Movie Recommender

Welcome to the Movie Recommender repository! 🎬📽️ This project explores movie recommendation techniques using data analysis, sentiment classification, and vector search to suggest movies based on user preferences.

📂 Repository Structure

├── data-exploration.ipynb          # Initial data analysis and visualization
├── sentiment-classification.ipynb  # Sentiment analysis on movie reviews
├── vector-search.ipynb             # Movie recommendation using vector similarity
├── README.md                       # Project documentation (this file)

🚀 Project Overview

This project aims to build an effective movie recommendation system using various machine learning and natural language processing techniques:

1. Data Exploration (data-exploration.ipynb)

   • Load and analyze the dataset
   • Perform data cleaning and preprocessing
   • Generate insights from visualizations

2. Sentiment Classification (sentiment-classification.ipynb)

   • Use a pre-trained model from Hugging Face to classify movie plots as emotions
   • Evaluate sentiment impact on recommendations

3. Vector Search (vector-search.ipynb)

   • Convert movie features into vector representations
   • Use similarity measures (e.g., cosine similarity) to find similar movies
   • Generate personalized movie recommendations

🔧 Installation & Setup

To run the notebooks, follow these steps:

1. Clone the repository

   git clone https://github.com/batuhantug/movie-recommender.git
   cd movie-recommender

2. Install dependencies

   pip install -r requirements.txt

   (Ensure you have Python and Jupyter Notebook installed.)

3. Launch Jupyter Notebook

   jupyter notebook

📊 Dataset

The dataset used in this project : https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots

📈 Future Improvements

• Implement deep learning models for recommendation.
• Integrate collaborative filtering techniques.
• Deploy as a web application for user interaction.

📜 License

This project is open-source and available under the MIT License.