Movie Rating Prediction

Overview

This project is focused on building a predictive model to estimate movie ratings based on attributes like genre, director, actors, duration, and more. The dataset used is from IMDb (Indian Movies).

Features:

• Data Preprocessing (handling missing values, encoding categorical variables)
• Feature Engineering (e.g., director's average rating, movie age)
• Predictive Model (Random Forest Regressor)

Project Structure

• data/: Contains the dataset (e.g., IMDb_Movies.csv)
• notebooks/: Jupyter notebooks with code for data preprocessing, model building, and evaluation
• scripts/: Python scripts with helper functions
• requirements.txt: List of libraries required for the project
• README.md: This file

Dependencies

This project requires the following libraries:

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

You can install them using: pip install -r requirements.txt

Running the Project

1. Download the dataset (available here).
2. Load the dataset using the Jupyter notebook or Google Collab movie_rating_model.ipynb.
3. Follow the steps in the notebook to preprocess the data, build the model, and evaluate performance.

Evaluation Metrics

• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R2 Score