gopiashokan/Retail-Sales-Analysis-and-Forecast-using-Machine-Learning
Build a machine learning model to predict weekly sales with 97.4% accuracy. Integrated Exploratory Data Analysis tools to analyze trends, patterns, and actionable insights. The solution enables detailed sales comparisons, evaluates feature impacts and ranges, and identifies top performers, greatly enhancing decision-making in the retail industries.
This project helps retail managers, store owners, and department heads predict weekly sales for their stores and departments. You provide your historical sales data, including store details, sales figures, and environmental factors like holidays, temperature, and fuel prices. In return, you get accurate weekly sales forecasts and interactive analyses to understand sales trends, top performers, and the impact of various factors on sales.
No commits in the last 6 months.
Use this if you need to accurately forecast retail sales, understand which stores and departments are top performers, and analyze how different factors like holidays or fuel prices influence your sales.
Not ideal if you're looking for a simple, off-the-shelf business intelligence dashboard without the need for predictive modeling, or if your data isn't structured for detailed store and department-level analysis.
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14
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6
Language
Jupyter Notebook
License
MIT
Category
Last pushed
May 09, 2024
Commits (30d)
0
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