Please help me solve this using R studio. R CODE $!!!$ Solve using R studio please. Can you give me the full code for this and use naive and randomforest for classification model. Also please mention how to and what to analysis in each step.

Facebook ad sales analysisFacebook ad$-$sales analysis

Social Media Ad Campaign marketing is a leading source of Sales Conversion.

The data is from Facebook's ad campaign. It contains $1143$ observations in $11$ variables.

Variables explanation:

ad$\_$id: an unique ID for each ad$.$

$-$xyzcampaignid: an ID associated with each ad campaign of XYZ company.

fbcampaignid: an ID associated with how Facebook tracks each campaign.

age: age of the person to whom the ad is shown.

gender: gender of the person to whom the ad is shown

interest: a code specifying the category to which the person's interest belongs $($interests

are as mentioned in the person's Facebook public profile$).$

Impressions: the number of times the ad was shown.

Clicks: number of clicks on for that ad$.$

Spent: Amount paid by company xyz to Facebook, to show that ad$.$

Total conversion: Total number of people who enquired about the product after seeing

the ad$.$

Approved conversion: Total number of people who bought the product after seeing the

ad

Upload the data

Find the dimensions of this data set.

Summarize the data and showcase the structure of this dataset.

Visualize the Approved conversion using histogram

Remove the ID columns Create a new column to indicate if the approved conversion is low, average, or high $($if

approved conversion$<=2$ consider it as $$low$,$ if approved conversion$<10$ consider it as

average, otherwise consider it as high$)$

Your task is to determine which features make a

$$low$,$average$,$ and 'high' conversion!

Develop a model to be able to predict the ad$$s conversion based on the its features. Develop at

least two classifier models$/$strategies for comparison.

As part of your analysis make sure to include which features are the top $3$ discriminating factor

for conversion.

Please note that No regression or ANOVA analysis is required.