Next word Prediction using LSTM and GRU
Developed a deep learning model for predicting the next word in a given sequence of words, built using LSTM and GRU networks.
Developed a deep learning model for predicting the next word in a given sequence of words, built using LSTM and GRU networks.
Developed an end-to-end deep learning project using a simple RNN on the IMDb movie reviews dataset.
A complete end-to-end data engineering and analytics solution implemented using Microsoft Fabric for NYC Yellow Taxi trip data (January– October 2025)
A comprehensive spam detection system implementing multiple text classification approaches including Bag of Words (BOW), TF-IDF Vectorization with Multinomial Naive Bayes, Word2Vec with Random Forest Classifier
Sentiment analysis on Amazon Kindle reviews using various NLP techniques: Implemented multiple text vectorization methods (BOW, TF-IDF, Word2Vec), built and compared different classification models, processed and analyzed the Kindle Reviews dataset.
Built a Power BI report analyzing sales, RFM segments, and customer behavior. Uncovered that 20% of customers drive 49% of sales. Identified high returns in Office Supplies and weak new customer acquisition.
Designed an interactive Excel dashboard using pivot tables, charts, and slicers. Tracked key metrics like call time, volume, and amount. Improved visibility into performance trends. Strengthened my Excel visualization skills.
Built a Power BI dashboard to analyze global sales by region, category, and time. Included dynamic filters, forecasts, and decomposition trees. Enabled trend discovery and strategic comparison.
Created a Power BI dashboard inspired by Pokémon’s Pokedex. Integrated external data to build interactive profiles and visuals. Designed a fun, game-style user experience.
Built a Power BI dashboard to visualize headcount, salaries, leave data, and demographics. Used DAX and Power Query for dynamic insights. Supports strategic HR decisions through clear, interactive visuals.
Analyzed customer behavior and sales for a Japanese diner using PostgreSQL. Tackled real-world questions with CTEs and subqueries. Revealed purchase patterns and visit frequency.
Used SQL to evaluate ad performance and user behavior for an online seafood store. Found a 23% lift in purchases from ad engagement. Identified top campaigns and provided optimization strategies.
Analyzed HR data to understand attrition drivers at Salifort Motors. Used EDA and ML (Random Forest, Decision Tree) for prediction. Achieved 96.2% accuracy, 93.8% AUC. Delivered actionable retention insights.
Predicted student academic outcomes using demographic and educational data. Applied EDA, preprocessing, and classification models. Helped identify at-risk students early.
Explored Walmart transaction data to uncover sales trends, top products, and customer segments. Highlighted category performance and optimization areas. Used SQL CTEs for efficient querying.