Movie Rating Prediction Model

Overview

This project aims to predict movie ratings based on various features using machine learning techniques. The model is trained on a dataset containing information about movies, including their genres, release year, and other relevant attributes. It is then deployed using a web application built with Streamlit, allowing users to input movie attributes and receive predicted ratings.

App link

Movie Rating Prediction App Link

Features

• Data: The dataset consists of [insert number] movies with [insert number] features, including:

  • Genre
  • Number of Genres
  • Release Year
  • Number of Votes

• Model: Different machine learning models were trained on the dataset to predict movie ratings.

  • Models tested: Linear Regression, Random Forest Regression, Gradient Boosting Regression, XGBoost Regressor, etc.

• Pickle File: A serialized version of the trained model is saved in a pickle file, allowing for efficient deployment.

• Streamlit App: A user-friendly web interface built with Streamlit to interact with the model, input movie attributes, and receive predicted ratings.

Prerequisites

Before starting the project, make sure you have the following:

• Python 3.x
• scikit-learn
• pandas
• numpy
• Streamlit
• Pickle

Model Architecture

1. Data Preprocessing

• Feature Engineering: Creating new features and transforming raw data into meaningful inputs.
• Normalization/Standardization: Scaling numerical features for improved model performance.
• Encoding Categorical Features: Converting categorical data into numerical format for model compatibility.

2. Model Selection

• Experimenting with various machine learning algorithms such as:
  • Linear Regression
  • Random Forest Regression
  • Gradient Boosting Regression
  • XGBoost Regressor

3. Model Training

• Data Split: Dividing the dataset into training and testing sets to evaluate model performance.
• Training: Training the selected model(s) on the training set.
• Evaluation: Using metrics like RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error) to assess model performance on the testing set.

4. Handling Outliers

• Capping: Identifying and capping outliers in numerical features to reduce their impact on the model's accuracy.

Deployment

To deploy the model and make it available on the web:

1. Create a GitHub Repository: Push all the project files (code, model, etc.) to GitHub.
2. Set up Cloud Platform Account: Use platforms like Streamlit Sharing, Heroku, or AWS to deploy the application.
3. Configure the Streamlit App: Ensure that the app is correctly configured for cloud deployment.

Contributions

Contributions are welcome! Feel free to:

• Improve model accuracy.
• Add new features to enhance the model's predictive capabilities.
• Enhance the Streamlit web app to improve user experience.