This project employs a deep neural network architecture for the classification of toxic comments, utilizing the Kaggle competition dataset from the Jigsaw Toxic Comment Classification Challenge.
Tex Classification
Implemented a machine learning model to detect fake news using Natural Language Processing techniques like TF-IDF and stemming. Trained multiple classifiers including Logistic Regression and PassiveAggressiveClassifier for accurate classification. This project showcases practical NLP skills for tackling misinformation in media.
Content-based Movie Recommender System
This Repository provides the basic code snippets for all the most widely used ML Algorithms like Supervised, Unsupervised, and Recommender system algorithms.
Built a content-based movie recommendation system using Python and Machine Learning. The system suggests similar movies based on user selection by computing similarity scores using cosine similarity.
This repository houses a comprehensive Machine Learning project aimed at classifying Yelp reviews using Multinomial Naive Bayes and Natural Language Processing (NLP) techniques.
This project focuses on building a machine learning model to detect fake news articles. The model is trained using a dataset of news articles and utilizes Logistic Regression for classification. The primary goal is to classify news articles as either "real" or "fake" based on their text features such as title and author.
🧠 The project aims to predict the popularity of a movie based on it's overview text. It involves a thorough analysis of a movie dataset, exploring various aspects of data preprocessing, model building, training, and evaluation.
Developed a Python-Django server integrated with a machine learning model to provide optimized product recommendations tailored to user preferences, enhances user experience through intelligent, data-driven suggestions.
A content based movie recommender system using cosine similarity
Self-study NLP project comparing sentiment analysis models and applying segment-level inference with mean probability pooling to mitigate training-test data length mismatches
To associate your repository with the textvectorization topic, visit your repo's landing page and select "manage topics."