In this proposed study we design a step-by-step methodology for lung
cancer classification based on clinical evidence. The following points
are our methodology steps.
In first we select data from UCI. Which lung cancer data set. Also,
selecting the python language for our data analysis.
Next, we import data set into language by using specific libraries.
That is using for data reading and writing.
Now performing data preprocessing techniques to identify missing and
duplicate values in data.
After data cleaning performing label encoding to convert categorical
data to numerical for our model.
Then creating a graphical presentation of data by using data
visualization techniques to understand data.
Now divide data set dependent class and independent classes.
This step is very important to normalize data for the algorithm.
After data normalization we applying our proposed machine learning
supervised model.
Generating classification and prediction results from the supervised
algorithm.
Now it is important to calculate the performance of our model.
Second, last, we identifying how much our result are accurate by
evaluation and validation results.
Comparing our results with existing research work.