Recommendation System

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Overview

This recommendation system is designed to suggest relevant items based on a given category and price range. It uses pandas dataframes to filter and select items from various categories.

Files

• Project.ipynb: Contains the Python code for the recommendation system.
• merged_df.csv: CSV file containing the dataset with item details.
• README.md: This README file.

Setup

1. Ensure you have Python installed on your system.

2. Install the required dependencies using pip, or you can refer this link:

   pip install notebook
   

3. clone this repo.

   git clone https://github.com/dkshitij29/recommendation_system.git
   

4. Now navigate to the recommendation_system directory

   cd recommendation_system
   

5. If you have configured the notebook correctly you can run the jupyter notebook with this command.

   jupyter notebook
   

alternatively you could upload this notebook in collab.

Usage

1. Open the Project.ipynb file in a text editor or an notebook or collab.
2. Follow the prompts to enter a category and price range.
3. The system will then suggest relevant items based on the given category and price range.

Example

For testing we included recommendations for "Gift Wrapping Supplies" with a price range of $8.89 ± $5. You would run the script and input the category and price range accordingly.

Notes

• Ensure that the merged_df.csv dataset contains the necessary columns (title, category_name, price, etc.) and is properly formatted.
• The recommendation system uses pandas dataframes for efficient data manipulation and selection.

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Feel free to customize this README file further based on your specific needs or additional information about the recommendation system.

Future Improvements:

• Designing an addon system which can categories the items into parent categories which will act as a input to the system.
• We are specific to one data set in other works we are data specific and data driven, An data independent system can be designed using our design as a base.