/Movie-Recommendation-System

Primary LanguageJupyter Notebook

Movie recommendation System Awesome

An advanced Movie Recommendation System.!

Live Demo :

Table of Contents

Description

This is a movie recommendation System using Porter Stemmer , TF-IDF Vectorizer, and text processing.

Features

  • Movie Recommendation system based on Popularity rating, Director similarity
  • Movie Plot Description/Overview based Recommendation.

Examples

Examples Examples

Application Demo

animated

Steps 😍

  • Step 1: Generating a Bag Of Words of teh Search results
  • Step 2: Calculating Word frequency ofeach words (TF-IDF) in the Bag Of Words.
  • Step 3: Calculating Weighted Movie Score.
  • Step 4: Finalizing the results.

Built With

This section should list any major frameworks that we have used to build the application.

Getting Started 🤖

In this section, A whole installation guide is mentioned. also trouble shooting guide is also given.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

  • Python ( Version 3.8.5)
  • Git

Installation

  1. Clone the repo

    git clone https://github.com/Sghosh1999/fuzzy_search.git

    Open Command prompt and navigate to the folder ( fuzzy_search)

    cd fuzzy_search

  2. Install & Create a Virtual Python environment

    python -m pip install virtualenv
python -m virtualenv myenv

  3. Activate the Virtual environment

    myenv\scripts\activate

  4. Install necessary packages

    python -m pip install -r requirements.txt

  5. Check if these two packages are installed or not: (Optional)

    python -m streamlit --version

    If streamlit is not recognized, then run the command (Optional if error)

  python -m pip install streamlit

Running the Application

python -m streamlit run app.py
  • For the first time it will ask you for the email. Please provide the email and the application will be open in your browser.