Projects

  • Informed and Uninformed Search

    Developed a Python-based agent to solve the "Expense 8 Puzzle Problem," to optimize the cost of moving tiles. • Implemented various search algorithms including BFS, UCS, DFS, DLS, IDS, A* and analyzed an optimal solution based on cost to achieve desired puzzle configuration.

  • Relational DBMS

    Using ORACLE/MySQL in Omega, created database for ‘European Soccer’ for the year of 2014. • Loaded over 5,000 records into the created tables and executed the given queries. • Leveraged SQL's capabilities for complex joins, aggregations and Implemented SQL scripts for data cleaning and transformation.

  • Heart attack prediction

    Predicted the Heart Problem severity among individuals using Pandas Frameworks for Data Preprocessing. Applied ML Algorithms including Logistic Regression and Random Forest Classifier to accurately predict the probability of severe heart problems in individuals. The analysis provides valuable insights into feature importance, contributing to a better understanding of cardiovascular risk factors.

This Power BI exploration into Adidas shoe sales has been an insightful endeavor, revealing critical trends that can help strategize future sales approaches, marketing campaigns, and product development. It's clear that the blend of traditional and digital sales channels, along with a keen eye on regional preferences, can pave the way for sustained growth in the competitive footwear market.

Using a dynamic dataset for a global coffee chain, I analyzed sales, profits, and regional trends across products like coffee, espresso, and tea. Creating interactive dashboards in Tableau allowed me to uncover trends, tell compelling stories, and develop strategies for growth.