- Analyzed extensive sales data from 2013, comprising 1559 products across 10 stores in diverse cities, with the objective of constructing a predictive model for sales estimation.
- Utilized Python libraries like pandas, matplotlib, seaborn, and scikit-learn to preprocess, visualize, and analyze the dataset, employing regression algorithms including Linear Regression, Ridge, Lasso, Decision Tree, Random Forest, and Extra Trees to develop robust predictive models.
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Analyzed sales data from 2013 (1559 products, 10 stores, various cities) to build a predictive sales model using Python libraries (pandas, matplotlib, seaborn, scikit-learn) and regression algorithms (Linear Regression, Ridge, Lasso, Decision Tree, Random Forest, Extra Trees).
rohitmore07/Bigmart-Sales-Prediction-Analysis-Regression
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Analyzed sales data from 2013 (1559 products, 10 stores, various cities) to build a predictive sales model using Python libraries (pandas, matplotlib, seaborn, scikit-learn) and regression algorithms (Linear Regression, Ridge, Lasso, Decision Tree, Random Forest, Extra Trees).
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