IMDb Score Prediction Analysis

Introduction

This project, led by Obi-Win, aims to predict IMDb ratings of upcoming movies using a comprehensive statistical model. The report outlines the use of a dataset from around 2000 IMDb-listed movies, incorporating various factors like ratings, actors, genres, and more.

Project Objectives

The main goal is to forecast IMDb ratings for twelve highly anticipated movies. The project emphasizes the importance of identifying key variables that significantly influence these ratings.

Methodology

The methodology involved extensive feature engineering and selection. Techniques included constructing running averages for key personnel, standardizing certain variables, focusing on English films primarily from the UK and USA, and applying specific transformations to better represent the data.

Results

The model successfully predicts IMDb ratings for the selected films, indicating a diverse range of audience receptions. Key outcomes include the predictive power of the model, highlighted by its R-squared value and Mean Squared Error.

Usage

This repository serves as a detailed reference for anyone interested in film rating predictions or related data analytics projects.

Contribution

Insights from this analysis are valuable for stakeholders in the film industry, from producers to marketers, offering a data-driven approach to understanding audience reception.

Explore this fascinating journey into predictive film analytics with me here on GitHub.