Are you ready to dive into the extreme world of XGBoost? Brace yourself for an exhilarating webinar that will take you from start to finish in mastering XGBoost in Python. Buckle up and get ready to boost your predictive analytics skills to new heights!
Contents
Introduction
Welcome to the StatQuest webinar on XGBoost in Python! I’m Josh Starmer, your guide through this extreme journey. In this webinar, we will leverage XGBoost, an exceptionally powerful machine learning method, to build a collection of boosted trees. We will use both continuous and categorical data from the IBM Base Samples website to predict customer churn, which refers to customers ceasing to use a company’s services.
Jumpstart with XGBoost
Before we get down to business, let’s take a moment to appreciate the power of XGBoost. XGBoost is an extreme machine learning method that strikes the perfect balance between accurate classification and interpretability. It is highly effective when you need to make precise predictions without sacrificing the ability to understand and interpret the model.
Formatting the Data
To begin our XGBoost journey, we need to prepare the data for analysis. First, we’ll load the telco churn dataset from IBM Base Samples into a Pandas dataframe. Then, we’ll examine the first five rows of the dataset to get a sense of its structure.
Next, we’ll remove irrelevant data, such as the customer ID and columns related to churn reasons, CLTV, and churn score. These columns don’t contribute to the prediction task and can lead to biased results. After removing these columns, we’ll verify that the changes were made correctly.
Handling Missing Data
Missing data can be a major obstacle in data analysis projects. In our dataset, missing values are represented by blank spaces. XGBoost has a default behavior for missing data, which treats them as zeros. To ensure consistency, we’ll replace all blank spaces in the dataset with zeros.
Once missing data is handled, we’ll convert the “total charges” column to numeric format and replace white spaces in column names with underscores. This step is essential for drawing a clear tree diagram later on.
One Hot Encoding
Categorical data, such as customer city or payment method, cannot be directly used in XGBoost. We need to convert them into numeric format using one hot encoding. This process involves creating separate columns for each unique category and assigning values of 0 or 1 based on presence or absence of the category. This enables XGBoost to effectively utilize categorical data for prediction.
After performing one hot encoding, we’ll split the data into training and testing datasets. It’s important to note that the data is imbalanced, with only 27% of customers leaving the company. To address this, we’ll use stratification during the splitting process to maintain the same percentage of churned customers in both datasets.
Building the XGBoost Model
Now, it’s time to build our preliminary XGBoost model. We’ll create an instance of the XGBClassifier, specifying the objective as binary logistic and missing data as zero. Using early stopping, we’ll determine the optimal number of trees to build based on the validation dataset’s performance.
We’ll evaluate the model’s performance using the testing dataset and visualize the results using a confusion matrix, which shows the number of correctly and incorrectly classified instances. While the initial model achieves 51% accuracy in predicting churned customers, there is room for improvement.
Optimizing the Model
To enhance the model’s accuracy, we’ll optimize its hyperparameters using cross-validation and grid search. Hyperparameters like max depth, learning rate, gamma, regularization parameter, and scale pause weight play a crucial role in fine-tuning the model’s performance.
Through systematic exploration of parameter combinations, we’ll find the optimal values that maximize the model’s accuracy. This process involves running XGBoost multiple times with different parameter combinations and selecting the best one.
Enhanced Predictive Power
With our newly optimized XGBoost model, we’ll witness a significant improvement in predicting churned customers. The confusion matrix now captures 84% of churned customers, compared to the initial 51%. While accuracy in predicting non-churning customers decreases slightly, this trade-off is acceptable because retaining existing customers is a priority.
Unveiling the Tree Diagram
To gain insight into the model’s decision-making process, let’s visualize the first tree. This tree diagram showcases the conditions for splitting customers into different groups based on various features. Although the diagram may appear complex, it provides valuable information for optimizing the model’s performance.
Conclusion
Congratulations on mastering XGBoost in Python! Throughout this webinar, we undertook an extreme journey from loading and formatting data to building and optimizing an XGBoost model. We witnessed the power of XGBoost in accurately predicting customer churn and discovered the importance of hyperparameter optimization.
Remember, XGBoost is just one of the countless adventures awaiting you in the world of predictive analytics. So don’t stop here! Continue exploring and expanding your skills to unlock even greater insights and possibilities.
Thanks for joining me in this epic quest, and until next time, keep questing and stay curious!
