This project aims to classify news articles as either real or fake using Natural Language Processing (NLP) techniques. It uses a dataset of news articles and applies machine learning algorithms like Logistic Regression and Random Forest for classification.
Implementation: Fake News Detection Dataset
Technologies Used: Python, Pandas, Scikit-learn, NLTK, TensorFlow
This project focuses on analyzing a dataset containing various smartphone specifications, such as price, features, and performance. It helps in understanding trends in smartphone prices and performance correlations.
Implementation: Smartphones Dataset
Technologies Used: Python, Pandas, Matplotlib, Seaborn
This project involves analyzing Uber rides data to understand trends in ride requests, pricing, and ride durations. It helps to identify peak times, popular locations, and pricing patterns.
Implementation: Uber Rides Dataset
Technologies Used: Python, Pandas, Matplotlib, Seaborn