1. Installation
2. Introduction
3. Project Objective
4. Project Motivation
5. Conclusion
6. Licensing, Authors, and Acknowledgements

The following packages (libraries) need to be installed:

1. pandas
2. NumPy
3. scikit Learn
4. wordcount
5. eli5
6. TFID
7. LGB boost
8. GB regressor

The secondary goal is to practice skills data wrangling, data visualization, Random forest, Linear Regression,LGB boost, GB regressor

This project has 4 high-level steps:

1. Data acquisition which we have extracted for TMDB data set.
2. Data exploratory analysis and features engineering explore and visualize the data
3. Calculate Feature Weight.
4. Modeling experiments to evaluate performance and select machine learning method.
5. final evaluate the model on the validation set using R Square.

1. Drama is the most popular genre, following by action, comedy and thriller.
2. Maximum Number Of Movies Release In year 2013.
3. Avenger', 'Furious7' and 'beauty and the beast' are the most profitable movies.
4. Movie released on 2nd quarter of year has more revenue.
5. Revenue is directly connected to the budget.
6. Movies with higher budgets have shown a corresponding increase in the revenues.

Must give credit to kaggle for providing with data set. I would also like to thank Coursera and Mr Snehan Kekre.

Saphal Adhikari Medium post :https://medium.com/@franticarsenal/how-to-use-machine-learning-approach-to-predict-movie-box-office-revenue-success-e2e688669972?sk=4236202d116bde563c3e75f254c75cca