The Movie Recommender System is an application that suggests personalized movie recommendations to users based on their preferences and viewing history. It uses a content-based filtering techniques to generate accurate and relevant movie recommendations.

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.

Content-based Movie Recommender System

The "Bag of Words" (BoW) is a basic and fundamental technique in Natural Language Processing (NLP) for representing text data as numerical features that can be used in machine learning models.

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.

This Repository provides the basic code snippets for all the most widely used ML Algorithms like Supervised, Unsupervised, and Recommender system algorithms.

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.

A content based movie recommender system using cosine similarity

Tex Classification

🧠 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.

This repository houses a comprehensive Machine Learning project aimed at classifying Yelp reviews using Multinomial Naive Bayes and Natural Language Processing (NLP) techniques.