The Data Analyst Toolkit

Scikit-learn, often abbreviated as sklearn, is an open-source machine learning library for Python. It is built on top of other Python libraries such as NumPy, SciPy, and matplotlib, making it a versatile and easy-to-use tool for machine learning tasks. Scikit-learn provides a wide range of machine learning algorithms and tools that facilitate tasks such as classification, regression, clustering, dimensionality reduction, and more.

Data Preprocessing #

Level 1: Data Preprocessing

1. train_test_split: Splits a dataset into training and testing sets.
   from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
2. StandardScaler: Standardizes features by removing the mean and scaling to unit variance.
   from sklearn.preprocessing import StandardScaler
scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train)
3. LabelEncoder: Encodes categorical labels into numerical values.
   from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
y_encoded = label_encoder.fit_transform(y)

Regression #

Level 2: Regression

4. LinearRegression: Fits a linear regression model.
   from sklearn.linear_model
import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
5. RandomForestRegressor: Builds a random forest regression model.
   from sklearn.ensemble import RandomForestRegressor
rf_regressor = RandomForestRegressor(n_estimators=100)
rf_regressor.fit(X_train, y_train)
6. cross_val_score: Performs k-fold cross-validation for regression models.
   from sklearn.model_selection import cross_val_score
scores = cross_val_score(regressor, X, y, cv=5)

Classification #

Level 2: Classification

7. LogisticRegression: Fits a logistic regression model for binary classification.
   from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression() classifier.fit(X_train, y_train)
8. SVM (Support Vector Machine): Trains a support vector machine classifier.
   from sklearn.svm import SVC svm_classifier = SVC(kernel='linear') svm_classifier.fit(X_train, y_train)
9. GridSearchCV: Performs grid search for hyperparameter tuning.
   from sklearn.model_selection import GridSearchCV
param_grid = {'C': [1, 10, 100], 'gamma': [0.1, 1, 10]}
grid_search = GridSearchCV(SVC(), param_grid, cv=3)
grid_search.fit(X_train, y_train)

Model Evaluation #

Level 3: Model Evaluation

10. accuracy_score: Computes accuracy for classification models.
    from sklearn.metrics import accuracy_score
y_pred = classifier.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
11. mean_squared_error: Calculates mean squared error for regression models.
    from sklearn.metrics import mean_squared_error
y_pred = regressor.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
12. classification_report: Generates a detailed classification report.
    from sklearn.metrics import classification_report
report = classification_report(y_test, y_pred)

Advanced Techniques #

Level 4: Advanced Techniques

13. Ensemble Methods: Combines multiple models (e.g., RandomForestRegressor, GradientBoostingRegressor) into an ensemble for regression.
    from sklearn.ensemble import GradientBoostingRegressor
ensemble = GradientBoostingRegressor(n_estimators=100)
ensemble.fit(X_train, y_train)
14. Neural Network: Utilizes scikit-learn’s MLPClassifier for classification and MLPRegressor for regression.
    from sklearn.neural_network import MLPClassifier, MLPRegressor
classifier = MLPClassifier(hidden_layer_sizes=(100, 50))
classifier.fit(X_train, y_train)
15. Pipeline: Creates a data processing and modeling pipeline.
    from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
pipe = Pipeline([('scaler', StandardScaler()), ('classifier', RandomForestClassifier())]) pipe.fit(X_train, y_train)

These definitions and examples cover a range of functions in scikit-learn for data preprocessing, regression, classification, model evaluation, and more. They are divided into different levels of complexity to provide a structured learning path for users.