DataRenaissance - Unilever Data Science POC Use Case - TH Deep Tech/Machine Learning Theme EME

[gauravkantrod]

DataRenaissance - Unilever Data Science POC Use Case - TH Deep Tech/Machine Learning Theme EME

ℹ️ Project information

1. Deep Tech or Machine Learning - Unilever Data Science POC Use Case

2. Project Name: Sales Predictions

3. Short Project Description: Predicting Sales using Machine Learning

4. Team Name: DataRenaissance

5. Team Members: Gaurav Kantrod https://github.com/gauravkantrod

6. Demo Link: NA

7. Repository Link(s): https://github.com/gauravkantrod/Skillenza-MishMash-MachineLearning

8. Presentation Link: NA

9. Deep Tech - Problem Statement - 3: Yes, Submitted both dataset solution

10. Deep Tech - Problem Statement - 2: NA

11. Azure Services Used- Kindly mention the Azure Services used in your project. Azure services I would use are deployment, testing, monitoring and chatbot services

🔥 Your Pitch

First I have extracted some faetures such as Year and period. For imputation I have used mean values for the feature. I have used regression models such Linear Regression, Ridge, Lasso, RandomForest and XGBoost Regressor. In case of Lasso there were faetures which were of no importance and their coefficients were reduced to zero. Thus implies dimensionality reduction and much simple model. Lastly I have tested the model on Ridge which gave best MAPE for the given dataset and for test data I got MAPE of 14.34

Data_Renaissance_MishMash_Gaurav_Kantrod.zip

🔦 Any other specific thing you want to highlight?

(Optional) I have made a dataframe for LASSO which have some coefficients equal to 0 which show the feature selection technique.

✅ Checklist

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