Final Project for 2010 University of Minnesota Data Analytics Bootcamp IMDb Machine Learning by Jon, Mallory, Habibe and Jag. This project is focused on designing models to predict IMDB movie ratings based on genre, year of release, budget, duration, and director.
https://jagnoor.github.io/IMDB-Machnine-Learning/
We sourced our data from Kaggle.com which featured a set of CSV files with information regarding movies rated by IMDB users dating back from 1906 to 2019.
Link to Dataset: https://www.kaggle.com/stefanoleone992/imdb-extensive-dataset/
- Data Cleaning using python in a jupyter notebook
- Data Visualization using Tableau
- Machine learning models using python and jupyter notebooks: multiple linear regression, deep learning, random forest, and support-vector machines.
movie_cleaning.ipynb was used to complete ETL on IMDB_movies.csv and IMDB_ratings.csv to generate imdb_final.csv. This final CSV is the basis for the data used in machine learning. These files are saved in Resources.
Tableau was used to explore and visualize the data.
The data from imdb_final.csv needed to be preprocessed. First, the categorical columns genre and director were dummy encoded using pandas. Then, that data was reformmated using using label encoder from sklearn.preprocessing,scaled using standard scaler from sklearn.preprocessing, and then to categorical using tensorflow.keras.utils.
Multiple linear regression is not a feasible model for this data. It was attempted in the file: multiple_linear_regression.ipynb
Deep learning is a model that can predict the rating of a movie. It was completed in: deep_learning.ipynb
Random forest is a model that can predict the rating of a movie. It was completed in: rf_model.ipynb
Support-vector machines is a model that can predict the rating of a movie. It was completed in: svm_model.ipynb
