This is scarlet, a data science project! I downloaded data from a UK retailer that had over a million data points. These data points contained data regarding the sizes of baskets, products being sold, data of sale, and how much of any given product was being sold. During this project, I wanted to see if data science and machine learning models could accurately predict inventory for the retailer. Could these models help in telling the retailer which products to stock up on depending on the time period among other interesting questions regarding buyer's behavior
- Visualizations: Matplotlib and Seaborn
- Data storage: Pandas and Numpy
- Training models: SciKit-Learn
- Processes: Data imputation/cleaning, Data visualization, Exploratory data analysis, Cross validation, Grid Search
- Models: Time series (ARMA/ARIMA), KNN modeling, Linear Regression, Polynomial regression, Decision trees, Lasso regularization, Boosting and ensemble models.