https://github.com/hallowshaw/market-basket-analysis-using-apriori-algorithm
Market Basket Analysis is a data mining technique used to discover associations between products or items frequently purchased together. This project implements the Apriori Algorithm to perform Market Basket Analysis on a given dataset, identifying rules that indicate relationships between items in a transaction.
https://github.com/hallowshaw/market-basket-analysis-using-apriori-algorithm
Last synced: 3 months ago
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Market Basket Analysis is a data mining technique used to discover associations between products or items frequently purchased together. This project implements the Apriori Algorithm to perform Market Basket Analysis on a given dataset, identifying rules that indicate relationships between items in a transaction.
- Host: GitHub
- URL: https://github.com/hallowshaw/market-basket-analysis-using-apriori-algorithm
- Owner: hallowshaw
- License: mit
- Created: 2023-11-25T07:49:41.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-06T08:05:07.000Z (over 1 year ago)
- Last Synced: 2025-01-05T19:16:11.720Z (5 months ago)
- Language: Jupyter Notebook
- Size: 904 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Market Basket Analysis with Apriori Algorithm
This Python project implements Market Basket Analysis using the Apriori Algorithm. The analysis is performed on a market dataset to discover associations between products purchased by customers. Additionally, the project utilizes the Apyori library for association rule mining and includes data visualization using Seaborn, Matplotlib, and WordCloud.
## Requirements
- Python 3.x
- Pandas
- Matplotlib
- Seaborn
- WordCloud
- Apyori
Install the required libraries using:
```bash
pip install pandas matplotlib seaborn wordcloud apyori
```
### Dataset
The dataset (market_data.csv) should be in the project directory. Ensure it has the necessary transaction data for market basket analysis.
## Usage
1. Clone the repository:
```bash
git clone https://github.com/hallowshaw/market-basket-analysis.git
```
2. Navigate to the project directory:
```bash
cd market-basket-analysis
```
3. Run the script:
```bash
python market_basket_analysis.py
```
Adjust the file path in the script if your dataset is located elsewhere.
## Results
The script outputs association rules based on the specified parameters for minimum support, minimum confidence, minimum lift, and minimum length. Visualizations include a WordCloud showing the most popular items and a Seaborn count plot displaying the frequency of the most popular items.
## Parameters
- **min_support**: Minimum support threshold for itemsets.
- **min_confidence**: Minimum confidence threshold for association rules.
- **min_lift**: Minimum lift threshold for association rules.
- **min_length**: Minimum number of items in an association rule.
## Visualizations
### WordCloud
**Caption:** Most popular items bought first by the customers.
### Frequency of Most Popular Items
**Caption:** Frequency of most popular items.
## Example
```python
association_rules = apriori(records, min_support=0.0045, min_confidence=0.2, min_lift=3, min_length=2)
association_results = list(association_rules)
# Print the first association rule
print("Rule: " + str(list(association_results[0][2][0][0])) + " -> " + str(list(association_results[0][2][0][1])))
print("Support: " + str(association_results[0][1]))
print("Confidence: " + str(association_results[0][2][0][2]))
print("Lift: " + str(association_results[0][2][0][3]))
```
Adjust the parameters based on your dataset and analysis requirements.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- [Apyori Library](https://github.com/ymoch/apyori) for providing a convenient implementation of the Apriori Algorithm.
- [Seaborn Library](https://seaborn.pydata.org/) for data visualization.
- [WordCloud Library](https://github.com/amueller/word_cloud) for creating WordCloud visualizations.