Theory:
Data Preprocessing in the Data Mining Process:
The data mining/KDD process
Why data preprocessing?
Issues in Data Preprocessing:
Data Cleaning
Data Transformation
Variable Construction
Data Reduction and Discretization
Data Integration
The data mining/KDD Process:
Understanding customer: 10%-20%
Understanding data:20-30
Prepare data: 40-70%
Build Models: 10-20%
Evaluate models: 10%-20%
Take action:10%20%
Why data mining?
Real – world data is dirty
Low data quality anyway a huge problem in data mining
Garbage in,garbage out
Different methods, different requirements
R Working Codes for data mining:
R code is case sensitive:
I am doing it from professors sheet.
dim means dimension
This line i could not make work:
hist(Ozone,breaks=25,ylim=(c(0,45)),main=”Original data”)
And another question how the imputation works
Exercise 2 (K)= I have to find the answers
Exercise 3: Answer:
clothing=read.csv(file="F:/desktop and documents/Desktop/dataminingdata/clothing_store.txt")
