https://github.com/chandkund/movie-recommender-system
Unlocking the magic of personalized movie recommendations! This project uses the Cosine Similarity Algorithm to suggest movies based on user-selected titles, making movie discovery more enjoyable.
https://github.com/chandkund/movie-recommender-system
cosine-similarity matplotlib nltk numpy pandas-python seaborn
Last synced: 4 months ago
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Unlocking the magic of personalized movie recommendations! This project uses the Cosine Similarity Algorithm to suggest movies based on user-selected titles, making movie discovery more enjoyable.
- Host: GitHub
- URL: https://github.com/chandkund/movie-recommender-system
- Owner: chandkund
- License: mit
- Created: 2024-12-30T18:44:18.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-30T18:58:47.000Z (6 months ago)
- Last Synced: 2024-12-30T19:35:33.979Z (6 months ago)
- Topics: cosine-similarity, matplotlib, nltk, numpy, pandas-python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🎥 Movie Recommender System
## Overview
This project is a Movie Recommender System that suggests movies to users based on their preferences. Using the cosine similarity algorithm, it identifies and recommends movies similar to a user's selected movie by analyzing their features.
## Project Workflow
1. **Data Exploration**
- Examined and visualized key movie attributes such as genres, ratings, and popularity.
- Ensured data consistency by handling missing values and duplicates.
2. **Data Preprocessing**
- Transformed text-based features (like genres and descriptions) into numerical representations.
- Standardized features for accurate similarity calculations.
3. **Cosine Similarity Algorithm**
- Calculated pairwise similarity scores between movies based on their features.
- Ranked movies to recommend the most relevant ones for a user-selected title.
4. **User Interface**
- Developed an interactive system where users input a movie name to get tailored recommendations.
## Datasets
The dataset includes the following features:
- **Movie Title**: Title of the movie.
- **Genres**: Genres associated with the movie.
- **Description**: A brief synopsis of the movie.
- **IMDB Rating**: Rating of the movie.
- **Popularity**: A score indicating the movie's popularity.
## Installation
1. **Clone the repository**:
```bash
git clone https://github.com/chandkund/MOVIE-RECOMMENDER-SYSTEM
.git
```
2. **Navigate to the project directory**:
```bash
cd MOVIE-RECOMMENDER-SYSTEM
```
3. **Install required packages**:
```bash
pip install -r requirements.txt
```
## Usage
Run the script to get movie recommendations:
```bash
python MOVIE-RECOMMENDER-SYSTEM.py
```
## Results
The recommender system effectively suggests movies that align with user preferences. Using cosine similarity, it ensures accurate and personalized recommendations, enhancing the overall movie discovery experience.
## LICENSE
This project is licensed under the MIT License - see the [LICENSE](LICENSE)file for details.