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HERE'S WHAT I HAVE DONE
Home Depot Recommendation System
This repository contains a recommendation system developed for Home Depot's product offerings, aimed at enhancing the customer shopping experience through personalized product suggestions.
Technology Stack:
Python
Suture Analysis using ML model
Developed an ML model to provide accurate suture grading and provide training material for medical professionals.
Technology Stack:
Python
Supply Chain Analytics using ETL
Created a Power BI dashboard to analyze and visualize supply chain data for a fashion and makeup company, using Python for ETL, Snowflake for data warehousing, and Power BI for interactive insights.
Technology Stack:
Power BI, Python
Customer Churn Analysis
Created a Power BI dashboard to analyze customer churn, identify key factors, and provide actionable insights to reduce churn rates and boost customer retention.
Technology Stack:
Power BI
Customer Churn prediction
A machine learning project designed to predict customer churn by analyzing historical and demographic data using supervised learning models.
Technology Stack:
Python
Big Tech Stock price prediction using ML
Created a Jupyter notebook to predict stock prices of major tech companies using various machine learning models, including Linear Regression, Random Forest, MLP, and ARIMA, leveraging historical data from 2010 to 2023.
Technology Stack:
Python
Credit Card Fraud Detection
Developed a model to detect credit card fraud using logistic regression, SVM, random forest, XGBoost, MLP, and neural networks, leveraging a highly imbalanced dataset and various evaluation metrics to ensure accuracy and reliability.
Technology Stack:
Python
Disaster Fake Tweet Classification
Developed and hosted a real-time tweet classification system for disaster management using LSTM and BERT models to distinguish between fake and real tweets, improving situational awareness and public safety.
Technology Stack:
Python
Chess AI Checkmate Solver
Developed a Chess Puzzle Solver using advanced search algorithms like Minimax, Alpha-Beta Pruning, Negamax, and Negascout to efficiently solve chess puzzles and explore AI game theory.
Technology Stack:
Python
PacMan AI Search Algorithms
Implemented search algorithms such as DFS, BFS, UCS, and A* to assist Pac-Man in efficiently navigating maze-like environments
Technology Stack:
Python
PacMan AI Multi Agent Search
Implemented multi-agent search algorithms, including Minimax, Alpha-Beta Pruning, and Expectimax, to enhance Pacman's decision-making abilities in navigating mazes with ghosts.
Technology Stack:
Python
PacMan AI Reinforcement Learning
Implemented reinforcement learning algorithms, including Value Iteration and Q-learning, to train Pac-Man agents for efficient maze navigation and enhancing decision-making capabilities.
Technology Stack:
Python
NLP Text Classification using Logistic Regression
Developed a text classification system using scikit-learn to identify and categorize offensive language in social media posts, implementing logistic regression and additional features like sentiment analysis to enhance accuracy for the OffensEval shared task.
Technology Stack:
Python
Scientific Text Quantity Recognition
Developed and evaluated a Recurrent Neural Network using Keras for Quantity recognition in scientific texts, employing Bidirectional LSTM and pre-trained GloVe embeddings to enhance model performance as part of the SemEval-2021 MeasEval shared task.
Technology Stack:
Python
Common Sense Reasoning Models
Fine-tuned pre-trained language models like RoBERTa and BART to address common sense reasoning tasks for the ComVE shared task from SemEval-2020, covering binary classification, reason selection, and sequence-to-sequence generation.
Technology Stack:
Python
Leaf Disease Detection using CNN
Developed a Convolutional Neural Network (CNN) to detect leaf diseases and provide recommendations, utilizing a dataset of over 88,000 images across 38 classes, enhancing agricultural disease management.
Technology Stack:
Python
Napoleon's March Visualization Recreated
Reproduced Minard’s famous graphic of Napoleon’s March on Moscow using ggplot2, incorporating data on troop strength, cities, and temperature to visualize the historical event.
Technology Stack:
R
Visualizing Global Deforestation Trends
Developed visualizations to analyze global deforestation trends and soybean consumption impacts using data from Our World in Data, employing choropleth maps and animated plots to uncover patterns and changes over time.
Technology Stack:
R
Rental Data Time Series Visualization
Visualized and analyzed rental data in San Francisco using R, comparing the distribution of rental prices per bedroom across neighborhoods from 2010 to 2018, generating insightful plots to highlight trends and differences.
Technology Stack:
R
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Projects
If you have made it this far, here's something to get you excited!
I have completed a couple of projects in Analytics, Machine Learning and NLP. Below are the list of all the projects I have worked on in the past year!
In case you want to view the specifics of the project, please visit my GitHub page, link down below!
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