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This file includes:\n  - Data preprocessing \n    - Data cleaning, feature selection and creating time lagging predictors data\n    - Data preparation (predictor and predictand data)\n    - Splitting the data into training and testing datasets\n    - Standardizing the training and testing datasets\n  - Autocorrelation in time series\n  - Multi Layer Perceptron (MLP)\n    - Building a MLP sequential model, Training the model, Model evaluation and Plotting the results\n    - MLP Hyperparameter Tuning\n  - Long Short-Term Memory Networks (LSTM)\n    - Building LSTM model, Data preparation, Training the model, Model evaluation and Plotting the results\n  - Auto Regressive Integrated Moving Average (ARIMA)\n    - Model building and analysis\n    - Forecasting\n    - Model evaluation\n    - Results visualization \n  - Convolutional Neural Networks (CNN)\n\n- [projectML.py](https://github.com/javedali99/machine-learning-final-project/blob/main/projectML.py) - This file includes\n  - Data preprocessing\n  - Random Forest Regression (RFR)\n    - Data preprocessing\n    - Training the model\n    - Model evaluation\n    - Results visualization\n  - Support Vector Regression (SVR)\n    - Data preprocessing\n    - Training the model\n    - Linear, RBF and Polynomial kernels for SVM\n    - Model evaluation\n    - SVR hyper parameters tuning\n    - Results visualization\n    - Improvement of the SVR method by changing temporal resolution to \"daily max surge\" instead of hourly\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjavedali99%2Fmachine-learning-final-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjavedali99%2Fmachine-learning-final-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjavedali99%2Fmachine-learning-final-project/lists"}