The project focuses on forecasting Walmart's department-wide sales using historical data from 2010 to 2013, incorporating factors like promotional markdowns and economic indicators. Employing Linear Regression, Random Forest, and Support Vector Machine algorithms, the study cleans and prepares the data, emphasizing the significance of holiday periods on sales. Among the models tested, Random Forest outperformed others, showcasing its effectiveness in predicting weekly sales with the lowest Mean Absolute Error, demonstrating the potential for improving inventory management and strategic planning in retail operations.
This historical information includes weekly sales from February 5, 2010, to July 26, 2013. The dataset is from the Kaggle dataset Walmart Dataset - Regression Problem. The dataset contains 6745120 data that have been categorized using 16 variables.
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Data Cleaning
The methodology begins with data cleaning to ensure the integrity and usability of the dataset. The process involves merging features and store data into a single frame, addressing NaN values by filling them with averages for certain variables like Temperature, Fuel Price, and Unemployment, or zeroes for markdowns. This step is crucial for maintaining a robust dataset by removing extreme values and preparing it for analysis and modeling. -
Algorithms
The project utilizes Linear Regression, Random Forest, and Support Vector Machine (SVM) algorithms to forecast sales. Data is divided into training and testing sets, with weekly sales as the target variable. Each algorithm undergoes the same preparation, leveraging sklearn.linear_model for implementation. The quality of splits during sample division is critically evaluated, with the fit method used for training and the pre method for model predictions. -
Evaluation Procedure
Model performance is assessed through hold-out validation, using a pre-split dataset of Walmart's historical sales data into training and test sets. This approach allows for the evaluation of the model's efficacy on unseen data, providing a measure of the model's generalization capability. The primary metric for comparison is the Mean Absolute Error (MAE), which quantifies the average magnitude of errors in predictions, serving as a key indicator of model suitability and effectiveness.
The project's analysis likely highlighted the superior accuracy of the Random Forest model in forecasting Walmart's sales, outperforming Linear Regression and SVM based on Mean Absolute Error metrics. This outcome underscores Random Forest's effectiveness in capturing complex patterns within the retail sales dataset, suggesting its potential for optimizing inventory and marketing strategies.
To address the issue of data discrepancies and further refine the model, future improvements will focus on several areas:
1.Data Preprocessing: Apply advanced anomaly detection to identify and correct discrepancies, enhancing data quality.
2.Feature Engineering: Create sophisticated features, including customer sentiment from social media, to capture deeper insights.
3.Modeling Techniques: Explore deep learning, such as LSTM networks, for capturing complex data patterns.
4.Validation and Optimization: Use cross-validation and systematic hyperparameter tuning to refine model accuracy.
5.External Data: Incorporate broader factors like economic indicators and weather conditions to enrich the model's context.
The project's application encompasses the strategic refinement of retail operational frameworks by leveraging predictive analytics derived from machine learning algorithms. It empowers retailers to proactively manage inventory, tailor marketing campaigns, and streamline supply chain logistics based on anticipated sales trends.
Additionally, this predictive approach facilitates data-driven decision-making, enabling retailers to dynamically adjust to market demands, optimize resource allocation, and enhance the overall customer experience through improved product availability and targeted promotions. By integrating such predictive models, retailers can significantly reduce operational costs, increase revenue through informed stocking decisions, and bolster customer loyalty by meeting consumer needs more effectively.