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.

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

A content based movie recommender system using cosine similarity

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.

Content-based Movie Recommender System

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.

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.