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This predicts the future energy demand by using a Multivariate LSTM (Long Short Term Memory) Model i.e. (a kind of Recurrent Neural Network) by considering environmental & time series factors.
This is the Machine Learning(ML) based System which predicts the possibility of a heart attack based on the previous datasets which consists the following variables such as age ,sex ,blood pressure ,cholesterol ,obesity ,etc. The exploratory data analysis of these parameters plays a vital role in the precision of the model.
60-70% of the time ML model building workflow is consumed in data preparation. Aim of this repo is to get an introduction to how can you clean and transform your raw data.
This repo showcases a self-guided project that explores and analyzes the Nobel Prize Database using Python's data analysis and visualization libraries such as NumPy, Seaborn, and Pandas.