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Supply chain demand forecasting #641

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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?

Describe how it has been tested
Describe how have you verified the changes made

Checklist:

  • [* ] My code follows the guidelines of this project.
  • [ *] I have performed a self-review of my own code.
  • [ *] I have commented my code, particularly wherever it was hard to understand.
  • [ *] I have made corresponding changes to the documentation.
  • [ *] My changes generate no new warnings.
  • [ *] I have added things that prove my fix is effective or that my feature works.
  • [ *] Any dependent changes have been merged and published in downstream modules.

@prathimacode-hub
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prathimacode-hub commented Aug 15, 2024

Each PR should be independent. 1 PR equal to 1 Problem Statement. Never mix all the solutions together. What algorithms have you used fie supply chain demand forecasting? @surya-moorthy

@surya-moorthy
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I used xgboost and another one which is a hybrid approach combining ARIMA (AutoRegressive Integrated Moving Average) and Linear Regression.

@surya-moorthy
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surya-moorthy commented Aug 16, 2024

I can't able to solve #183 the datasets are huge , I did ask to assign it for me but I tried and I failed so can you unassign it

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