Here are the steps:

Convert the data file to a table

Calculate descriptive statistics for the following variables

tmdb$\_$popularity

runtime

imdb$\_$score

Create the following PivotTable

Mean tmdb$\_$popularity by Drama$\_$Alpha $($Columns$)$ and Decade $($Rows$)$

Create a stacked histogram for the following variables using PivotCharts

Count of title by type $($Columns$/$Axis$)$ and tmdb$\_$popularity $($Rows$/$Legend$)$; Group by Starting at $0,$ Ending at $200,$ By $10)$; Add slicers for Decade and Action$\_$Alpha

Create boxplots for the following variables

tmdb$\_$score by SciFi$\_$Alpha

Create the following stacked bar chart using PivotCharts

Mean tmdb$\_$popularity by Action$\_$SciFi$\_$Alpha $($Columns$/$Axis$)$ and type $($Rows$/$Legend$)$; Add a slicer for Decade

Create a scatterplot for the following variables

tmdb$\_$score by imdb$\_$score

Create a correlation a matrix that includes the following variables

tmdb$\_$popularity

runtime

imdb$\_$score

Action

Drama

SciFi

Comedy

European

Western

Animation

Dramedy

Action$\_$Drama

Action$\_$SciFi

Drama$\_$SciFi

Action$\_$Drama$\_$SciFi

Conduct a market basket analysis using the same variables for the correlation matrix to determine which of the variables predict tmdb$\_$popularity

Include as many regression analysis steps as are needed to identify the variables that predict tmdb$\_$popularity

Calculate the lift for each variable from the final regression model

Using the data mining results, prepare an Executive Summary of $1-2$ pages for Netflix that explains which variables predict movie popularity. Include in your summary the following:

A description of the analyses that were conducted

Charts, PivotTables, and PivotCharts showing the relationship of the key variables to the movie popularity

A summary table showing the key variables and their lift as calculated in your analyses

Any recommendations that you can provide to Netflix