Project 5: Ensemble Methods
Project 5: Ensemble Methods
Ensemble Methods for Predicting Customer Churn
In this project activity, we will use ensemble methods to predict customer churn for a telecommunications company. The dataset contains information on customer demographics, usage patterns, and account information. Our goal is to create a model that can accurately predict whether a customer will churn or not, based on this information.
1. Data Preparation
First, we need to prepare the data for training our models. We will start by loading the dataset into a pandas dataframe and cleaning the data by removing any missing values and redundant features. Then, we will split the dataset into training and testing sets, with a 70:30 split.
2. Baseline Model
Next, we will create a baseline model using a single decision tree classifier. We will train the model on the training set and evaluate its performance on the testing set using metrics such as accuracy, precision, recall, and F1 score.
3. Bagging Classifier
We will then create a bagging classifier using Scikit-learn’s BaggingClassifier class. We will set the base estimator as a decision tree classifier and the number of estimators to 10. We will train the bagging classifier on the training set and evaluate its performance on the testing set.
4. AdaBoost Classifier
We will also create an AdaBoost classifier using Scikit-learn’s AdaBoostClassifier class. We will set the base estimator as a decision tree classifier and the number of estimators to 10. We will train the AdaBoost classifier on the training set and evaluate its performance on the testing set.
5. Random Forest Classifier
Finally, we will create a random forest classifier using Scikit-learn’s RandomForestClassifier class. We will set the number of estimators to 10 and the maximum number of features to sqrt. We will train the random forest classifier on the training set and evaluate its performance on the testing set.
6. Model Comparison
We will compare the performance of the baseline model with the bagging, AdaBoost, and random forest classifiers. We will evaluate the models using metrics such as accuracy, precision, recall, and F1 score. We will also use a confusion matrix to visualize the performance of the models.
7. Conclusion
Based on the performance metrics and the confusion matrix, we will select the best performing model for predicting customer churn. We will provide insights and recommendations to the telecommunications company based on the findings of our analysis.