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
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!!
In the 'MOVICO' Directory
there are several files:
-
Project Python Code File-
MOVICO.ipynb
-
Dataset Files-
movies.csv
,ratings.csv
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
- Python
- Basic understanding of AI-ML algorithms
- KNN
- SVD
- Anaconda
- Jupyter notebook
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scipy
- Datetime
- Re
- Sklearn
- Ipywidgets
- IPython
- Surprise
To run MOVICO, follow these simple steps:
Clone > Launch > Navigate > Open > Run-all > MOVICO Specific Instructions > Outputs > CLOSE
- Clone the
'Movie-Recommendation-System-MOVICO'
github repository.
git clone https://github.com/ankitacoder3/Movie-Recommendation-System-MOVICO.git
- Launch
Jupyter Notebook
on your system, using Anaconda.
- Navigate to the
'MOVICO'
Directory in that.
cd Movie-Recommendation-System-MOVICO
cd MOVICO
- Open the
MOVICO.ipynb
file in Jupter Notebook.
-
Run-all cells,
by clicking on the
">>"
(fast forward) option in thetoolbar
,or the
"Restart & Run All Cells"
option from the"Kernel"
menu.This shall execute all the cells in the notebook.
-
MOVICO Specific Instructions:
-
a] In the cell number
60
,you can enter
any number from 1 to 9
forboth
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 clickenter
.-
Enter any movie name (say, 'Toy Story'), and press enter.
-
This shall display
personalized movie recommendations
.
-
-
-
Outputs: will be displayed after all the cells have ran.
These shall include
personalized movie recommendations
,evaluation
anderror tracking
based on your inputs.
There are 3 models used in MOVICO:
-
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
orMovie Recommendation System
could also be used as anAI-ML Project
, for courses likeMachine Intelligence Project
, or specifically as a project for the coursesUE20CS302
or ue20cs302.
Thank you for exploring the MOVICO project. Happy movie recommending, evaluating and watching! 🍿🎬