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[classworks]

run a k-means cluster analysis

from sklearn.cluster import KMeans

Create a KMeans model with the chosen number of clusters (k)

kmeans = KMeans(n_clusters=k, random_state=42)

Fit the KMeans model to your data

kmeans.fit(X) # X contains your clustering variables

cluster_labels = kmeans.labels_

cluster_centers = kmeans.cluster_centers_

from collections import Counter cluster_counts = Counter(cluster_labels)

[classworks]

lasso regression analysis

from sklearn.linear_model import LassoCV from sklearn.model_selection import cross_val_score

Create a LassoCV model with k-fold cross-validation

lasso_cv = LassoCV(alphas=[0.01, 0.1, 1.0, 10.0], cv=5) # Specify alpha values and the number of folds (cv)

Fit the LassoCV model on the training data

lasso_cv.fit(X_train, y_train) # X_train contains predictor variables, y_train contains the response variable

Get the optimal alpha value selected by cross-validation

optimal_alpha = lasso_cv.alpha_

Get the selected features (predictors) with non-zero coefficients

selected_features = X_train.columns[lasso_cv.coef_ != 0]

Use the selected features to train a final Lasso model

final_lasso_model = Lasso(alpha=optimal_alpha) final_lasso_model.fit(X_train[selected_features], y_train)

from sklearn.metrics import mean_squared_error, r2_score

Make predictions on the testing data

y_pred = final_lasso_model.predict(X_test[selected_features])

Evaluate the model

mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred)

print("Mean Squared Error:", mse) print("R-squared:", r2)

[classworks]

Run a Random Forest

from sklearn.ensemble import RandomForestClassifier

Create a Random Forest classifier

rf_model = RandomForestClassifier(n_estimators=100, random_state=42)

Fit the model to the training data

rf_model.fit(X_train, y_train) # X_train contains explanatory variables, y_train contains the response variable

from sklearn.metrics import accuracy_score, classification_report

Make predictions on the testing data

y_pred = rf_model.predict(X_test) # X_test contains the explanatory variables for the testing set

Evaluate the model

accuracy = accuracy_score(y_test, y_pred) report = classification_report(y_test, y_pred)

print("Accuracy:", accuracy) print("Classification Report:\n", report)

import matplotlib.pyplot as plt

Get feature importances

feature_importances = rf_model.feature_importances_

Plot feature importances

plt.barh(range(len(feature_importances)), feature_importances, tick_label=feature_names) plt.xlabel('Feature Importance') plt.ylabel('Feature') plt.title('Random Forest Feature Importance') plt.show()

[classworks]

To run a Classification Tree, follow these steps:

Prepare your data: Ensure that your data is formatted correctly, with the explanatory variables and the binary, categorical response variable clearly identified.

Load the necessary libraries: In your programming language or software, load the libraries required to perform a decision tree analysis. For example, in R, you would use the "rpart" and "rpart.plot" libraries.

Specify the model: Define the decision tree model by specifying the formula representing the relationship between the explanatory variables and the response variable. For example, in R, you would use the formula syntax, such as "response ~ variable1 + variable2 + variable3".

Fit the decision tree model: Apply the decision tree model to your data to create the classification tree. This process involves recursively partitioning the data based on the selected variables and their cutoff values.

Visualize the decision tree: Use a plotting function to visualize the decision tree. This step helps in interpreting the results and understanding the importance of each variable in predicting the response.

Interpret the results: Analyze the output of the decision tree to understand the relationships and interactions between the explanatory variables and the response variable. Look for patterns and the splitting criteria used in each node of the tree. Consider the importance of each variable in predicting the response, as indicated by the tree.