Have you ever watched a movie and ended up disappointed with the outcome? Our project focused on designing models to predict IMDB movie ratings based on genre, year of release, budget, duration, and director.
For this project, we used various models to determine the best method for predicting the IMDB rating of a movie based on historical data. In order to conduct this predictive modeling endeavor, 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/
Prototype/Inspirations: https://netflix.com
Heroku app: https://clarke-imdb.herokuapp.com/
- Data Cleaning
- Data Visualization
- Python
- Pandas
- Jupyter
- Javascript
- D3
- HTML
- CSS
- Machine Learning Models:
- Support Vector Model
- Deep Learning
- Logistic Regression
- Random Forest
- DataTables
- Flask
- PostgreSQL
Our site was designed using HTML and CSS. We wanted our site to be easily navigated, and fun to use. We highlighted important elements on our homepage, and showed our project process through navigational links.
The directors we have shown are the more popular directors with the most votes and data. We used directors as one of our elements for the prediction models as well. Here we are showing the director data comparison of gender vs rating and budget vs rating.
For gender vs rating showing green from 0-7.3 and blue 7.4 to 8. For these particular directors we noticed that all of them are male and the male gender voted for highest rated directors. But on the far left side of the gender vs rating we have Martin Scorsese, who is known for The Godfather has the highest rating votes but not the highest budget for movies.
From the budget vs rating dashboard we see Anthony Russo known for The Avengers and Captain America is showing the highest avg budget of 230 million with an average vote of 8 but he's not the highest rated director. So we see just because some one has a higher budget doesn't mean they are the most popular voted director.
Given the analysis that we did in Tableau, we decided to predict the movie’s rating class based on release year, duration, budget, genre, and director. We started with a linear regression model and elasticnet, but the accuracy was around 10%. So we figured that no amount of hyper-parameter tuning would get us much higher than that. For some models we tried only using the numerical features (year, duration, and budget) but the results were not better so we decided to move forward with both numerical and categorical data.
For the SVM we cleaned the data with label and dummy encoder to get a better accuracy for our target rating class for bad excellent and good. We have the SVM training accuracy at 0.91% and SVM testing accuracy at 0.64%.
