Can you list out the steps to do these task below and the codes as jupyter notebook for it.

As for the code, assume the data will have column with numerical such as: age, euribor3m, number of money own,...; categorical: name. job, marital, loan, ....

Task 3: Data Modelling

Model the data as a Classification Task. You must use three different classification models, and when building each model, it must include the following steps:

Select appropriate features.

Select the appropriate model (e.g. DecisionTree for classification) from sklearn.

Train and evaluate the model appropriately.

Train and evaluate the model by selecting the appropriate values for each parameter in the model. You need to show how you choose these values and justify why you choose it. After you have built three classification models on the dataset, the next step is to compare the performance of the models. For each model, you must report 3 evaluation metrics (Precision, Recall, and F-score). You need to include the results of this comparison, including a recommendation of which model should be used, in your report.