Case Studies

4.1.1 Types of Predictive Models

a. Regression Models:

Case Study 1: Housing Price Prediction

Problem: A real estate company wants to predict house prices based on various features such as square footage, number of bedrooms, and location.

Solution:

1. Data Gathering:
   • Collect historical data on house prices, including features like square footage, number of bedrooms, location, and any other relevant factors.
   • Utilize real estate databases, property listings, or collaborate with local real estate agents to gather comprehensive and up-to-date information.
2. Model Selection:
   • Choose regression models like linear regression or multiple regression, considering the continuous nature of the target variable (house price).
   • Consider factors such as interpretability and ease of understanding for stakeholders.
3. Model Evaluation:
   • Use evaluation metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) to measure the accuracy of the regression model in predicting house prices.
   • Validate the model on a test dataset to ensure its generalizability.

Case Study 2: Sales Forecasting for a Retail Store

Problem: A retail store wants to predict monthly sales based on factors like advertising spending, promotions, and seasonality.

Solution:

1. Data Gathering:
   • Collect historical sales data, advertising spending, and promotion details for the past few years.
   • Consider external factors like economic indicators and seasonal trends.
2. Model Selection:
   • Opt for regression models like linear regression or polynomial regression, depending on the complexity of relationships between variables.
   • Keep the model interpretable for better decision-making.
3. Model Evaluation:
   • Evaluate the model using metrics like Mean Squared Error (MSE) to measure the accuracy of sales predictions.
   • Validate the model’s performance using a holdout dataset or cross-validation.

b. Classification Models:

Case Study 1: Customer Churn Prediction

Problem: A telecom company wants to predict customer churn based on usage patterns, customer service interactions, and contract details.

Solution:

1. Data Gathering:
   • Collect historical customer data, including usage patterns, customer service interactions, and contract details.
   • Include labels indicating whether a customer churned or not.
2. Model Selection:
   • Explore classification models like logistic regression or decision trees, which are suitable for binary outcomes (churn or no churn).
   • Consider model interpretability and the ability to explain predictions to stakeholders.
3. Model Evaluation:
   • Use metrics like accuracy, precision, recall, and F1 score to assess the performance of the classification model.
   • Perform cross-validation to ensure robustness.

Case Study 2: Spam Email Detection

Problem: An email service provider wants to classify emails as spam or non-spam based on various features like sender, subject, and content.

Solution:

1. Data Gathering:
   • Collect a diverse dataset of emails labeled as spam or non-spam.
   • Extract features such as sender information, subject line, and content.
2. Model Selection:
   • Consider classification models like support vector machines or decision trees, which are effective for binary classification tasks.
   • Balance model complexity with interpretability.
3. Model Evaluation:
   • Use metrics like accuracy, precision, recall, and F1 score to assess the model’s performance.
   • Regularly update the model as email patterns change over time.

4.1.2 Model Selection and Evaluation Criteria

a. Model Selection:

Case Study 1: Disease Diagnosis using Medical Data

Problem: A healthcare provider wants to predict the presence or absence of a disease based on medical test results and patient information.

Solution:

1. Data Gathering:
   • Collect medical data including test results, patient history, and relevant health indicators.
   • Collaborate with hospitals or healthcare institutions to obtain a diverse dataset.
2. Model Selection:
   • Choose appropriate classification models like logistic regression or support vector machines, considering the interpretability and complexity required for medical decision-making.
   • Ensure compliance with medical regulations and ethical considerations.
3. Model Evaluation:
   • Use evaluation criteria such as precision, recall, and F1 score, as misdiagnoses can have significant consequences.
   • Involve medical professionals in the evaluation process to ensure clinical relevance.

Case Study 2: Credit Scoring for Loan Approval

Problem: A financial institution wants to predict the creditworthiness of applicants for loan approval.

Solution:

1. Data Gathering:
   • Collect historical data on loan applicants, including financial information, credit history, and employment details.
   • Ensure the dataset is representative of the target population.
2. Model Selection:
   • Opt for classification models like logistic regression or decision trees, considering the need for transparency and adherence to regulatory requirements.
   • Consider the balance between false positives and false negatives, as both have financial implications.
3. Model Evaluation:
   • Evaluate the model using metrics like accuracy, precision, and ROC-AUC, as the consequences of misclassifying creditworthiness are significant.
   • Regularly update the model to adapt to changes in economic conditions.

b. Evaluation Criteria:

Case Study 1: Fraud Detection in Financial Transactions

Problem: A bank wants to detect fraudulent transactions based on customer transaction history and behavior.

Solution:

1. Data Gathering:
   • Collect transaction data, including details on transaction amounts, locations, and customer behavior.
   • Ensure the dataset includes labeled instances of fraudulent and non-fraudulent transactions.
2. Model Selection:
   • Choose appropriate classification models like logistic regression or ensemble methods, considering the need for high precision in identifying fraud.
   • Incorporate features that capture anomalies in transaction patterns.
3. Model Evaluation:
   • Utilize evaluation criteria like precision, recall, and F1 score, with a focus on minimizing false positives to prevent inconvenience to legitimate customers.
   • Implement ongoing monitoring and updates to adapt to evolving fraud patterns.

Case Study 2: Employee Attrition Prediction

Problem: A company wants to predict employee attrition based on factors such as job satisfaction, work-life balance, and performance.

Solution:

1. Data Gathering:
   • Collect HR data, including employee satisfaction scores, work-life balance assessments, and performance metrics.
   • Ensure the dataset includes labels indicating whether an employee left the company or not.
2. Model Selection:
   • Opt for classification models like logistic regression or decision trees, considering interpretability and the ability to identify factors contributing to attrition.
   • Include both quantitative and qualitative features in the model.
3. Model Evaluation:
   • Use evaluation metrics like accuracy, precision, and recall, with a focus on identifying potential attrition cases early.
   • Conduct regular surveys and updates to capture changing employee sentiments and external factors.

These case studies provide a comprehensive overview of predictive modeling, model selection, and evaluation criteria across both regression and classification scenarios. They emphasize the importance of data gathering, appropriate model selection, and careful evaluation to ensure effective and ethical use of predictive models in various domains.