🐍 VenoScan

✨ Built an AI-powered application to classify snakes as venomous or non‑venomous using image recognition.

✨ Designed and trained deep learning models (CNN with Keras/TensorFlow) for accurate classification.

✨ Integrated Flask backend to serve predictions via a web interface.

✨ Deployed the model locally and on cloud platforms with Git LFS for handling large .h5 files.

✨ Implemented Dockerfile and environment control for reproducible deployment.

⚙️ Tech Stack

Languages: Python

Frameworks/Libraries: Fine-Tunning, TensorFlow, Keras, Flask, NumPy, Pandas

Tools: GitHub, Git LFS, Docker, Render/Google Cloud

Environment: Jupyter Notebook, Visual Studio Code

✈️ Flight Ticket Booking Website

✨ Designed and implemented a Java‑based application for booking airline tickets with a user‑friendly interface

✨ Developed role‑based access with separate modules for Admin (managing flights, schedules, and passenger records) and Passenger (booking, viewing, and managing tickets)

✨ Structured project using Maven build system with a clean package layout for professional deployment

✨ Implemented data persistence and session handling to ensure smooth booking flow and secure logout

⚙️ Tech Stack

Language: Java

Frameworks/Tools: Swing (UI), Maven (build), GitHub (version control)

Environment: Visual Studio Code / IntelliJ IDEA

Deployment: Local run and cloud deployment

House Prize Prediction

✨ Developed a machine learning model to predict house prices based on variables such as location, size, and property features.

✨ Conducted data preprocessing and feature engineering (handling missing values, normalization, and encoding categorical variables) to improve accuracy.

✨ Implemented Logistic Regression for classification of housing prices into categories (low, medium, high).

✨ Visualized data distributions and model results with Matplotlib and Seaborn for clear insights.

✨ Achieved an overall accuracy of ~85% on the test dataset, demonstrating moderate predictive performance

⚙️ Tech Stack

Language: Python

Libraries: NumPy, Pandas, Scikit‑learn, Matplotlib, Seaborn

Environment: Jupyter Notebook / Google Colab

Version Control: Git & GitHub