Project-1: Predict cancer mortality rates for US counties
1. Overview of the Model
The model is a multivariate Ordinary Least Squares (OLS) regression designed to predict cancer mortality rates for different counties in the United States. It leverages a dataset that incorporates diverse information, including demographic, economic, educational, and healthcare-related features, to build a predictive model for the “TARGET_deathRate” variable.
2. Motivation of the Model
The primary motivation behind building this model is to provide a tool that can assist in understanding and predicting cancer mortality rates across counties. This information is crucial for public health initiatives, resource allocation, and policy planning. By analyzing the complex interplay of various factors associated with cancer mortality, the model aims to uncover insights that can aid in improving healthcare strategies and outcomes.
3. Success Metrics
The success of the model will be evaluated using several key metrics:
(Adjusted) R-squared: Measures the proportion of variance in the target variable that the model explains. A higher R-squared value indicates a better fit. Root Mean Squared Error (RMSE): Quantifies the average magnitude of prediction errors. A lower RMSE suggests better predictive accuracy.
4. Requirements & Constraints
4.1. Functional Requirements
Data Processing: Preprocess the dataset to handle missing values, outliers, and categorical variables. Model Building: Develop the multivariate OLS regression model using the processed data. Model Evaluation: Assess the model’s performance using metrics like R-squared and RMSE. Diagnostics: Perform diagnostics for linearity, independence of errors, heteroskedasticity, normality of residuals, and multicollinearity.
4.2. Non-Functional Requirements
Accuracy: The model should provide accurate predictions of cancer mortality rates. Efficiency: The model should be efficient in terms of computation time and resource usage. Interpretability: The model’s coefficients and statistical significance should be interpretable and insightful. Scalability: The model should be scalable to accommodate additional data or updates.
4.3. Constraints
The model’s accuracy is limited by the quality and scope of the provided data. The model’s predictive power may vary for different counties due to unique local factors.
4.4. Out-of-Scope
The model doesn’t address causal relationships; it focuses on predictive power. External factors, such as medical advancements, are not considered in this model.
5. Methodology
5.1. Problem Statement
The problem involves predicting cancer mortality rates for US counties using a multivariate OLS regression approach.
5.2. Data
The dataset comprises various features, including demographic, economic, education, and health-related variables for each county.
5.3. Techniques
The key technique employed is multivariate Ordinary Least Squares (OLS) regression. The model estimates coefficients that best fit the relationship between the predictor variables and the target variable, “TARGET_deathRate.”
6. Architecture
The model’s architecture involves data preprocessing, model training using OLS regression, model evaluation, and diagnostics. It follows a linear regression framework, with features as input and “TARGET_deathRate” as the output.
7. Pipeline
Data Preprocessing: Handle missing values, outliers, and categorical variables. Model Building: Use the OLS regression technique to build the predictive model. Model Evaluation: Calculate R-squared and RMSE to assess the model’s performance. Diagnostics: Conduct various diagnostic tests to validate regression assumptions.
8. Conclusion
In conclusion, this model offers a data-driven approach to predict cancer mortality rates in US counties. By incorporating a wide range of features, it seeks to provide insights into the complex factors influencing cancer mortality. The model’s success will be determined by its accuracy in predicting “TARGET_deathRate” and its ability to provide interpretable insights that contribute to informed decision-making in public health and policy planning.