Projects
Online Retail Customer Analysis Using RFM and K-Means Clustering
This project involves analyzing an online retail dataset by applying RFM (Recency, Frequency, Monetary) analysis to segment customers based on their purchasing behavior. After data exploration and cleaning, K-Means clustering is used to categorize customers into different groups. The Elbow Method and Silhouette Score help evaluate and determine the optimal number of clusters for effective customer segmentation.
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AI-Powered MCQ Generator Using NLP and LSTM
An AI-powered MCQ generator leverages Natural Language Processing (NLP) and Long Short-Term Memory (LSTM) networks to automatically create multiple-choice questions. It preprocesses text using spaCy, extracts key nouns, and generates distractors using word embeddings. The model enhances educational assessments by automating question creation with high accuracy and relevance. This approach simplifies content generation for educators and e-learning platforms.
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Student Performance Analysis
The Student Performance Analyzer is a machine learning-based project designed to predict student scores based on various factors like parental education, lunch type, and test preparation. It includes data preprocessing, exploratory data analysis (EDA), and model training to improve prediction accuracy. The project features a Flask-based web application where users can input student details and receive predicted scores. Its structured pipeline ensures efficient data ingestion, transformation, and deployment.
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- Python
- NumPy
- Pandas
- Matplotlib
- Excel
- Seaborn
- Machine Learning
- Scikit-learn
- Deep Learning
- NLP
- MySQL
- Power BI