You are Required to Design and Implement an Exploratory data Analysis (EDA) Project: Business Analytics Assignment, NUS, Singapore

In this assignment, you are required to design and implement an exploratory data analysis (EDA) project. Datasets You can choose one of three datasets for analysis:

(1) Loan applications: loanapp.csv. The dataset contains data on loan applications to a bank, including various types of information on the applicant and the purpose of the loan, along with the eventual loan decision (approve or reject – see the column loan_decision). A detailed description of the columns can be found here here.

(2) Major League Baseball: mlb.csv. Data on salaries and other information (such as race, position, and performance information) on baseball players in MLB in 1993. A detailed description of the columns can be found here.

(3) Wages: wage.csv. Data on employees, such as their hourly wage, gender, race, marital status, etc. A detailed description of the columns can be found here. The source for all three datasets is a companion page for Woolridge J. (2013). Introductory Econometrics: A Modern Approach.

You can prepare your project in the form of a Jupyter notebook (Python), RMarkdown notebook (R), or an Excel file. With any of the three options, the final submission should be converted to a PDF file (when using Excel you may consider inserting relevant tables and plots into an MS Word document and before converting it to PDF). Steps of the EDA The EDA project will need to include as many as possible of, but not limited to, the following steps:

1. Load dataset from a file

2. Display descriptive statistics about the dataset

3. Check if any records in the data have any missing values; handle the missing data as appropriate (interpolate missing values, delete records with missing values, etc).

4. Display the distribution of (some of) numerical variables as histograms. Provide verbal comments on the graph.

5. Display unique values of a categorical variable.

6. Build a contingency table of two potentially related categorical variables. Conduct a statistical test of the independence between the variables. Provide verbal comments on the output.

7. Retrieve a subset of the data based on two or more criteria and present descriptive statistics on the subset. Provide verbal comments on the output.

8. Conduct a statistical test of the significance of the difference between the means of two subsets of the data. Provide verbal comments.

9. Create pivot tables, i.e., create a table that groups the data by a certain categorical variable and displays summarized information for each group (e.g. the mean or sum within the group). Provide verbal comments.

10. Implement a linear regression model and interpret its output. Each step of the analysis should be documented with comments, describing what the step is meant to achieve, and interpreting the result of the step. If the result of the step is a graph, interpret the graph in the comments below the graph. Before you start to work on this assignment, please familiarise yourself with the detailed evaluation criteria for this assignment by studying the assessment brief (see above).