Using Scikit-Learn Pipelines: A Cleaner Way to Build Machine Learning Models

Based on the content provided, it appears you're discussing how to use Pipeline and ColumnTransformer from scikit-learn for preprocessing data and training machine learning models. The key points covered are:

1. Handling Different Data Types: You can create separate pipelines for numerical and categorical features.
2. Combining Pipelines with ColumnTransformer: Use ColumnTransformer to apply different transformations to different columns of the dataset.
3. Connecting Preprocessing to Model Training: Combine preprocessing steps and model training into a single pipeline, making it easier to manage the entire workflow.
4. Hyperparameter Tuning with GridSearchCV: You can tune both preprocessing parameters (like those in StandardScaler or OneHotEncoder) and model parameters using GridSearchCV.

Here's an example of how you might set up these pipelines:

python
 
1from sklearn.compose import ColumnTransformer
2from sklearn.pipeline import Pipeline
3from sklearn.impute import SimpleImputer
4from sklearn.preprocessing import StandardScaler, OneHotEncoder
5from sklearn.linear_model import LogisticRegression
6
7# Assuming numerical_features and categorical_features are defined as lists of column names
8numerical_features = ['age', 'income']
9categorical_features = ['gender']
10
11# Numer
12
13[Read the full article at DEV Community](https://dev.to/audrine_m/using-scikit-learn-pipelines-a-cleaner-way-to-build-machine-learning-models-2moh)
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