Project 2: Decision Trees

Project 2: Decision Trees

Building a Decision Tree Classifier using Titanic Dataset

In this project activity, we will use the Titanic dataset to build a decision tree classifier. This dataset contains information about the passengers who were aboard the Titanic when it sank, and we will use this information to predict whether a passenger survived or not based on their age, gender, ticket class, and other features.

Here are the steps to follow:

1. Load the Titanic dataset into a pandas DataFrame.
2. Preprocess the data by removing any missing values and converting categorical variables to numerical variables.
3. Split the data into training and test sets using the train_test_split() function from the sklearn.model_selection module.
4. Create a decision tree classifier object using the DecisionTreeClassifier() function from the sklearn.tree module.
5. Train the decision tree classifier on the training data using the fit() method.
6. Use the predict() method to make predictions on the test data.
7. Evaluate the performance of the classifier using metrics such as accuracy, precision, recall, and F1 score.
8. Visualize the decision tree using the export_graphviz() function from the sklearn.tree module.
9. Tune the hyperparameters of the decision tree classifier to improve its performance.
10. Compare the performance of the decision tree classifier with other machine learning algorithms such as logistic regression, support vector machines, and random forests.

Note: 

This project activity aims to build a basic decision tree classifier using the Titanic dataset. You can explore more advanced topics such as pruning, ensembling, and feature selection in future projects.

To get started, first load the Titanic dataset into a pandas DataFrame and preprocess the data by handling missing values and converting categorical variables into numerical variables. Once you have cleaned the data, split it into training and test sets using the train_test_split() function.

Next, create a decision tree classifier object using the DecisionTreeClassifier() function from the sklearn.tree module. Train the classifier on the training data using the fit() method and then use the predict() method to make predictions on the test data.

To evaluate the performance of the classifier, calculate metrics such as accuracy, precision, recall, and F1 score. You can use the classification_report() function from the sklearn.metrics module to calculate these metrics.

To visualize the decision tree, use the export_graphviz() function from the sklearn.tree module. This will generate a .dot file, which you can convert to an image using Graphviz.

Finally, tune the hyperparameters of the decision tree classifier to improve its performance. You can use techniques such as grid search or randomized search to find the best hyperparameters for your classifier.

Once you have built and tuned your decision tree classifier, compare its performance with other machine learning algorithms such as logistic regression, support vector machines, and random forests. This will help you determine which algorithm is best suited for your particular problem.