Recommender-Rakshita-102303498 0.2.1

A simple content-based recommender system

Recommender-Rakshita-102303498

Description

Submitted by: Rakshita Garg Roll no: 102303498 Group: 3C0E35

Recommender-Rakshita-102303498 is a Python package that implements a simple content-based recommender system. It recommends items based on feature similarity using machine learning techniques.

This project is mainly intended for academic learning and mini-projects.

Installation

Use the package manager pip to install simple-recommender-rg.

pip install Recommender-Rakshita-102303498

Usage

Enter the CSV filename followed by the .csv extension.

recommend sample.csv

To view usage help, use:

recommend -h

Example

A CSV file containing numeric feature values for different items.

| Item | Feature1 | Feature2 | Feature3 |
|------|----------|----------|----------|
| A    | 10       | 7        | 9        |
| B    | 8        | 6        | 5        |
| C    | 9        | 9        | 8        |

Working

1. The CSV file is read using the pandas library.
2. The first column (item names) and first row (headers) are removed before processing.
3. Feature values are normalized using Min-Max scaling.
4. Cosine similarity is calculated between items.
5. Items are ranked based on similarity scores.

Output Table

| Item | Similarity Score | Rank |
|------|------------------|------|
| A    | 1.000000         | 1    |
| C    | 0.976532         | 2    |
| B    | 0.845210         | 3    |

Other Notes

• The CSV file should not contain categorical (string) values.
• There should be no missing values in the dataset.
• This package is designed for educational purposes.
• The first column and first row are removed automatically before processing.

License

MIT