Data Preprocessing is the process of cleaning and preparing raw data before using it in Machine Learning.<\/p>\n\n\n\n
Raw data is often incomplete, inconsistent, or noisy.
Preprocessing ensures that the data is accurate, clean, and ready for modeling.<\/p>\n\n\n\n
It is one of the most important steps in Machine Learning.<\/p>\n\n\n\n
Data preprocessing helps:<\/p>\n\n\n\n
Improve model accuracy
Handle missing values
Remove errors and duplicates
Normalize data
Convert data into proper format
Reduce noise<\/p>\n\n\n\n
Good data leads to better predictions.<\/p>\n\n\n\n
Data cleaning involves fixing or removing incorrect data.<\/p>\n\n\n\n
Common tasks:<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
If age is missing \u2192 Replace with mean or median
If duplicate records exist \u2192 Remove them<\/p>\n\n\n\n
Common techniques:<\/p>\n\n\n\n
Choosing the right method depends on the dataset.<\/p>\n\n\n\n
Machine Learning models work with numbers, not text.<\/p>\n\n\n\n
So categorical data must be converted.<\/p>\n\n\n\n
Techniques:<\/p>\n\n\n\n
Label Encoding \u2192 Assign number to each category
One-Hot Encoding \u2192 Create separate column for each category<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
Gender:
Male \u2192 0
Female \u2192 1<\/p>\n\n\n\n
Or:<\/p>\n\n\n\n
Male \u2192 [1,0]
Female \u2192 [0,1]<\/p>\n\n\n\n
Some algorithms perform better when features are on the same scale.<\/p>\n\n\n\n
Two common methods:<\/p>\n\n\n\n
Standardization<\/p>\n\n\n\n
Normalization<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
Salary range: 10,000 to 1,000,000
Age range: 18 to 60<\/p>\n\n\n\n
Scaling ensures one feature does not dominate others.<\/p>\n\n\n\n
Outliers are extreme values that can affect model performance.<\/p>\n\n\n\n
Methods:<\/p>\n\n\n\n
Removing outliers improves model stability.<\/p>\n\n\n\n
Not all features are useful.<\/p>\n\n\n\n
Feature selection helps:<\/p>\n\n\n\n
Reduce overfitting
Improve performance
Reduce training time<\/p>\n\n\n\n
Techniques:<\/p>\n\n\n\n
Correlation analysis
Feature importance
Recursive feature elimination<\/p>\n\n\n\n
Before training:<\/p>\n\n\n\n
Split data into:<\/p>\n\n\n\n
Training set (70\u201380%)
Testing set (20\u201330%)<\/p>\n\n\n\n
This ensures the model is evaluated properly.<\/p>\n\n\n\n
Pandas \u2192 Data cleaning
NumPy \u2192 Numerical operations
Scikit-learn \u2192 Encoding and scaling
Matplotlib\/Seaborn \u2192 Visualization<\/p>\n\n\n\n
Garbage in \u2192 Garbage out<\/p>\n\n\n\n
If data is poor quality, the model will perform poorly \u2014 no matter how advanced the algorithm is.<\/p>\n\n\n\n
Data Preprocessing prepares raw data for Machine Learning by cleaning, transforming, and organizing it.<\/p>\n\n\n\n
It is a crucial step that directly impacts model performance and accuracy.<\/p>\n\n\n