Build a model that predicts the rating of a movie based on features like genre, director, and actors. You can use regression techniques to tackle this problem. The goal is to analyze historical movie data and develop a model that accurately estimates the rating given to a movie by users or critics.

Movie Website built on python Django framework; Uses Content Based Predictive Model approach to predict similar movies based on the contents/genres similarities

A multi-modal deep learning model trained to predict a movie's genre given the movie poster and overview as an input.

Movie Revenue Prediction System predicts the revenue of a movie with 14 parameters: name, rating, genre, year, released, score, votes, director, writer, star, country, budget, company and runtime using gradient boosting______________________________ Training Accuracy: 91.58%____________ Testing Accuracy: 82.42%.

A model that predicts movie box office revenue using historical data and features like cast, genre, release date.

Genre Genie - Movie Genre Predictor (multi-label classification with NLP)

MovieIQ is a Streamlit-based interactive dashboard that predicts movie success using data-driven insights. It combines data cleaning, visualization, statistical tests, and a Random Forest model to analyze how factors like budget, genre, and ratings influence box office performance.

Predicting Movie Genres

Multiclass Text Classification problem where I used movie plot summaries to predict movie genres

This project predicts movie ratings using machine learning by analyzing features like director, genre, actor, and votes. It includes data cleaning, feature engineering, model training, evaluation, and visualization.

Using a 7-layer mini-VGG styled convolutional neural net to predict a movie's genres based only on its poster.

text analysis of movie synopsis to predict the films genres (multilabel learning)

This project aims at determinig the genre of the movie using its posters for image classifications CNNs are the most effective types of neural network in this project we try to create a CNN which would predict the genres of these movies.

The goal of this project is to predict movie ratings based on features like genre, director, and leading actors. The project uses Linear Regression, a machine learning technique, to create a predictive model that estimates movie ratings.

It is a model that predicts the rating of a movie based on features like genre, director, and actors. Use regression techniques to tackle this problem.

Welcome to the Movie Genre Classification repository! This project focuses on classifying movies into their respective genres using machine learning techniques. By leveraging natural language processing (NLP) and advanced machine learning models, we aim to accurately predict the genre of a movie based on its synopsis and other relevant features.

Predicting genre of movies based on the summary/plot.

Predicting movie genre based on a model using movie posters and other movie metadata.

Predicting Movie Genre Based on Description, Poster, Title and etc.

Machine learning algorithm to predict the genre of a movie based on a short storyline.

Movie Genre Classification using Multinomial Naive Bayes and n-grams involves predicting the genre of a movie based on its plot summary using a probabilistic model that handles text data and assumes independence among features within each genre category.

This project is designed to predict the genre of the movie to give a basic idea of the movie whether the is related to comedy, action, horror, or drama. The web application is developed using Natural Language Processing by implement Bag of words model and some vectorization techniques to predict the genre

A demonstration of an IoT project using AWS. I use data from the IMDB to predict the best combination of director, genre, and year for a new movie.

This project predicts movie ratings using Machine Learning based on features such as genre, director, and actors. The goal is to analyze how different factors influence a movie’s rating and build a model that can estimate ratings for new movies.

A regression system was designed that predicts the IMDb rating of a movie. A movie viewer would otherwise have to rely on a critic's review or self-instincts. A dataset, obtained from Kaggle, contains certain attributes (such as genre, duration, names of actor, director, number of voters for the rating, plot and keywords, language, etc.) and performance rating of over 5000 movies. On observation, the dataset is preprocessed in order to fill missing data and convert categorical features to numerical. Several hypotheses are selected and a few not chosen due to unfavorable conditions observed. A 5-fold cross-validation on the hypotheses set exhaustively searches for the most optimum hyperparameters of the model with the least MSE and helps curtail overfitting. Random forest regressor is chosen due to its low MSE and simplicity. The model is retrained and gives a better coefficient of determination on the test set when compared to previous techniques on Kaggle. Software: Python with use of Scikit-learn library

Natural language processing (NLP) group project to create a multi-label classifier for predicting genre(s) given an IMDb movie description

A data science project that uses machine learning to predict genres of movies

Training a transformer model to predict movie genres

Use CNNs to predict movie genres and regression models to predict box office.

Repository for a Machine Learning model to predict movie genres based on poster images.