ASSINMENT ON Data analysis in Rapid miner.

Data analysis in Rapid miner
Task 1.1 Data exploration and preparation
The data set is imported from the excel file into rapid miner studio. The data set is then stored in
respiratory using the name “my first prediction” for later analysis. To use the data set for analysis
we drag and drop the file containing the data set into the process window which we then connect
the output and input to get the results. The data set contain 7,043 rows and 21 variables. The only
variable having missing values is the “TotalCharges” which has 11 missing values. All the other
attributes have no missing values. The data set consist of different data types such as polynomial,
integers and real. Some of these variables have been summarized on the table below;
From the data set the relationship between various variables have been analyzed through the use
of scatter plot and bar graph as shown below:

The above graph shows the relationship between the number of months customers have stayed
with the company (tenure) and amount charged to customers monthly (MonthlyCharge).

The bar graph above also indicates the relationship between gender and the total amount charged
to customers.
In this software the data transformation have been done in the window called Turbo prep, in this
window the variables have been converted from nominal to numeric, the target variable have
also been converted to binominal. The missing values have also been filled by the average of
each variable. The variable “CustomerID” have been removed from the dataset. Once all these
transformation have been carried out, the data set is now ready for creation of models and further
analysis.
Task 1.2 Decision tree model
On building this model, the dataset is loaded into the auto model window. Thereafter we select
the target variable which is “Churn”. We then select the target to see between the loyal