Phone Recommendation System

This notebook demonstrates a machine learning project to build a phone recommendation system using a dataset of items and reviews. We go through the steps of loading the data, preprocessing, building the recommendation system, and evaluating the results.

Dataset

• Name: items.csv and reviews.csv
• Description: The dataset contains information about phone items and user reviews. The goal is to recommend phones based on content and collaborative filtering.

Project Steps

1. Data Preprocessing: Handle missing values and combine text features for better representation.
2. Feature Extraction: Convert text data into numerical features using TF-IDF Vectorization.
3. Compute Cosine Similarity: Measure the similarity between items using cosine similarity.
4. Build the Recommendation System: Develop a content-based recommendation system.
   • Algorithms Used: TF-IDF, Cosine Similarity
5. Hybrid Recommendation System: Combine content-based and collaborative filtering methods.
6. Evaluation: Evaluate the recommendation system using precision and recall.
   • Evaluation Metrics: Precision, Recall

Installation

To get started with this project, clone the repository and install the required dependencies:

git clone https://github.com/mohammad007kh/Phone-Recommendation-System.git
cd Phone-Recommendation-System
pip install -r requirements.txt

Contributing

Contributions are welcome! Please note that this project is still a work in progress and may not provide the expected outputs yet. Feel free to open an issue or submit a pull request.

License

This repository is licensed under the MIT License. See the LICENSE file for more information.