You will have to select a topic for your capstone project.It is up to you to decide if you want to perform a project for a company/business of your choice

Project selection: You will have to select a topic for your capstone project. It is up to you to decide if you want to perform a project for a company/business of your choice, or if you want to use a dataset found on the Internet and answer questions about it. The content of your project must be comprehensive enough to justify the work of one team for an entire semester.

Note: there is a tendency for students to use datasets from Kaggle.I would check less explored options. I don`t have anything against Kaggle; it is a good source of datasets to play with and find good insights about models. However, many of their datasets have been extensively used. Reports, codes, and results about those can be easily found on the Internet. This course is not about duplicating what others have done but about developing something new. In other words, your work must be original. An important part of your project is descriptive analytics (data understanding, data preparation, and visualization). I cannot approve a dataset if its descriptive analytics is available online or if it is extensively studied by others and related projects and reports can be found online. In that case, you would need to change your dataset/topic. 

The selected dataset should ideally have at least 50,000 records. However, I might accept datasets with at least 10,000 records depending on the originality of the topic.

I will meet every team in the second week of the semester to discuss their selected topic. So, please submit a document that includes the following information by the deadline.

• Tentative name of the project
• What is the problem? What is the issue that needs to be studied?
• Why is this problem important? Who will benefit from addressing this issue?
• What are the specific questions that you want to answer with your project? I understand that some of these questions may change as you make progress with your project, but I need to know at least what is on your mind and how you plan on tackling those questions.
• A brief description of columns in the dataset (no visualization or analysis).
• Link to the data that you will use. If the data is not available online (e.g., you perform a project for a company/business), attach the data file to your submission. 

You will have to select a topic for your capstone project. It is up to you to decide if you want to perform a project for a company/business of your choice.

Please note that if I do not approve your dataset, you must select a new dataset and resubmit the assignment. Your score on the assignment will be penalized by 10% for every resubmission.

You will have to select a topic for your capstone project. It is up to you to decide if you want to perform a project for a company/business of your choice.

Plagiarised (DO NOT COPY)

Capstone Project Proposal

Tentative Name of the Project: "Analyzing Customer Churn in the Saudi Telecom Industry"

Problem Statement: High customer churn rates are a significant issue in the telecom industry in Saudi Arabia. Customer churn, the percentage of customers who stop using a company`s services during a given period, leads to lost revenue and increased acquisition costs. Understanding and mitigating the factors leading to churn is critical for telecom companies to sustain their market position and profitability.

Importance: This problem is crucial for several reasons:

• Revenue Impact: Customer churn directly reduces revenue and profitability. Retaining customers is often more cost-effective than acquiring new ones.
• Customer Loyalty: Reducing churn enhances customer loyalty, leading to longer-term customer relationships and increased lifetime value.
• Competitive Advantage: Companies that effectively manage churn can differentiate themselves from competitors by offering superior customer experience.
• Operational Efficiency: Understanding churn factors helps in refining business processes and service offerings to meet customer needs better.

Addressing this issue benefits telecom companies in Saudi Arabia by:

• Increasing customer retention rates.
• Enhancing customer satisfaction and loyalty.
• Improving overall financial performance.

Specific Questions:

1. What are the main factors contributing to customer churn?
   • Identifying key variables such as service quality, customer service interactions, and billing issues that influence churn.
2. How can we predict customer churn using historical data?
   • Utilizing machine learning models to forecast churn based on historical customer data and behaviors.
3. What strategies can be implemented to reduce churn rates?
   • Developing targeted retention strategies such as personalized offers, improved customer support, and proactive engagement.

Dataset Description: The dataset contains over 50,000 records of telecom customers, including:

• Customer ID: Unique identifier for each customer.
• Demographic Information: Age, gender, location.
• Service Subscription Details: Plan type, subscription duration, usage patterns.
• Customer Service Interactions: Frequency and type of interactions with customer support.
• Billing Information: Payment history, billing disputes.
• Churn Status: Indicator of whether the customer has churned (Yes/No).

This comprehensive dataset allows for detailed analysis and model building to predict churn and identify contributing factors.

Link to the Data: Telecom Customer Churn Data (Replace with an actual link or attach the file if the data is not publicly available).

Project Implementation Plan:

1. Data Understanding and Preparation:
   • Clean and preprocess the dataset to handle missing values, outliers, and inconsistencies.
   • Perform exploratory data analysis (EDA) to understand the distribution and relationships of variables.
2. Descriptive Analytics:
   • Generate visualizations and summary statistics to identify patterns and trends in customer behavior.
3. Predictive Modeling:
   • Apply machine learning algorithms (e.g., logistic regression, decision trees, random forests) to build predictive models for churn.
   • Evaluate model performance using metrics such as accuracy, precision, recall, and ROC-AUC.
4. Strategy Development:
   • Based on model insights, develop strategies to mitigate churn, such as improving service quality, enhancing customer support, and offering personalized incentives.
5. Reporting and Recommendations:
   • Compile findings into a comprehensive report.
   • Provide actionable recommendations for the telecom company to implement.

Conclusion: This capstone project aims to address the critical issue of customer churn in the Saudi telecom industry. By leveraging a detailed dataset and advanced analytics techniques, the project will provide valuable insights and strategies to reduce churn and improve customer retention. This will ultimately enhance the financial performance and competitive position of the telecom company.

This proposal outlines the scope, significance, and implementation plan for the project, ensuring a comprehensive approach to tackling customer churn.