Problem Statement

Predicting customer churn is a critical task for telecommunications companies aiming to retain

their customers and reduce revenue loss. Customer churn refers to the phenomenon where

customers discontinue using the company's services. By accurately predicting which customers

are likely to churn, the company can take proactive measures to improve customer satisfaction

and retention.

For this assignment, you are provided with a dataset containing various features related to

customer demographics, account information, and service usage patterns. The dataset also

includes random missing values to simulate real$-$world data issues.

The objective is to build a machine learning classification model to predict whether a customer

will churn $($i$.$e$.,$ stop using the company's services$).$ You will need to preprocess the data, handle

missing values, perform feature engineering, build and evaluate classification models, and

provide insights based on the model's performance.

Dataset: customer$\_$churn$\_$dataset.csv $($uploaded as separate file$)$

Meta Data: metadatafile$\_$customerchurndata.txt $($uploaded as separate file$)$

Import Libraries$/$Dataset

a$.$ Download the dataset.

b$.$ Import the required libraries