Machine learning /Deep Learning model in R

Develop two models: (a). LSTM multi-factor model and (b) Ensemble model using NN, RF, XGBOOST, ADABOOST, GBM and other algorithm of your choice and ensemble using GBM using CARET package in R (already have reference code in R for ensemble model, one just need to implement it on my data and put the code in function form as mentioned below)

Code design: two separate function for model training and model prediction, first function takes in data-name, dependent variable name and target variable name and forecasting window to train and save the model. while second function, takes the model name to be used in prediction and give output,

Key points essentially to be done.

- Prepare the dataset that let you forecast to the future to the specific window of time. I will share the timeseries data with variables and target

- Apply LSTM multi-factor model and ML ensemble model in design explained in code design section. Note that LSTM gives the numeric output while ensemble will give the binary output for a specific window of time

- Perform the train and test split and check the model performance using rmse and mse

- Finally, apply the trained model to forecast beyond the train and test data using 2 methods: recursive multi-step forecast and direct multi step forecast

Skills: R Programming Language, Statistics, Python, Deep Learning, Machine Learning (ML)

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Project ID: #30602291

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