Ram1103/Movie_Recommender

Python recommendation system with API, UI - Streamlit

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

2.Movie Recommendation

3.Movie Selection