Time Series Forecasting for Retail Sales Optimization Using Walmart Sales Data
Python | SQL | Machine Learning | Time Series Forecasting | Data Preprocessing | Feature Engineering | NeuralProphet | Greykite | Economic Analysis | Pandas | NumPy | Matplotlib | Seaborn | Data Visualization | Regression Analysis | Statistical Modeling | Business Analytics | Inventory and Demand Forecasting
Project Description
In this project, I developed a comprehensive time series forecasting model to predict future sales for Walmart stores using historical sales data. This analysis provided actionable insights for inventory management, sales optimization, and strategic decision-making. By leveraging advanced data science techniques such as time series forecasting, statistical modeling, and machine learning, I analyzed patterns and trends in sales data, incorporating external factors like weather, fuel prices, and holidays. The project utilized the Greykite Python library and Facebook’s NeuralProphet model to build robust forecasting models that can predict demand and optimize operations, ensuring Walmart's profitability during peak seasons and holidays.
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The insights generated from this project could have enabled Walmart to efficiently manage their supply chain, adjust pricing strategies, and optimize inventory levels based on accurate demand forecasts. Predicting sales around holidays and promotions allowed Walmart to maximize profitability while minimizing the risk of overstock or understock situations. By understanding the influence of external factors such as fuel prices, consumer behavior, and economic indicators like CPI and unemployment, this analysis supported data-driven decision-making, resulting in cost savings and improved customer satisfaction.
Project Skills
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Time Series Forecasting: Leveraged advanced techniques to predict future sales using historical data, capturing seasonal trends, and external factors.
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Machine Learning and Neural Networks: Implemented the NeuralProphet model for precise forecasting, improving the accuracy of predictions by integrating various features like markdowns and holiday effects.
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Statistical Analysis: Performed comprehensive data exploration and statistical analysis to understand the distribution of sales, markdowns, and external economic factors.
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Data Cleaning and Preprocessing: Merged multiple datasets, handled missing values, and performed feature engineering to extract meaningful insights from raw data.
Data Visualization: Created visualizations such as heatmaps and line plots to display trends, correlations, and patterns in the data.
Demand Forecasting: Provided Walmart with actionable insights on future demand, enabling them to optimize inventory management and pricing strategies, particularly during peak demand seasons.
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Economic Impact Assessment: Assessed the influence of macroeconomic factors like CPI, unemployment rates, and fuel prices on consumer behavior and store sales, offering strategic recommendations for long-term planning.
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Profitability Optimization: Identified key trends and potential bottlenecks that could affect Walmart's profitability, enabling cost-efficient operations and inventory stocking.
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Risk Mitigation: Developed strategies to mitigate risks associated with overstock and understock situations by forecasting demand based on historical sales patterns and economic factors.
Project Demostrates
This project demonstrates a comprehensive application of data science and business analytics to predict future sales using time series forecasting techniques. By leveraging Walmart's historical store data, I utilized advanced machine learning libraries, such as Greykite and NeuralProphet, to develop predictive models that analyze key factors influencing sales, including temperature, fuel prices, promotional markdowns, and macroeconomic variables like CPI and unemployment rates. The project showcases a deep understanding of time series analysis, data preprocessing, feature engineering, and model development, emphasizing my ability to handle complex datasets, create actionable insights, and drive data-driven decision-making in business contexts. Through this project, I have exhibited proficiency in critical data analytics techniques and demonstrated the use of economic principles to optimize business operations, inventory management, and pricing strategies.