https://github.com/apfirebolt/movie_recommendation_using_scikitlearn_and_pyqt5
A movie recommendation system built using KNN model from scikit-learn library. GUI components are powered by pyQt5, a library to create GUI applications in Python
https://github.com/apfirebolt/movie_recommendation_using_scikitlearn_and_pyqt5
cosine-similarity jupyter-notebook knn-algorithm movie-recommedation pandas python scikit-learn
Last synced: 3 months ago
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A movie recommendation system built using KNN model from scikit-learn library. GUI components are powered by pyQt5, a library to create GUI applications in Python
- Host: GitHub
- URL: https://github.com/apfirebolt/movie_recommendation_using_scikitlearn_and_pyqt5
- Owner: Apfirebolt
- License: other
- Created: 2025-01-26T09:14:09.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-01-26T19:22:14.000Z (8 months ago)
- Last Synced: 2025-03-02T09:33:49.139Z (7 months ago)
- Topics: cosine-similarity, jupyter-notebook, knn-algorithm, movie-recommedation, pandas, python, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 2.68 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# A GUI application Movie Recommendation App
[](https://scikit-learn.org/)
[](https://en.wikipedia.org/wiki/Machine_learning)
[](https://numpy.org/)
[](https://pandas.pydata.org/)
[](https://www.python.org/)
## Overview
This application provides movie recommendations based on user preferences. It uses machine learning algorithms to analyze user data and suggest movies that the user is likely to enjoy.
## Features
- User-friendly GUI using PyQt5
- Movie recommendations using KNN and Cosine Similarity Metrics
- Search functionality across all 4800 movies
- Detailed movie information
## Screenshots
*Home Screen of the Movie Recommendation App*
*Recommendation Results for a Movie (Spider-Man 3)*
## Algorithms used
### KNN
K-Nearest Neighbors (KNN) is a simple, yet powerful machine learning algorithm used for classification and regression tasks. In this application, we use the KNN algorithm from the `scikit-learn` library to provide movie recommendations.
### How KNN Works
KNN works by finding the `k` nearest data points (neighbors) to a given input and making predictions based on the majority class (for classification) or the average value (for regression) of these neighbors.
### Implementation in scikit-learn
To implement KNN in our application, we use the `KNeighborsClassifier` from `scikit-learn`. Here is a brief overview of the steps involved:
1. **Import the necessary libraries:**
```python
from sklearn.neighbors import KNeighborsClassifier
```
2. **Prepare the data:**
- Split the data into features and labels.
- Normalize the data if necessary.
3. **Initialize the KNN classifier:**
```python
knn = KNeighborsClassifier(n_neighbors=5)
```
4. **Train the classifier:**
```python
knn.fit(X_train, y_train)
```
5. **Make predictions:**
```python
predictions = knn.predict(X_test)
```
By using KNN, our application can effectively recommend movies based on the preferences of similar users.
### Cosine Similarity
Cosine Similarity is a metric used to measure how similar two data points are, irrespective of their size. It is often used in text analysis and recommendation systems to find similarities between items.
### How Cosine Similarity Works
Cosine Similarity calculates the cosine of the angle between two vectors in a multi-dimensional space. The cosine value ranges from -1 to 1, where 1 indicates that the vectors are identical, 0 indicates orthogonality (no similarity), and -1 indicates complete dissimilarity.
### Implementation in scikit-learn
To implement Cosine Similarity in our application, we use the `cosine_similarity` function from `scikit-learn`. Here is a brief overview of the steps involved:
1. **Import the necessary library:**
```python
from sklearn.metrics.pairwise import cosine_similarity
```
2. **Prepare the data:**
- Convert the data into a suitable format, such as a term-document matrix for text data.
3. **Calculate Cosine Similarity:**
```python
similarity_matrix = cosine_similarity(data_matrix)
```
4. **Use the similarity matrix:**
- The similarity matrix can be used to find the most similar items to a given item, which can then be recommended to the user.
By using Cosine Similarity, our application can effectively recommend movies that are similar to the ones the user has liked in the past.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/Apfirebolt/movie_recommendation_using_scikitlearn_and_pyqt5
```
2. Navigate to the project directory:
```bash
cd movie_recommendation
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Run the application:
```bash
python main.py
```
2. Interact with the GUI to get movie recommendations.
## Contributing
Contributions are welcome! Please fork the repository and submit a pull request.
## License
This project is licensed under the MIT License.