Dumb_Terminals - EQ Forecast - Deep Tech/Machine Learning

[G-Slient]

Dumb_Terminals - EQ Forecast - Deep Tech/Machine Learning

ℹ️ Project information

1. Deep Tech or Machine Learning is sponsored by UNILEVER. Problem Statement 3 - Data Science POC Use Case

2. Project Name: EQ Forecast

3. Short Project Description: Machine Learning Approach to Forecast the EQ Sales of a Company.

4. Team Name: Dumb_Terminals

5. Team Members:

• G Kranthi Kiran - kranthik13
• G Mothy - G-Slient
• Sudhanshu Joshi - SJoshi7

6. Demo Link: NA

7. Repository Link(s):
   https://github.com/G-Slient/MishMash-Online-Hackathon-PAN-INDIA.git

8. Presentation Link:
   https://docs.google.com/presentation/d/1rqTu922RLXNbTivpdRjWCFsGxpV0fYUX2t2d1qvzhzY/edit?usp=sharing

9. Deep Tech - Problem Statement - 3:

   • We have provided both the models.

🔥 Your Pitch

1. We have experimented with almost all the models and picked the best model i.e XG Boost.
2. Forecasting was applied on both the datasets.
3. We have created a baseline of all the models.
4. In our approach, we have used the aggregated train data for building models, which gave better results than the non-agrregated data.
5. Bayesian Approach was also used for forecast the data for both the datasets.

🔦 Any other specific thing you want to highlight?

ML Approach

Dataset	Train	Valid	Test
First	6.71	20.16	35.69
Second	21.14	22.67	19.76

Bayesian Approach Results

Dataset	Train	Valid	Test
First	29.09	29.42	153.9
Second	7.970	88.45	49.97

✅ Checklist

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• [x] You have provided all the information correctly.

[G-Slient]

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