MLND-Capstone

Machine Learning Engineering Nano Degree Capstone project

TMDB Movie Revenue Prediction

Project Overview

We live in a movie obsessed world, where movies made an estimated $41.7 billion in 2018, if we include home entertainment revenue global film industry is worth $136 billion. The film industry is more popular than ever. But what movies make the most money at the box office? How much does a director matter? Or the budget? For some movies, it's "You had me at 'Hello.'" For others, the trailer falls short of expectations and you think "What we have here is a failure to communicate."

Objective Objective of this project is to predict a movie revenue based on historic data about movie revenues and performance at the global box office. Such a prediction would be useful for optimization in many areas during various stages of planning and production of movies, for instance, selection of actors, crew, location, production spend, marketing spend, logistics and so on.

Data

Data for this project can be obtained from https://www.kaggle.com/c/tmdb-box-office-prediction/data Make sure data is stored in data folder

Project Instructions

Clone the repository and navigate to the downloaded folder.

git clone https://github.com/srinivascreddy/MLND-Capstone.git
cd MLND-Capstone.git

Create and activate a new environment.

conda create -n capstone python=3.7 
source activate capstone

Install required packages from requirements file
```
pip install -r requirements.txt 
```
Open the notebook.
```
jupyter notebook TMDB_Capstone.ipynb
```

Techniques used

This project uses blended and stacked ensemble learning techniques. Dive right in if this is of interest!

Outputs

Output predictions are stored in output folder