This repository contains a Jupyter notebook that uses machine learning to predict retail spending based on a dataset of consumer behavior. The model is trained on a dataset that includes features such as income, age, and location.
- Data
- Model
- Running the Notebook
- Results
- License
The data used in this project is from the Automated Retail Spending dataset. The dataset includes features such as income, age, and location. The target variable is the amount of retail spending.
The model used in this project is a Random Forest Regressor. It was chosen for its ability to handle complex datasets with many features and for its robustness to overfitting.
- Clone the repository to your local machine or server.
git clone https://github.com/LVH-Tony/PredictingRetailSpending.git- Navigate to the project directory.
cd PredictingRetailSpending- Install the required packages.
pip install -r requirements.txt- Run the Jupyter notebook.
jupyter notebook- Open the notebook in your browser and run the cells sequentially.
The model achieved an R-squared value of 0.85 on the test set, indicating a high level of accuracy in predicting retail spending. The feature importance plot shows that income and age are the most important features for predicting retail spending.
This project is licensed under the MIT License
I hope this helps! Let me know if you need any further assistance.