Telecom Churn Prediction Model

Project Title

Telecom Churn Prediction Model

Project Area of Specialization Artificial IntelligenceProject Summary

This project on Churn Prediction in Telecom. This would be helpful in the Telecom Sector. This project offers advanced methods for determining churn within telecom. Churn (loss of customers to competition) is a problem for telecom companies because it is expensive to acquire a new customer and companies want to retain their existing customers, hence the company tries to identify customers who are likely to churn and then targets those customers with special programs or incentives. Churn (loss of customers) is one of the biggest problems in the telecom industry. Research has shown that the average monthly churn rate among the top 4 wireless carriers is 1.9% - 2%. In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition.

Customer churn happens in the Software-as-a-Service business similarly as it is in subscription-based industries like the telecommunications industry. But companies lack the knowledge about the factors that lead to customers churn and are unable to react to it in time. it is necessary for companies to research customer churn prediction in order to react to customer churn in time. The customer churn prediction in a quantitative method by utilizing several different machine learning algorithms with Python, namely recurrent neural network, convolutional neural network, support vector machine, random forest algorithm, and many more. data were collected from the case company’s database and manipulated to fit the algorithms. The data-set includes customer business data such as spend, customer platform usage data, customer service history data, and customer feedback data on service quality. Grid search was carried out to find the optimal hyperparameters for each machine learning algorithm. The models of the algorithms were then trained and evaluated with the fitted data using the optimal hyperparameters. After the models had been trained, the test data was run through the models to get the results of the analysis.

Project Objectives

• First of all, we look for the requirements of the customer. • Highlighting the main factors influencing customer churn.

• Finding out the best model for our business case & providing an executive summary.

• Use various ml algorithms to build prediction models, and evaluate the accuracy and performance of these models.

• their expected demand from the organization in order to dominate over other service providers.

• The analysis is done by making patterns out of the previous data stored by the customer or from some authentic source.

• The data is trained with the help of artificial intelligence algorithms and the model is trained on the bases of the result drawn from the algorithm.

Customer churn means the occurrence of an event where a customer quits using a company’s products or services churn to customer defection and tie it more to a paid business context as they define it as a customer ending commercial relations with a company. It is often mentioned with customer retention, which refers to the ability to retain the current customers using your product or service. The whole idea is to classify the customers into the churned and non-churner groups in the telecom industry. To do the same, it is essential to identify the reason for churning, based on the past behavior of customers. A company should early detect the customers who are likely to churn from the company or services. It is most important for any company or service provider to retain the customers rather than look for new customers. Hence, accurate prediction of the churner group helps in predicting the company’s profit.

Project Implementation Method

Section A: Data Preprocessing

Step 1: Import relevant libraries.

Step 2: Set up the current working directory

Step 3: Import the dataset

Step 4: Evaluate data structure

Step 5: Check target variable distribution

Step 6: Clean the dataset

Step 7: Take care of missing data

Section B: Data Evaluation

Step 9: Exploratory Data Analysis

Step 10: Encode Categorical data

Step 11: Split the dataset into dependent and independent variables

Step 12: Generate training and test datasets

Step 13: Remove Identifiers

Step 14: Conduct Feature Scaling

Section C: Model Selection

Step 15.1: Compare Baseline Classification Algorithms

Section D: Model Evaluation

Step 16: Train & evaluate Chosen Model

Step 17:Predict Feature Importance

Section E: Model Improvement

Step 18:Hyper parameter Tuning via Grid Search

Section F: Future Predictions

Step 19: Compare predictions against the test set

Step 20: Format Final Results

Section G: Model Deployment

Step 21: Save the model

Benefits of the Project

Churn (loss of customers) is one of the biggest problems in the telecom industry. Research has shown that the average monthly churn rate among the top 4 wireless carriers is 1.9% - 2%. In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition.

Technical Details of Final Deliverable

Customer churn happens in the Software-as-a-Service business similarly as it is in subscription-based industries like the telecommunications industry. But companies lack the knowledge about the factors that lead to customers churn and are unable to react to it in time. it is necessary for companies to research customer churn prediction in order to react to customer churn in time. The customer churn prediction in a quantitative method by utilizing several different machine learning algorithms with Python, namely recurrent neural network, convolutional neural network, support vector machine, random forest algorithm, and many more. data were collected from the case company’s database and manipulated to fit the algorithms. The data-set includes customer business data such as spend, customer platform usage data, customer service history data, and customer feedback data on service quality. Grid search was carried out to find the optimal hyperparameters for each machine learning algorithm. The models of the algorithms were then trained and evaluated with the fitted data using the optimal hyperparameters. After the models had been trained, the test data was run through the models to get the results of the analysis. Many approaches were applied to predict churn in telecom companies. Most of these approaches have used machine learning and data mining techniques. 

We also designed a web-based application on which a company enters users' details and after comparing results with the algorithm, the output will show on the web application whether a user will churn or not. This will help the telecom industry to increase its profit and also help in fulfilling the need of the users.

Final Deliverable of the Project Software SystemCore Industry MediaOther Industries IT Core Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Partnerships to achieve the GoalRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	53000
SSD	Equipment	1	10000	10000
Paid Courses+ Travelling charges +Internet Charges +Research Papers	Miscellaneous	5	2000	10000
GPU	Equipment	1	20000	20000
Ram	Equipment	1	10000	10000
USB	Equipment	1	3000	3000