A passionate and accomplished Data Scientist and Machine Learning Engineer with a mission to uncover actionable insights and craft cutting-edge solutions. My journey is defined by a relentless drive to transform challenges into opportunities, leveraging data science and artificial intelligence to create meaningful impact.
With experience across diverse business domains—including E-commerce, Retail, Supply chain, legal, manufacturing, and marketing for well-known brands such as —I bring a unique blend of business acumen and technical expertise. My ability to navigate complex industry challenges is complemented by advanced programming skills in Python, R, SQL, and expertise in visualization tools like Power BI and Tableau.
Throughout my career, I have successfully managed multiple projects, delivering measurable value across various industries. My focus lies in bridging the gap between strategic decision-making and technical insights, utilizing Machine Learning, AI, Data Visualization, and Predictive Analytics to create data-driven solutions that drive results.
I firmly believe in the transformative power of data to empower individuals, optimize industries, and solve global challenges. For me, data science and AI aren't just about algorithms or statistics—they're about creating tools, products, and solutions that foster understanding, enable better decisions, and improve people’s experiences and lives.
Below are some of my key projects, showcasing skills in Machine Learning (ML), Natural Language Processing (NLP), Artificial Intelligence (AI), Recommendation Systems, Computer Vision, and Automation.
Stock Price Prediction App
Description: Predicts real-time stock prices using LSTMs and integrates data visualization.
- Key Technologies: Python, LSTM, Time-Series Forecasting.
Mood-Based Song Recommender
Description: A web application that provides songs links based on the user's mood (e.g., happy, sad, neutral) .
- Key Technologies: Python, Streamlit.
Travel Route Recommender
- Key Technologies: Google Maps API, Python
BART Text Summarizer App
Description: A user-friendly web app to generate concise text summaries using the pre-trained BART model deployed with
Streamlit.
Amazon Product Review Analysis
Description: This project analyzes Amazon product reviews using Natural Language Processing (NLP) techniques to uncover patterns in customer feedback. By processing and visualizing text data, the project provides insights into product sentiment, feature analysis, and common issues. The analysis highlights key aspects of customer reviews to inform product improvements and marketing strategies.
AI-Powered NLP Model for Toxic Comment Detection Using BERT Description: This project uses BERT, a deep learning powerful pre-trained model, to classify comments as Toxic or Non-Toxic. The project addresses data imbalance using oversampling techniques and evaluates model performance using both Logistic Regression and BERT for comparison.
- Ship Detection from Satellite Imagery
Description: Detecting ships in satellite imagery using CNNs and advanced image processing techniques.- Key Technologies: TensorFlow, Keras, Computer Vision.
- Automated File Organizer
Description: A script to automatically organize files into categorized folders based on file types, improving efficiency and file management.- Key Technologies: Python, OS Automation.
Job Scraper Pipeline Description: A simple Python-based app that scrapes job listings from websites like Python.org and RemoteOK, filters for U.S. or remote jobs, and saves them in a structured Excel file with clickable job titles. Built for non-technical users using a one-click macOS Automator app.
- Key Technologies: Python, BeautifulSoup, Pandas, Excel, macOS Automator
- Employee Attrition Prediction
Description: A machine learning project to predict employee attrition based on work-related and demographic factors. Built and tuned classification models to identify key contributors to attrition.- Key Technologies: Python, Scikit-learn, Pandas, Matplotlib, Seaborn
- Heart Disease Prediction App: Predicts heart disease risks using ML models.