- We classify whether the data in the dataset is churn or not
- 1 if churn else 0 if not churn
- Build Decision tree classified From sklearn library and classify the dataset ,xt is given in the question ,target variable is ‘Churn’ Column
- Build Confusion matrices for Decision Tree Classifier: +Accuracy is:85.26%
- Build Ruc curve calculate auc and also graphviz to generate a pdf of the obtained Decision Tree Graphically +From sklearn split into test/train in the ratio 80:20.
- Use SVM classifier and calculate Accuracy.
- Accuracy is:90.424%
- Use MLP classifier(For ANN) and calculate Accuracy.
- Build a comparative table
- Sklearn:For train-test split for all the classifiers,SVM,DecisionTreeClassifier,MLP,confusion matrices
- Pandas:For dataframe ,For mode imputation
- Graphviz:To generate a pdf of decision tree
- Matplotlib:To plot graphs
Output Decision Tree:
