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Movie Recommendation App

Overview

This is a Python-based web application that provides various movie-related functionalities. It is built using Streamlit as the front-end framework and leverages several Python libraries to deliver a seamless movie selection and recommendation experience.

Key Features

  1. Movie Selection: You can search for any movie that exists using this feature. It utilizes an API call system to fetch movie details.

  2. Movie Recommender: The application provides movie recommendations based on a dataset from Kaggle. It recommends existing Netflix movies based on user preferences.

Installation

  1. Clone this repository to your local machine:

    git clone https://github.com/yourusername/movie-recommendation-app.git
  2. Navigate to the project directory:

    cd movie-recommendation-app
  3. Install the required Python packages:

    pip install -r requirements.txt

Usage

  1. Run the Streamlit app:

    streamlit run app.py
  2. Open your web browser and visit http://localhost:8501 to access the Movie Recommendation App.

Dependencies

The project uses the following Python libraries:

  • Streamlit: For creating the web application interface.
  • Pandas: For data manipulation and analysis.
  • Requests: For making API requests.
  • Scikit-learn (Sklearn): For machine learning and recommendation algorithms.
  • Plotly: For interactive data visualization.

Data Sources

  • Movie data for the "Movie Selection" feature is obtained through API calls (ThemovieDB).
  • Movie recommendation data is sourced from Kaggle (Netflix Movies 2019).

Screenshots

1.Homepage

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2.Movie Recommendation

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3.Movie Selection

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Python recommendation system with API, UI - Streamlit

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