Metadata-Version: 2.4
Name: Recommender-Rakshita-102303498
Version: 0.2.1
Summary: A simple content-based recommender system
Author: Rakshita Garg
License: MIT License
        
        Copyright (c) 2026 Rakshita Garg
        
        Permission is hereby granted, free of charge, to any person obtaining a 
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        of this software and associated documentation files (the "Software"), 
        to deal
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        rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or 
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        in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS 
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        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING 
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS 
        IN THE
        SOFTWAREMIT 
        License
        
        Copyright (c) 2026 Rakshita Garg
        
        Permission is hereby granted, free of charge, to any person obtaining a 
        copy
        of this software and associated documentation files (the "Software"), to 
        deal
        in the Software without restriction, including without limitation the 
        rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        MIT License
        
        Copyright (c) 2026 Rakshita Garg
        
        Permission is hereby granted, free of charge, to any person obtaining a 
        copy
        of this software and associated documentation files (the "Software"), to 
        deal
        in the Software without restriction, including without limitation the 
        rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in 
        all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL 
        THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING 
        FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 
        THE
        SOFTWARE.
        
        
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: scikit-learn

# 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.
```bash
pip install Recommender-Rakshita-102303498
```


## Usage
Enter the CSV filename followed by the `.csv` extension.

```bash
recommend sample.csv
```

To view usage help, use:

```bash
recommend -h
```


## Example

A CSV file containing numeric feature values for different items.

```bash
| 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

```bash
| 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 
