This project aims to explore the Big Mart sales dataset to understand factors influencing sales performance and identify potential areas for improvement.
The dataset contains information about various products sold in Big Mart stores, including product details, pricing, store information, and sales data. Data Cleaning: Handled missing values and outliers. Data Transformation: Created new features, such as product category and store type.
Purpose: The @interact function is a powerful tool for creating interactive widgets in Jupyter Notebooks. It allows you to dynamically modify the behavior of your code based on user input, making your analysis more engaging and interactive.
is a powerful tool for data exploration and can help you identify potential issues with your data early on, such as outliers, missing values, and skewed distributions. This information can be used to inform data cleaning and preprocessing steps, as well as feature engineering and model selection.