Mukashifa Fatima

Developed a predictive model using Logistic Regression, SVM, and Random Forest, achieving an 85 percent accuracy in predicting air quality levels. Processed and analyzed 10,000+ data points to quantify air pollution levels, contributing to public health awareness. Created visualizations that increased user engagement by 30 percent, enhancing public understanding of environmental data.

Analyzed 50,000+ YouTube comments using NLP and machine learning techniques, providing content creators with insights into viewer sentiments. Achieved 90 percent accuracy in sentiment classification, enabling creators to adjust their content strategy based on audience feedback. Designed visual reports that helped users identify key trends and patterns, improving decision-making processes.

Implemented a fake news detection system using Linear Regression, Naïve Bayes, and SVM, with an accuracy of 88 percent in identifying false information. Processed 20,000+ news articles from various online platforms, significantly reducing the spread of misinformation. Optimized model performance, reducing false positives by 15 percent.

Developed a full-stack web application connecting 500+ students and alumni, facilitating communication and knowledge sharing. Managed database operations, handling 1,000+ records efficiently, ensuring data integrity and accessibility. Improved user experience by implementing a streamlined UI, resulting in a 25 percent increase in user retention.

Relevant coursework included Software Engineering, Data Structures, Algorithms Analysis, Data Science, Artificial Intelligence, and Machine Learning.