In this, we will convert a time series problem to a supervised machine learning problem to predict driver demand. Exploratory analysis has to be performed on the time series to identify patterns. A regression model must be built and used to solve this time-series problem. Once the training model is prepared, spot testing will be performed on it. Following this, the driver demand prediction will be performed using Random Forest and Xgboost as the ensemble models.
In this, we will convert a time series problem to a supervised machine learning problem to predict driver demand. Exploratory analysis has to be performed on the time series to identify patterns. A regression model must be built and used to solve this time-series problem. Once the training model is prepared, spot testing will be performed on it. Following this, the driver demand prediction will be performed using Random Forest and Xgboost as the ensemble models.