Film Development for PProductions: Predicting IMDb Ratings

Author: Letícia Zorzi Rama

Project Goal

The goal of this project is to perform an analysis on a film database to guide PProductions in deciding which type of film should be developed next by predicting IMDb ratings.

Project Summary

This project is organized into two main parts, implemented in separate Jupyter notebooks:

1. Project Setup, pt. 1 & Exploratory Data Analysis (EDA)

   • Notebook: PProductions_EDA.ipynb

2. Project Setup, pt. 2 & Prediction

   • Notebook: PProductions_Prediction.ipynb

Report notebook

Focuses insights and answers to questions raised in the project.

Datasets

The project uses an enriched version of the IMDb dataset:

1. IMDb Dataset

   • Initial dataset: desafio_indicium_imdb.csv containing general information about films.

2. Data Augmentation

   • The IMDb dataset is enriched with budget and revenue features obtained from The Movie Database (TMDb).
   • The resulting dataset is named imdb_tmdb.csv and serves as the main dataset for both exploratory analysis and prediction.

Project Structure

├── PProductions_EDA.ipynb # Exploratory data analysis
├── PProductions_Prediction.ipynb # IMDb rating prediction
├── Report.ipynb # Focuses insights and answers to questions raised in the project
├── desafio_indicium_imdb.csv # Initial IMDb dataset
├── imdb_tmdb.csv # Enriched dataset used in the project
├── model.pkl # Trained predictive model
├── requirements.txt # dependencies versions
└── README.md # Project documentation

Thank you for taking the time to explore this project. I hope you enjoy reviewing the analysis and insights as much as I enjoyed developing them!

💬 I’d be glad to connect and talk about projects like this — feel free to reach out!