https://github.com/sanjiban08/movie-recommender-site

Discover your perfect film match with my Movie Recommender Site. My machine learning algorithms analyze your preferences, ensuring each recommendation is tailored just for you.
https://github.com/sanjiban08/movie-recommender-site

bag-of-words jupyter-notebook machine-learning pycharm python3 streamlit vectorization visualization

Last synced: 4 days ago
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Discover your perfect film match with my Movie Recommender Site. My machine learning algorithms analyze your preferences, ensuring each recommendation is tailored just for you.

• Host: GitHub
• URL: https://github.com/sanjiban08/movie-recommender-site
• Owner: Sanjiban08
• Created: 2024-01-18T15:15:21.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-03-04T05:57:31.000Z (10 months ago)
• Last Synced: 2024-10-31T17:44:40.298Z (about 2 months ago)
• Topics: bag-of-words, jupyter-notebook, machine-learning, pycharm, python3, streamlit, vectorization, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 1.59 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Movie Recommender Site

## Overview

Welcome to the Movie Recommender project! This innovative application harnesses the power of machine learning to deliver a personalized movie recommendation experience. This comprehensive README guide will walk you through essential components, including data gathering, data preprocessing in Jupyter, and the seamless creation of the app in Python.

## Data Gathering

A robust recommendation system starts with a diverse and comprehensive dataset. The data gathering process involves meticulously collecting information on movies, genres, and user interactions from reputable sources. This carefully curated dataset forms the backbone for training and testing the machine learning model.

## Data Preprocessing in Jupyter

The heart of the data preprocessing stage beats in Jupyter notebooks, where we sculpt raw data into a masterpiece. These notebooks are where we address missing values, encode categorical variables, and unearth valuable insights through exploratory analysis. The objective is to fashion a well-organized dataset that serves as the foundation for the machine learning model.

## Building the App in Python

The pinnacle of the project is the Python application, seamlessly integrating a trained machine learning model to analyze user preferences and deliver tailored movie suggestions. The user interface is crafted for simplicity and intuitive navigation, ensuring users effortlessly discover personalized movie gems.

### Running the App

To embark on this cinematic journey:

1. Launch the main Python script using `python app.py`.

2. Delve into the application via the provided URL or local server.

## Future Enhancements

This project is a canvas for continuous improvement. Future plans involve integrating additional features, refining the machine learning model, and expanding the dataset. The project thrives on community contributions and feedback, so feel free to explore, modify, and contribute to [Your Movie Recommender]! Let's collectively enhance the art of movie recommendations. Happy watching!