Real-Life Examples of Data Preprocessing in AI Projects for High School Students

📚 Quick Study Guide

• 🔍 Data preprocessing involves transforming raw data into a clean and usable format for AI models.
• 🧹 Common techniques include:
• 🧽 Data Cleaning: Handling missing values, removing duplicates, and correcting errors.
• 📊 Data Transformation: Scaling, normalization, and feature encoding.
• ✂️ Data Reduction: Reducing the size of the dataset while preserving important information.
• ➕ Importance: Improves model accuracy, efficiency, and reliability.
• 🗓️ Real-world examples: Image recognition, natural language processing, and predictive modeling.

🧪 Practice Quiz

1. Question 1: Which of the following is NOT a common data preprocessing technique?
   1. Data Cleaning
   2. Data Transformation
   3. Model Training
   4. Data Reduction
2. Question 2: What is the purpose of data cleaning?
   1. To increase the size of the dataset
   2. To handle missing values and correct errors
   3. To complicate the data for better security
   4. To randomly shuffle the data
3. Question 3: Which technique involves scaling data to a specific range, like 0 to 1?
   1. Data Cleaning
   2. Data Reduction
   3. Normalization
   4. Feature Encoding
4. Question 4: What is feature encoding?
   1. Converting categorical data into numerical data
   2. Removing irrelevant features from the dataset
   3. Compressing the data for storage
   4. Splitting the data into training and testing sets
5. Question 5: In image recognition, what preprocessing step might involve resizing images?
   1. Data Cleaning
   2. Data Reduction
   3. Data Transformation
   4. All of the above
6. Question 6: Which of the following is a reason to perform data preprocessing?
   1. To confuse the AI model
   2. To improve model accuracy and efficiency
   3. To make the data more difficult to understand
   4. To increase the amount of noise in the data
7. Question 7: What is the main goal of data reduction techniques?
   1. To increase the dimensionality of the data
   2. To reduce the size of the dataset while preserving important information
   3. To add more features to the dataset
   4. To make the data more complex