Project Overview:

Developed Ticket Tales V2, a feature-rich single-page web application for online ticket booking and management. Enhanced the project using Vue.js for a responsive frontend and Flask for a robust backend with secure CRUD APIs. Implemented admin capabilities for venue management, show additions, and performance metrics. Introduced monthly entertainment reports for users and theatre performance reports for administrators, leveraging Celery for background task management and Redis for caching.

Key Features:

• Vue.js for frontend development. Created a responsive and user-friendly interface for seamless ticket booking and management.


• Flask for backend development. Built a robust backend with secure CRUD APIs for efficient data management and user interactions.


• Admin capabilities. Implemented features for venue management, show additions, and performance metrics to enhance user experience and system functionality.


• Monthly entertainment reports. Introduced reports for users to track their entertainment activities and preferences, providing valuable insights and recommendations.


• Theatre performance reports. Developed reports for administrators to monitor theatre performance and make data-driven decisions for business growth and improvement.


• Celery for background task management. Utilized Celery to manage background tasks efficiently, ensuring smooth system performance and user experience.


• Redis for caching. Implemented Redis for caching to improve system performance and speed, making the application responsive and suitable for a high volume of users.


• It was a curriculum project for Modern Application Development II, also received a Best Project Award for this project and scored 95/100.

Project Overview:

Developed a comprehensive machine learning project as part of a Kaggle Competition, focusing on taxi fare prediction, a Regression problem.

Key Features:

• A comprehensive machine learning project as part of a Kaggle Competition, focusing on taxi fare prediction, a Regression problem.


• Conducted Exploratory data analysis (EDA) to understand the dataset's characteristics and identify key features for predictive modelling.


• Implemented end-to-end model development, including data preprocessing, feature engineering, model selection, and hyperparameter tuning.


• Trained and evaluated four regression models, including Linear Regression, KNN Regressor, Random Forest Regressor, and XGBoost Regressor, achieving the highest R2 score of 0.956 on the test dataset with XGBoost Regressor.


• It was a curriculum project for Machine Learning Practice and scored 100/100.

Project Overview:

Performed Data Analysis on 8 months of business data of Haryana State Warehousing Corp. (Govt. organisation) and found 2 major underlying problems.

Key Features:

• Conducted Data Analysis on 8 months of business data of Haryana State Warehousing Corp. (Govt. organisation) and found 2 major underlying problems.


• Identified underutilized and overutilized districts during peak and off-peak seasons by means of Data Analysis and developed strategies to address capacity and distribution challenges.


• Gave 7 Data-Driven recommendations to improve efficiency, reduce operating costs, and enhance profitability for HSWC.


• It was a curriculum project of Business Data Management, I undertook the project spanning three stages: Proposal, Mid-term submission, and Final submission, culminating in a Viva presentation.

Project Overview:

Developed a Movies Recommender System using collaborative filtering techniques to recommend movies to users based on their preferences and ratings.

Key Features:

• Developed a content-based recommender system using unsupervised learning techniques, leveraging two datasets of movies from TMDB (The Movie Data Base) website.


• Conducted data extraction and manipulation to create feature tags, and used scikit-learn to calculate cosine similarities, exporting the results to a pickle file for efficient storage and retrieval.


• Designed an interactive web interface with Streamlit, integrating TMDB API for dynamic movie posters and information display.


• Provided an intuitive user experience with seamless movie search and recommendation features, enhancing the application's usability and appeal.


• Hosted the application on Heroku, ensuring reliable and scalable access for users, and utilized Python and scikit-learn for data processing and algorithm implementation.

Project Overview:

Developed an Object Detection model using OpenCV and Python to identify and classify objects in images and videos.

Key Features:

• Implemented real-time object detection using OpenCV and MobileNetSSD, capable of processing both video streams and images.


• The system loops through each frame of the video, detecting objects such as Person, Car, MotorBike, TrafficLights, Dog, Cat, etc., based on labels defined in a Labels.txt file.


• Detected objects are highlighted with bounding boxes and labeled with their respective names, enhancing visual identification and analysis.


• Utilized efficient algorithms to ensure accurate and fast object detection, suitable for various applications requiring real-time analysis.


• Developed a robust and scalable solution for object detection, applicable to surveillance, autonomous vehicles, and other real-time monitoring systems.

Project Overview:

Developed a Chrome Extension that fetches and displays the Astronomy Picture of the Day (APOD) from NASA's API, providing users with daily insights into the cosmos.

Key Features:

• Developed a Chrome Extension called "Astronomical Pic Of the Day" (APOD) that displays the daily astronomical picture using the NASA API.


• Utilized basic HTML, CSS, and JavaScript to create the extension, which fetches and shows a brief description of the daily image from the API.


• The extension refreshes daily to provide users with a new astronomical picture and its details every day.


• Enabled users to navigate through previous APOD images using a date picker, providing access to a vast collection of astronomical images and insights.


• Marked the beginning of my coding journey with this project, showcasing my ability to integrate APIs and build functional browser extensions.