In this project, we aim to predict the sales of Walmart stores. By applying data preprocessing techniques, conducting exploratory data analysis, and building regression models, we strive to create accurate predictions that can assist in decision-making and planning.

The dataset used in this project contains sales data from various Walmart stores. It includes features such as store information, date, and other relevant variables. As a preprocessing step, outliers were removed from the data to ensure the quality and reliability of the analysis.

During the exploratory data analysis phase, we delved into the dataset through both univariate and bivariate analysis. We visualized distributions, relationships between variables, and identified patterns that could provide insights for modeling.

We implemented several regression models to predict Walmart sales:

• Linear Regression
• Ridge Regression
• K-Nearest Neighbors (KNN)
• Decision Tree
• Random Forest
• XGBoost

Each model was trained and evaluated to determine its performance in predicting sales accurately.