features such as location, number of rooms, area, and other property attributes. The project involved exploratory data analysis (EDA), correlation analysis, feature engineering, and model optimization to improve predictive accuracy. Click here to reach Github for more information.

Tech Stack Used:-

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Machine Learning Algorithm: RandomForestRegressor
• Tools: Jupyter Notebook, GitHub

Developed an automated plant disease detection system using Convolutional Neural Networks (CNNs) to classify plant diseases based on leaf images. The model analyzes plant health in real-time, helping farmers take timely action to prevent crop damage. The system was trained on an image dataset of diseased and healthy plants, leveraging deep learning techniques for accurate classification. Click here to reach Github for more information.

Tech Stack Used:-

• Programming Language: Python
• Deep Learning Framework: Keras (TensorFlow backend)
• Computer Vision: OpenCV
• Data Handling & Augmentation: ImageDataGenerator, NumPy, Pandas
• Visualization: Matplotlib, Seaborn
• Deployment (Optional): Flask (for web-based inference)

Designed and developed a fully responsive and visually appealing portfolio website using modern web technologies. The portfolio showcases projects, skills, and experiences in an interactive and user-friendly manner. It includes smooth scrolling, animations, and an interactive contact form to enhance user engagement. Click here to reach Github for more information.

Tech Stack Used:-

• Frontend:HTML, CSS, Bootstrap, JavaScript
• UI Enhancements: CSS Animations, Scroll Effects
• Interactivity: JavaScript for dynamic behavior
• Deployment: GitHub Pages / Netlify

Developed a responsive and interactive dog information website that fetches random dog facts using an API. The website enhances user engagement with a clean UI, smooth animations, and dynamic content updates. It is designed to be mobile-friendly and provides fun and educational dog-related facts at the click of a button. Click here to reach Github for more information.

Tech Stack Used:-

• Frontend:HTML, CSS, Bootstrap, JavaScript
• API Integration: Fetch API for retrieving random dog facts
• UI Enhancements: CSS animations, hover effects
• Deployment: GitHub Pages / Netlify