Explain data preprocessing with an example

Explain data preprocessing with an example

Answer:-

Data preprocessing is the process of cleaning, transforming, and organizing raw data before feeding it into a machine learning model. It improves the quality of data and helps the model learn effectively.

Need for Data Preprocessing:

Real-world data is often:

• Incomplete (missing values)
• Noisy (errors or outliers)
• Inconsistent (conflicting values)
• Unstructured (not in usable format)

Preprocessing helps convert such data into a structured and clean format.

Steps in Data Preprocessing:

1. Data Cleaning:
   • Handle missing data (e.g., by replacing with mean/median).
   • Remove duplicates and outliers.
   • Correct errors.
2. Data Integration:
   • Combine data from multiple sources into a single dataset.
3. Data Transformation:
   • Normalize or scale data (e.g., range 0 to 1).
   • Encode categorical data (e.g., convert “Male” to 0, “Female” to 1).
4. Data Reduction:
   • Reduce the size of data by feature selection or dimensionality reduction.
5. Data Discretization (optional):
   • Convert continuous values into categorical bins.

Example:

Suppose you have a dataset to predict house prices:

Preprocessing steps:

• Fill missing Area with average value.
• Fill missing Bedrooms with mode (most common).
• Remove or impute missing Price.
• Encode Location using label encoding.
• Normalize Area and Price.