Predicting Movie Success using Machine Learning

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This website features the Final Project - Predicting Movie Success using Machine Learning (of Data Analytics and Visualization Bootcamp provided by University of Minnesota). The Purpose of this project was to showcase our Skills with Machine learning and other tools learned in the bootcamp.

The group consists of Anthony Njuguna, Humera Anjum, Molly Sullivan and Rebecca Tricker.

Project :
This Project utilizes HTML, CSS and Bootstrap along with Python (Jupyter Notebook using pandas, matplotlib, numpy, sklearn, tensorflow and Keras libraries) to analyze movie data whether aspects of a movie can predict their success (good or bad rating score on IMDB). Using the movie data, we created five different Machine Learning models to predict a movie’s success: Logistic Regression, Random Forest, KNN, SVM, and Neural Networks.

We then compared and contrasted the results of these Machine Learning models - Logistic Regression, Random Forest, KNN, SVM, and Neural Networks.

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Machine Learning Models

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