Movie-Recommendation-System-MOVICO

The repo Movie-Recommendation-System-MOVICO contains the AI-ML Project, namely MOVICO. It is a 'Movie Recommendation System' that mainly uses 'Collaborative Filtering Techniques'.

The project Movie-Recommendation-System-MOVICO was created as a project for the Machine Intelligence Course , which was part of the course UE20CS302.

Table of Contents

Introduction

Prerequisites and Techstack

Steps for Execution

Sample Screenshots

Usage

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Introduction

The repo Movie-Recommendation-System-MOVICO contains the project MOVICO.

MOVICO is a MOVIe recommendation system, and it mainly focuses and utilizes COllaborative filtering techniques.

The name MOVICO originates from the fusion of MOVIe COllaborative (i.e., MOVI-CO), encapsulating the essence of Collaborative Movie Recommendations with precision and accuracy.

The various collaborative filtering techniques utilized are KNN, SVD, etc...

Welcome to MOVICO!!

Files :

In the 'MOVICO' Directory there are several files:

• Project Python Code File- MOVICO.ipynb

• Dataset Files- movies.csv, ratings.csv

Repository Structure :

MOVICO repo structure click...

Below is the structure of the MOVICO project repository

  Movie-Recommendation-System-MOVICO/
  ├── MOVICO/             # Project Folder             
  │   ├── MOVICO.ipynb    # Code file
  │   └── dataset/        # Dataset Folder 
  │        ├── movies.csv      
  │        └── ratings.csv       
  └─── README.md           # Repository README
  

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Prerequisites and Techstack

Language:

• Python

Prerequisites:

• Basic understanding of AI-ML algorithms
• KNN
• SVD

Other Tools:

• Anaconda
• Jupyter notebook

Python Modules:

• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scipy
• Datetime
• Re
• Sklearn
• Ipywidgets
• IPython
• Surprise

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Steps for Execution

To run MOVICO, follow these simple steps:
Clone > Launch > Navigate > Open > Run-all > MOVICO Specific Instructions > Outputs > CLOSE

1. Clone the 'Movie-Recommendation-System-MOVICO' github repository.

git clone https://github.com/ankitacoder3/Movie-Recommendation-System-MOVICO.git

2. Launch Jupyter Notebook on your system, using Anaconda.

3. Navigate to the 'MOVICO' Directory in that.

cd Movie-Recommendation-System-MOVICO
cd MOVICO

4. Open the MOVICO.ipynb file in Jupter Notebook.

5. Run-all cells,

   by clicking on the ">>" (fast forward) option in the toolbar,

   or the "Restart & Run All Cells" option from the "Kernel" menu.

   This shall execute all the cells in the notebook.

6. MOVICO Specific Instructions:

   • a] In the cell number 60,

     you can enter any number from 1 to 9 for both the inputs.

     • Enter the number of movies you would love to watch from the list of recommendations. Enter any number from 1 to 9 (say, 6)

     • Enter the number of movies from the list of recommendations that you would say are irrelevant to your taste. Enter any number from 1 to 9 (say, 5).

     • These can be used for fine-tuning models too.

   • b] In the cell number 53 ,

     you can enter the name of a movie in the widget, and click enter.

     • Enter any movie name (say, 'Toy Story'), and press enter.

     • This shall display personalized movie recommendations.

   
   

7. Outputs: will be displayed after all the cells have ran.

   These shall include personalized movie recommendations, evaluation and error tracking based on your inputs.

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Sample Screenshots

There are 3 models used in MOVICO:

• MODEL1 : USER-BASED COLLABORATIVE FILTERING

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  • 

• MODEL2 : KNN-BASED COLLABORATIVE FILTERING

  • 
  • 

• MODEL3 : SVD-BASED COLLABORATIVE FILTERING

  • 
  • 

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Usage

• MOVICO can be used to recommend movies to users, based on collaborative filtering techniques .

• MOVICO outputs personalized movie recommendations based on users inputs.

• MOVICO also evaluates the recommendations received, from the recommendation models.

• More effective recommendation systems can be built using MOVICO.

• The project MOVICO or Movie Recommendation System could also be used as an AI-ML Project, for courses like Machine Intelligence Project, or specifically as a project for the courses UE20CS302 or ue20cs302.

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Thank you for exploring the MOVICO project. Happy movie recommending, evaluating and watching! 🍿🎬