Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. The main contribution of your work is to develop a churn prediction model that assists telecom operators in predicting customers who are most likely subject to churn. Perform the following operations as you create the much needed deep learning application.Using the given datasetLinks to an external site. extract the relevant features that can define a customer churn.Use your EDA(Exploratory Data Analysis) skills to find out which customer profiles relate to churning a lot.Using the features in (1) define and train a Multi-Layer Perceptron modelEvaluate the model’s accuracy and calculate the AUC scoreCreate a platform to host the model either web-based or desktop applicationAllow users to use the application to enter new data and your model should predict if the supplied data of a new customer can result in a churn or not giving the confidence factor of the model

Outline the steps involved in developing and evaluating a predictive model for customer churn using logistic regression or a decision tree. Discuss the importance of data preparation, feature engineering, model training, and model evaluation in the process. Explain how you would evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score

You are refining a logistic regression model to predict customer churn. The dataset includes various customer interaction metrics. To enhance your model, explore how polynomial features can improve prediction accuracy. This approach allows the model to capture complex interactions between variables.Here is your setup:from sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LogisticRegressionfrom sklearn.preprocessing import PolynomialFeatures# Generate synthetic data for binary classificationX, y = make_classification(n_samples=1000, n_features=3, n_classes=2, random_state=42)# Split the data into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Apply polynomial features manuallypoly = PolynomialFeatures(degree=2)X_train_poly = poly.fit_transform(X_train)X_test_poly = poly.transform(X_test)What is the correct procedure to fit a logistic regression model on the training data after transforming it with polynomial features, and how should predictions be made on the test data?model = LogisticRegression()model.fit(X_train, y_train)y_pred = model.predict(X_test)model = LogisticRegression()model.fit(X_test_poly, y_test)y_pred = model.predict(X_train_poly)model = LogisticRegression()model.fit(X_train_poly, y_train)y_pred = model.predict(X_test_poly)model = LogisticRegression()model.fit(X_train_poly, y_test)y_pred = model.predict(X_test_poly)

A machine learning model is trained to predict customer churn for a telecom company. The model achieves high accuracy during training but performs poorly when applied to new, unseen data. What could be the most likely cause of this issue?a)Inappropriate choice of evaluation metricb)Insufficient training datac)Underfittingd)Overfitting

Which of the following focuses on understanding and predicting the rate at which customers stop using a product or service? A. Cohort Analysis B. Churn Analysis C. Retention Analysis D. Milestone Analysis

How can improving Customer Lifetime Value (CLV) help reduce churn rate?Answer choicesSelect only one optionREVISITBy encouraging businesses to focus on customer retention and satisfactionBy encouraging businesses to focus on increasing product pricesBy encouraging businesses to focus on hiring more employeesBy encouraging businesses to focus on expanding their product line