10 Scikit-Learn Exercises for Aspiring Data Scientists

Here are ideas for 10 scikit-learn exercises for aspiring Data Scientists:

1. Build a linear regression model to predict housing prices using the Boston Housing dataset. Relevant scikit-learn tools: LinearRegression
2. Train a decision tree classifier to classify iris species using the Iris dataset. Relevant scikit-learn tools: DecisionTreeClassifier
3. Use k-means clustering to group similar observations in the Iris dataset. Relevant scikit-learn tools: KMeans
4. Build a logistic regression model to predict whether an email is spam or not using the Spambase dataset. Relevant scikit-learn tools: LogisticRegression
5. Train a random forest classifier to predict wine quality using the Wine Quality dataset. Relevant scikit-learn tools: RandomForestClassifier
6. Use principal component analysis (PCA) to reduce the dimensionality of a dataset and visualize the results. Relevant scikit-learn tools: PCA
7. Implement a support vector machine (SVM) to classify images of hand-written digits using the MNIST dataset. Relevant scikit-learn tools: SVC
8. Build a neural network using scikit-learn's MLPClassifier to classify images of clothing items in the Fashion MNIST dataset. Relevant scikit-learn tools: MLPClassifier
9. Use the KNeighborsClassifier to classify the Breast Cancer dataset Relevant scikit-learn tools: KNeighborsClassifier
10. Use the DecisionTreeRegressor to predict the prices of the cars in the Auto-MPG dataset. Relevant scikit-learn tools: DecisionTreeRegressor