scikit-learn: 100 Tips and Strategies for Mastering Machine Learning

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Here are 100 tips for working with scikit-learn, a popular machine learning library in Python:

Basics of scikit-learn:

1. Import scikit-learn with import sklearn.
2. Install scikit-learn using pip install scikit-learn.
3. Access scikit-learn version with sklearn.__version__.
4. Import specific modules from scikit-learn, e.g., from sklearn import datasets.
5. Use sklearn.model_selection.train_test_split for splitting datasets into training and testing sets.

Data Loading and Preprocessing:

1. Load built-in datasets with sklearn.datasets.load_*.
2. Explore dataset information with data.DESCR for data description.
3. Handle missing values with sklearn.impute.SimpleImputer.
4. Encode categorical variables with sklearn.preprocessing.LabelEncoder or sklearn.preprocessing.OneHotEncoder.
5. Scale numerical features using sklearn.preprocessing.StandardScaler or sklearn.preprocessing.MinMaxScaler.
6. Explore feature statistics with sklearn.feature_selection methods.

Classification: