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Summary - Data preprocessing

In this group you learned data cleaning, missing data, and label encoding.

Summary: data preprocessing

The steps data goes through before the model:

  1. Cleaning: duplicates, spelling differences, outliers.
  2. Missing values: dropna or SimpleImputer.
  3. Types: to_numeric, astype.
  4. Encoding: LabelEncoder for labels, one-hot for unordered features.
  5. Splitting: train_test_split, with stratify and a validation set when needed.

The golden rule: every learned step such as filling and encoding is fit only on the training data.

Practice Leak-free filling

fit a SimpleImputer(strategy="mean") on X_train only, then transform X_test and print the result.

Starter code

model.py
import numpy as np
from sklearn.impute import SimpleImputer

X_train = np.array([[2.0], [4.0], [6.0]])
X_test = np.array([[np.nan], [10.0]])

# buraya yaz

Sample solution

model.py
import numpy as np
from sklearn.impute import SimpleImputer

X_train = np.array([[2.0], [4.0], [6.0]])
X_test = np.array([[np.nan], [10.0]])

imp = SimpleImputer(strategy="mean")
imp.fit(X_train)
print(imp.transform(X_test))

Expected output

[[ 4.]
 [10.]]

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 How should SimpleImputer be used on the test data?

  • fit_transform again on the test data
  • It shouldn't be used at all
  • It should be fitted on all the data
  • Only transform, with the object fitted on training Correct answer

Explanation: The test data's mean must not leak into training; the same learned value is applied.

2 Apply the filling learned on training to the test data.

Question 
X_test = imp.____(X_test)
Answer 
X_test = imp.transform(X_test)

Explanation: transform uses the values learned during fit.

3 Match the steps with the tools.

Term Meaning
Filling missing values SimpleImputer
Text label LabelEncoder
Unordered feature OneHotEncoder
Balanced split stratify

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4