Projects
This project will aim to create a robust data warehouse for all the Spotify Music data, create data pipelines to control the flow and data and perform ETL processes on the raw data before storing it in the data warehouse for further analysis.
This project delves deeper into the intricate data ecosystem of Boston's Airbnb listings. By employing advanced machine learning and data mining techniques, we aim to illuminate key factors that influence listing prices and guest preferences.
The primary objective of this project is to securely handle, streamline, and analyze structured and semi-structured data from YouTube videos, focusing on video categories and trending metrics.
To identify the factors that lead to loan repayment defaults and build a model to predict whether a customer will default on their loan. The insights derived from this predictive modeling initiative will enable Aegis Bank to optimize resource allocation, enhance customer engagement, and maintain a robust risk management framework
Project Problem Statement is to design a database system for taking a medical appointment of doctor’s schedule at a specific hospital and their chains to efficiently manage the appointments, the patient friendly system and streamlining the doctor’s workflow.
This project is a clean, reproducible implementation of Human Activity Recognition (HAR) on the classic UCI HAR dataset using a family of Recurrent Neural Networks: Simple RNN, GRU, and LSTM. The project compares 3 progressively improved variants for each architecture and reports test-set results.
