Predicted the energy consumed by various appliances using moisture content, temperature and other external conditions given in the data set. We used feature engineering techniques and the LSTM Network for training and got the best results. This project was a part of the DataQuest 2020, which was a Kaggle competition organized by PICT ACM Student Chapter. We achieved a RMSE error of 79.66 and a R2 score of 0.12 and were ranked 2nd out of 50+ participants after the final validation
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Predicted the energy consumed by various appliances using moisture content, temperature & other external conditions given in the data set. We used feature engineering techniques and the LSTM Network for training and got the best results. We were ranked 2nd out of 50+ participants after the final evaluation
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rylp/Appliance_Energy_Prediction
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Predicted the energy consumed by various appliances using moisture content, temperature & other external conditions given in the data set. We used feature engineering techniques and the LSTM Network for training and got the best results. We were ranked 2nd out of 50+ participants after the final evaluation
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