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MachineHack Retail price forecasting hackathon. The main aim was to apply EDA and Feature engineering and make use of the best possible ML model to predict the retail price based on input features.

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Retail-Price-Forecating

MachineHack Retail price forecasting hackathon

Dataset Description: Train.csv - 284780 rows x 8 columns (Inlcudes UnitPrice Columns as Target) Test.csv - 122049 rows x 7 columns Sample Submission.csv - Please check the Evaluation section for more details on how to generate a valid submission

Attribute Description: Invoice No - Invoice ID, encoded as Label StockCode - Unique code per stock, encoded as Label Description - The Description, encoded as Label Quantity - Quantity purchased InvoiceDate - Date of purchase UnitPrice - The target value, price of every product CustomerID - Unique Identifier for every Customer Country - Country of sales, encoded as Label

Data Link : https://www.machinehack.com/hackathons/retail_price_prediction_mega_hiring_hackathon/data

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MachineHack Retail price forecasting hackathon. The main aim was to apply EDA and Feature engineering and make use of the best possible ML model to predict the retail price based on input features.

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