Get a real-world data to solve either a classification OR regression problem. The chosen data must be related to crop price prediction for TWO types of crop. 7 For the selected data, you have to: Conduct the exploratory data analysis, such as the feature selection, feature extraction, data normalization, handling the data quality issues, scaling and etc. before developing the machine learning model. Apply THREE types of machine learning models to solve the selected classification or regression problem. You have to evaluate the performance of these machine learning models using suitable evaluation metrics. For example, for the classification problem, the suitable evaluation metrics could be accuracy, precision, recall, F1 score and others. For the regression problem, you can apply the mean-squared error, root mean squared error, mean absolute error and R-squared. . 1 Get a real-world data to solve either a classification OR regression problem. The chosen data must be related to crop price prediction for TWO types of crop. 7 For the selected data, you have to: Conduct the exploratory data analysis, such as the feature selection, feature extraction, data normalization, handling the data quality issues, scaling and etc. before developing the machine learning model. Apply THREE types of machine learning models to solve the selected classification or regression problem. You have to evaluate the performance of these machine learning models using suitable evaluation metrics. For example, for the classification problem, the suitable evaluation metrics could be accuracy, precision, recall, F1 score and others. For the regression problem, you can apply the mean-squared error, root mean squared error, mean absolute error and R-squared. . 1