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Related Issue
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[ *] CSSOC 2024 Participant
[ *] Contributor
Closes: Supply Chain Demand Forecasting #638
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This project focuses on analyzing and predicting sales trends using an Amazon sales dataset. The analysis includes data cleaning, preprocessing, exploratory data analysis (EDA), and building predictive models. By leveraging tools such as pandas, matplotlib, and XGBoost, the project aims to uncover insights into sales patterns and develop models to forecast future sales performance.
Key steps include:
Data Cleaning: Removing irrelevant columns, handling missing data, and converting dates to a usable format.
Feature Engineering: Extracting time-based features like year, month, and weekday, and encoding categorical variables.
Exploratory Data Analysis (EDA): Visualizing sales trends over time and decomposing the time series to identify seasonal patterns.
Modeling: Building and evaluating predictive models using XGBoost and assessing their accuracy with various metrics.
How Has This Been Tested?
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Checklist: