Training data preparation in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Training data preparation

This pipeline shows how raw data is cleaned and organized before training a machine learning model. It prepares the data so the model can learn well.

Data Flow - 6 Stages

1Raw data collection

1000 rows x 10 columns→Collect data from various sources with missing and noisy values→1000 rows x 10 columns

Row example: {"age": 25, "income": null, "gender": "M", ...}

↓

2Data cleaning

1000 rows x 10 columns→Fill missing values and remove duplicates→980 rows x 10 columns

Row example: {"age": 25, "income": 50000, "gender": "M", ...}

↓

3Feature selection

980 rows x 10 columns→Keep only relevant columns for prediction→980 rows x 6 columns

Columns kept: age, income, gender, education, hours_worked, target

↓

4Data encoding

980 rows x 6 columns→Convert categorical data to numbers→980 rows x 8 columns

Gender 'M' -> 1, 'F' -> 0; Education levels one-hot encoded

↓

5Data normalization

980 rows x 8 columns→Scale numeric features to 0-1 range→980 rows x 8 columns

Age scaled from 0 to 1, income scaled from 0 to 1

↓

6Train/test split

980 rows x 8 columns→Split data into training and testing sets→784 rows x 8 columns (train), 196 rows x 8 columns (test)

Training set example row: {age: 0.3, income: 0.5, gender: 1, ...}

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |****
0.5 |***
0.4 |**
0.3 |*
    +------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning with moderate loss and accuracy
2	0.50	0.72	Loss decreases and accuracy improves as model learns
3	0.40	0.80	Model continues to improve with lower loss and higher accuracy
4	0.35	0.85	Training converges with good accuracy and low loss
5	0.32	0.87	Final epoch shows stable loss and accuracy

Prediction Trace - 3 Layers

Layer 1: Input features

Layer 2: Hidden layer with ReLU activation

Layer 3: Output layer with sigmoid activation

Model Quiz - 3 Questions

What happens to missing values during data cleaning?

A They are filled or removed

B They are left as is

C They are converted to zeros

D They are duplicated

Key Insight

Preparing data carefully by cleaning, selecting, encoding, and normalizing helps the model learn better and faster. Good data preparation leads to lower loss and higher accuracy during training.

Practice

1. What is the main purpose of training data preparation in machine learning?

easy

A. To clean and organize data for better model learning

B. To create the final model architecture

C. To deploy the model to production

D. To write the code for model training

Solution

1. Step 1: Understand the role of training data preparation

   Training data preparation involves cleaning and organizing data so the model can learn effectively.

2. Step 2: Differentiate from other steps in machine learning

   Creating model architecture, deployment, and coding are separate steps after data preparation.

3. Final Answer:

   To clean and organize data for better model learning -> Option A

4. Quick Check:

   Training data preparation = cleaning and organizing data [OK]

Hint: Focus on data cleaning and organizing for training [OK]

Common Mistakes:

• Confusing data preparation with model building
• Thinking deployment is part of data preparation
• Assuming coding is data preparation

2. Which of the following is the correct way to split data into training and testing sets in Python using scikit-learn?

easy

A. split_train_test(data, 0.2)

B. train_test(data, split=0.2)

C. train_test_split(data, test_size=0.2)

D. test_train_split(data, size=0.2)

Solution

1. Step 1: Recall the scikit-learn function for splitting data

   The correct function is train_test_split with parameters like test_size.

2. Step 2: Check the syntax of each option

   Only train_test_split(data, test_size=0.2) uses the correct function name and parameter syntax.

3. Final Answer:

   train_test_split(data, test_size=0.2) -> Option C

4. Quick Check:

   Correct function and parameter = train_test_split(data, test_size=0.2) [OK]

Hint: Remember scikit-learn's train_test_split function name [OK]

Common Mistakes:

• Using wrong function names
• Incorrect parameter names
• Mixing order of parameters

3. Given the code below, what will be the output of print(X_train.shape, X_test.shape)?

from sklearn.model_selection import train_test_split
import numpy as np
X = np.arange(20).reshape(10, 2)
X_train, X_test = train_test_split(X, test_size=0.3, random_state=42)

medium

A. (7, 2) (3, 2)

B. (3, 2) (7, 2)

C. (10, 2) (0, 2)

D. (5, 2) (5, 2)

Solution

1. Step 1: Understand the data shape and split ratio

   The data X has 10 rows and 2 columns. test_size=0.3 means 30% data for testing (3 rows) and 70% for training (7 rows).

2. Step 2: Calculate the shapes of training and testing sets

   Training set shape: (7, 2), Testing set shape: (3, 2).

3. Final Answer:

   (7, 2) (3, 2) -> Option A

4. Quick Check:

   70% train = 7 rows, 30% test = 3 rows [OK]

Hint: Calculate rows by multiplying total by split ratio [OK]

Common Mistakes:

• Swapping train and test sizes
• Ignoring the shape's second dimension
• Misunderstanding test_size meaning

4. Identify the error in the following code snippet for normalizing data using MinMaxScaler:

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
X_scaled = scaler.fit_transform(X)
print(X_scaled)

medium

A. MinMaxScaler cannot handle negative values

B. MinMaxScaler requires data as a numpy array, not list

C. fit_transform should be called on scaler.fit(X).transform(X)

D. No error, code runs correctly

Solution

1. Step 1: Check input data type compatibility

   MinMaxScaler accepts lists or numpy arrays as input, so list input is valid.

2. Step 2: Verify method usage

   Calling scaler.fit_transform(X) is the correct way to fit and transform data in one step.

3. Final Answer:

   No error, code runs correctly -> Option D

4. Quick Check:

   MinMaxScaler works with lists and fit_transform method [OK]

Hint: MinMaxScaler accepts lists and arrays directly [OK]

Common Mistakes:

• Thinking input must be numpy array
• Misusing fit and transform methods
• Assuming scaler rejects negative values

5. You have a dataset with categorical text features and numeric features. Which sequence of steps correctly prepares the data for training a machine learning model?

hard

A. Split data, encode categorical features, normalize numeric features, then clean missing values

B. Clean missing values, encode categorical features, normalize numeric features, then split data

C. Normalize numeric features, clean missing values, split data, then encode categorical features

D. Encode categorical features, split data, clean missing values, then normalize numeric features

Solution

1. Step 1: Clean missing values first

   Cleaning missing data ensures no errors during encoding or normalization.

2. Step 2: Encode categorical features before normalization

   Categorical data must be converted to numbers before normalization.

3. Step 3: Normalize numeric features and then split data

   Normalization scales numeric data; splitting last avoids data leakage.

4. Final Answer:

   Clean missing values, encode categorical features, normalize numeric features, then split data -> Option B

5. Quick Check:

   Proper order: clean -> encode -> normalize -> split [OK]

Hint: Always clean first, encode before normalize, split last [OK]

Common Mistakes:

• Splitting data before cleaning causes leakage
• Normalizing before encoding categorical data
• Encoding after splitting leads to inconsistent categories