The Walmart Sales Prediction Project aims to forecast the sales of different Walmart stores and departments, taking into account various influencing factors such as holidays, economic conditions, and store size.

The project utilizes a range of data analysis and machine learning techniques, including:

• Data Preprocessing: Handling missing values, feature engineering, and data normalization to prepare the dataset for modeling.
• Exploratory Data Analysis (EDA): Applying statistical analysis and visualization techniques to understand trends and patterns within the data.
• Predictive Modeling: Implementing machine learning algorithms like linear regression, decision trees, random forest, and gradient boosting (e.g., XGBoost) to predict future sales.
• Model Evaluation: Using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score for model performance assessment.
• Tools and Libraries: Python is used as the primary programming language, with libraries such as Pandas for data manipulation, Matplotlib and Seaborn for data visualization, and Scikit-Learn for machine learning applications.

This README outlines the objectives, methodologies, and tools used in the project.