Projects
Projects
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Sequence to Sequence Learning with Neural Networks
Sequence to Sequence Learning with Neural Networks
A Question Answering System was developed using various Natural Language Processing techniques. User questions are answered using natural language processing. Machine learning and other cutting-edge technologies is used to retrieve data from Yale University.
A Question Answering System was developed using various Natural Language Processing techniques. User questions are answered using natural language processing. Machine learning and other cutting-edge technologies is used to retrieve data from Yale University.
Instance Segmentation
Instance Segmentation
Identification of individual objects within an image and the allocation of each pixel to a specific object instance is done in Pytorch. Images from University of New Haven surroundings are collected and are annotated using Label Studio to identify various classes an object belongs to in an image using Deep Learning techniques.
Identification of individual objects within an image and the allocation of each pixel to a specific object instance is done in Pytorch. Images from University of New Haven surroundings are collected and are annotated using Label Studio to identify various classes an object belongs to in an image using Deep Learning techniques.
Fraud Detection
Fraud Detection
The goal of the project was to build a Machine Learning model to detect the auto insurance fraud. Multiple algorithms like Naïve Bayes, SVM, Random Forest, Ada Boost and XG Boost were tested and the best model was later deployed into AWS cloud to make the predictions.
The goal of the project was to build a Machine Learning model to detect the auto insurance fraud. Multiple algorithms like Naïve Bayes, SVM, Random Forest, Ada Boost and XG Boost were tested and the best model was later deployed into AWS cloud to make the predictions.
X’s & O’s
X’s & O’s
A Tic-Tac-Toe game is developed using different artificial intelligence agents in Python. Implemented the game using the following four agents with their variations: Expectimax, Minimax, Alpha- Beta, Q learning and Approximate Q learning algorithms.
A Tic-Tac-Toe game is developed using different artificial intelligence agents in Python. Implemented the game using the following four agents with their variations: Expectimax, Minimax, Alpha- Beta, Q learning and Approximate Q learning algorithms.
Toy Sales Analysis
Toy Sales Analysis
Analysis was done using Power BI to determine which toy had the maximum number of sales, as well as which store location and city have the maximum sales. Moreover, identification of the year that had higher sales and the number of units sold in order to gain insights into customer
Analysis was done using Power BI to determine which toy had the maximum number of sales, as well as which store location and city have the maximum sales. Moreover, identification of the year that had higher sales and the number of units sold in order to gain insights into customer
preferences and behavior was done.
preferences and behavior was done.
Other Projects:- UNIVERSITY OF TEXAS AT AUSTIN ACADEMIC E-PORTFOLIO
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