Week 6 Individual Project Assignment Write At Least 3-4 Page

Week 6 Individual Project assignment write At Least A 3 4 Page Apa Form

Write at least a 3-4-page APA formatted paper on a business problem that requires data mining. Explain why the problem requires data mining, the general approach you plan to take, the kind of data you plan to use, and how you plan to get the data. Describe your problem, approach (specific ex: CRISP-DM, SEMMA, KDD, etc.), dataset (specific ex: historical artifacts, transactions, etc.), data analysis/tools and techniques (specific algorithm, specific technique, specific method such as clustering, sampling, etc.), and how your data mining will attempt to solve your problem. Include an abstract and a conclusion, and a reference page with at least 3-5 references. All reports should be submitted in MS Word.

Paper For Above instruction

Data mining has become an essential component in solving complex business problems by extracting valuable patterns and insights from large datasets. This paper explores a business problem involving customer churn in a telecommunications company, elucidates why it necessitates data mining, outlines the approach and data to be used, and details the analytical techniques employed to address this issue.

Problem Description: The primary business challenge addressed is customer churn, which is the rate at which customers leave the service provider. High churn rates adversely affect revenue and profitability, making it imperative for the company to understand the factors influencing customer departure and to predict those at high risk of churning.

Approach Selection: The specific data mining approach adopted will be the CRoss-Industry Standard Process for Data Mining (CRISP-DM). This methodology emphasizes an iterative process involving business understanding, data understanding, data preparation, modeling, evaluation, and deployment. It ensures structured progression from problem definition to actionable insights.

Data Description: The dataset will comprise historical customer data, including demographic details, service usage patterns, billing information, and customer support interactions. Such data typically include transaction logs, call records, service complaint records, and account information collected over time.

Data Collection Methodology: Data acquisition will involve extracting information from the company's customer database, CRM systems, and billing platforms. Data cleaning and transformation processes will be implemented to address missing values, inconsistent entries, and to normalize data, ensuring readiness for analysis.

Analysis Techniques and Tools: The primary techniques will include classification algorithms like decision trees and logistic regression to predict customer churn, along with clustering techniques such as K-means to identify customer segments. Feature selection methods will also be utilized to enhance model accuracy, and sampling techniques will ensure balanced classes for better predictive performance.

Problem Solution via Data Mining: By applying these techniques, the model will identify key attributes and patterns associated with customer churn, enabling proactive retention strategies. For instance, the decision tree may reveal that frequent service complaints, high billing, and low engagement are significant predictors of churn, allowing targeted marketing efforts to retain at-risk customers.

Conclusion: Utilizing data mining in this manner allows the telecommunications company to transition from reactive to proactive customer retention, leveraging data-driven insights to improve customer satisfaction and profitability.

References

• Kim, M., & Lee, S. (2021). Data mining techniques for customer churn prediction in telecom industry. Journal of Business Analytics, 10(2), 150-165.
• Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37-54.
• Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann.
• Ngai, E. W. T., Xiu, L., & Tsang, D. K. Y. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592-2602.
• Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 15.