Scrapy project for scraping data from IMDB with Movie Dataset including 58,623 movies' data.

This repository contains the codebase for MovieCLIP: Visual Scene Recognition in Movies

This is a Movie Recommendation System that uses collaborative filtering techniques to provide personalized movie recommendations based on user ratings and preferences. The system also includes data cleaning, preprocessing, and exploratory data analysis (EDA) to gain insights into the dataset.

An interactive web app-based data dashboard displaying intuitive graphs, charts, and filters, for users to explore a wealth of information about movies, from box office earnings and critical ratings to genre trends built using JavaScript and Flask

The project creates a data analysis with R programming to determine if a film's IMDB ratings, directors, actors, genres, metascores, runtimes, and revenue affect the success of a movie and create bias reviews. The project further explores how IMDB ratings and metascores affect and correlate with each other. The raw dataset, IMDB_movies.csv, was …

Movie Recommendation System using Python, Machine learning and Streamlit

Movie Recommender System developed using Streamlit, Python, and The Movie Database (OMDb) API. It recommends movies based on similarity scores between movies.

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Movie Recommendation based on movie plot using LDA Topic Modelling

Movie dataset visualization

This notebook contains the sentimental analysis of the movie reviews on imdb.