Telecom_CustomerChurn_EDA

This dataset is related to customers of a telecom company and whether they have churned or not. It includes information such as customer demographics, account information, usage patterns, and customer service call details.

The telecom dataset contains information about the customer's usage patterns such as customer demographics, account information, and customer service call details. The dataset contains a total of 20 columns and 3,000+ rows of data. The columns include features such as state, account length, international plan, tenure, voice mail plan, number of customer service calls, and whether the customer has churned or not.

The dataset is intended for use in predicting customer churn, which is when a customer stops using the telecom company's services. The target variable, "Churn," indicates whether the customer has churned or not. By analyzing the data, we can gain insights into what factors are most likely to lead to customer churn and take action to reduce churn rates.

Table of Content

1. Load Dataset
2. Import libraries
3. Exploratory Data Analysis
4. Checking null values and cleaning Data
5. Visualisation
   • Churn Percentage
   • State wise churn rate
   • Top 5 highest and lowest churn rate
   • Analysising customer service
6. Conclusion

🛠 Skills

Python

Data Manupulation :-

Numpy, Pandas

Data Visualisation :-

Seaborn, Matplotlib

Methods

Exploratory data analysis(EDA):

Explore the data to identify patterns, trends, and insights. And use graph and charts to make to visualize relationships between variables.

Data visualization:

understand different charts, graphs, and other visualizations to present the data in a way that is easy to understand and interpret.

Objective

In this case study, we are going to analyse the reasons for customer churn in the telecom industry. Using some factors like churn rate, plan, and charges for their services that are most likely to lead to customer churn.

By identifying patterns and trends in the data, we can develop strategies to retain customers and increasecustomer loyalty.

Overall, the objective of analysing the Telecom Churn dataset is to understand customer behaviour and develop strategies to reduce churn rates, increase customer retention, and ultimately improve the profitability and sustainability of the telecom company.

Findings

1. The overall churn rate of the customer is 15%, which is huge.
2. Area code, account length and day, night, evening charges doesnot play any role in the churn rate.
3. Some states who have higher churn rate and some states who very low churn rate. Churn rate may be vary because of connectivity
4. There is no relation between churn rate and account length, voice mail messages, or area code.
5. Customer service centre their churn rate is also high, which means they didn't find the solution of their issue