Muskan Raisinghani's repositories
CreditCardApprovalPrediction
The Credit Card Approval Prediction system leverages regression models, AutoML, SHAP analysis, and advanced data visualization techniques. This comprehensive system enhances accuracy in predicting credit card approvals, showcasing a blend of sophisticated modeling, automation, and interpretability.
british-airways-customer-feedback-analysis
The aim of the project is to understand British Airways' customer experience through analysis of their feedback/reviews.
CloudCampus
CloudCampus is a Decentralized Virtual Education Platform aimed to help students with better education system and equal opportunity.
Investment-Analysis-and-Portfolio-Management
This data analytics project uses python's robust modules including Pandas, NumPy, Matplotlib, Seaborn, and Statistics, working extensively with real world Finance data. The project determines powerful relationships between risk, return, and price.
Movie-Recommendation-System
The project aims to create movie recommendation system with algorithms, including content-based, popularity-based, and collaborative filtering methods. Data of over 4800 movies is used.
Real-Time-Data-Streaming
The project demonstrates an end-to-end data pipeline using Apache Kafka to fetch and stream data from a website. The project is containerized with Docker for streamlined deployment and dependency management.
Text-classification-using-BERT-models
The project involves developing a proof-of-concept system for classifying financial excerpts into predefined categories using Natural Language Processing (NLP) techniques.
VolunteerManagement
The project functions as a worldwide platform that facilitates communication between volunteers and organizations in need. Constructed using Java in NetBeans, the primary goal of the project is to improve the efficiency of aligning volunteer opportunities with aid requests, promoting a heightened sense of community engagement and support.
SuperStore-Analysis-And-Feature-Selection
This data science project aims to assist a superstore giant in identifying crucial variables for predicting product prices. It employs Lasso regression for price prediction and utilizes scikit-learn for feature selection.