1. Display descriptive statistics on the dataset.

2. Check if any records in the data have any missing values; handle the missing data as appropriate.

3. Build a graph visualizing the distribution of one or more individual continuous variables of the dataset

4. Build a graph visualizing the relationship in a pair of continuous variables. Determine the correlation between them.

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 them.

7. Retrieve one or more subsets of rows based on two or more criteria and present descriptive statistics on the subset(s).

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

9. Create one or more tables that group the data by a certain categorical variable and display summarized information for each group.

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. The project should start with an introduction section motivating the project. The project should conclude with a conclusion section summarizing key findings.