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 1.1: Data Preparation

This assignment will focus on data modelling, and you need to focus on Classification techniques. For this assignment, you need to work on the Bank_Loan_Modelling data set. Being a careful data scientist, you know that it is vital to set the goal of the project, then thoroughly pre-process any available data (each attribute) before starting to analyse and model it. You will start by loading the data from the file. Then, you need to clean the data by dealling with the potential issues/errors in the data appropriately (such as: typos, impossible values, and missing values).

Task 1.2: Data Exploration

Carry out the following tasks: Explore at least 5 columns using appropriate descriptive statistics and graphs (if appropriate). For each explored column, please think carefully and report in your report:

1) the way you used to explore a column (e.g. the graph);

2) what you can observe from the way you used to explore it.

Please format each graph carefully and use it in your final report. You need to include appropriate labels on the x-axis and y-axis, a title, and a legend. The fonts should be sized for good readability. Components of the graphs should be coloured appropriately, if applicable. Note: These steps are for the training dataset only.

Task 2: Feature Engineering Use suitable Python approaches to extract potential features for model input. Conduct appropriate analysis to evaluate feature importance (e.g. correlation analysis), then use suitable method(s) to select the final features for the model. The feature choices must be explained via analysis. Note: These steps must be performed consistently for train/val/test sets.

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.