Data Preprocessing & Cleaning in MATLAB

🧹 Data Preprocessing & Cleaning in MATLAB

In MATLAB, this typically involves handling missing values, outliers, duplicates, scaling, and data type corrections.
MATLAB is developed by MathWorks.

🔹 Why Data Cleaning Is Important?

• Improves analysis accuracy

• Prevents model bias & errors

• Makes datasets consistent & reliable

• Essential step before ML, statistics, or visualization

1️⃣ Inspecting Data

🔸 For Tables

📌 Quickly understand size, types, and ranges.

2️⃣ Handling Missing Values

 Detect Missing Data

 Remove Rows with Missing Values

 Fill Missing Values

📌 Works great with tables & timetables.

3️⃣ Handling Outliers

 Detect Outliers

 Remove Outliers

 Replace Outliers

4️⃣ Removing Duplicate Rows

5️⃣ Data Type Conversion

 Convert to Numeric

 Convert to Categorical

6️⃣ Normalizing & Standardizing Data

 Normalize (0 to 1)

 Standardize (Mean = 0, Std = 1)

7️⃣ Filtering Rows (Conditions)

8️⃣ Renaming & Reordering Variables

9️⃣ Cleaning Time-Series Data (Timetables)

📌 Essential for sensor & log data.

🔟 Complete Example (Real-World Style)

⚠️ Important Notes

• Always inspect data first

• Choose filling method carefully

• Don’t blindly remove outliers

• Use tables/timetables for real datasets

🎯 Interview Questions: Data Preprocessing & Cleaning

🔹 Q1. What is data preprocessing?

Answer:
Preparing raw data for analysis by cleaning and transforming it.

🔹 Q2. How do you handle missing values in MATLAB?

Answer:
Using rmmissing() or fillmissing().

🔹 Q3. How do you detect outliers?

Answer:
Using isoutlier().

🔹 Q4. Difference between normalization and standardization?

Answer:
Normalization → scale to range
Standardization → zero mean, unit variance

🔹 Q5. Which data type is best for real-world datasets?

Answer:
Tables and timetables.

🔹 Q6. Why is preprocessing important before ML?

Answer:
It improves model accuracy and reliability.

✅ Summary

• Data cleaning is mandatory before analysis

• MATLAB provides rich preprocessing tools

• Handle missing values, outliers, duplicates

• Foundation for statistics & machine learning