Criteria 1: Quality of Exploratory Data Analysis (20%) [20]

---
module_code: COM747
module_title: Data Science and Machine Learning
course_work: CW2 Group Work Component - 01
tutor_name: Iftikhar Afridi 
semester: S3 2023-24
partner: Ulster University
---

# Criteria 1: Quality of Exploratory Data Analysis (20%) [20]
## Marks: 14 - 20 (Distinction)
- Comprehensive EDA with clear, well-structured analysis of the dataset.  
- Data cleaning and preparation steps are thoroughly explained (e.g., handling missing values, outliers, encoding).  
- Includes meaningful visualizations (e.g., histograms, scatter plots, heatmaps) that are visually appealing and easy to interpret.  
- Demonstrates deep insight by connecting findings from EDA to the goals of the logistic regression model.  
- Uses appropriate statistical summaries and charts to showcase relationships between variables (e.g., correlations).  

## Marks: 12 - 13.8 (Commendation)
- Good EDA with some meaningful analysis of the dataset.  
- Most steps in data cleaning and preparation are explained but lack minor details.  
- Includes useful visualizations that are moderately well presented and explained.  
- Insights derived from EDA are present but lack depth or comprehensive interpretation.  

## Marks: 10 - 11.8 (Pass)
- EDA is basic and covers minimal aspects of the dataset.  
- Few or unclear data preparation steps; limited explanation of cleaning/processing.  
- Visualizations are provided but are of low quality or poorly explained.  
- Limited or unclear connection between the EDA and model goals.  

## Marks: 0 - 9.8 (Fail)
- EDA is absent, extremely poor, or irrelevant.  
- No or very few visualizations, and those present are unclear or poorly formatted.  
- Little to no explanation of data cleaning, preparation, or insights.  


# Criteria 2: Quality of Definitions and Descriptions of Logistic Regression and Technical Methods (20%) [20]
## Marks: 14 - 20 (Distinction)
- Provides accurate, detailed, and comprehensive definitions of logistic regression.  
- Explains the theory behind logistic regression, including its mathematical foundation (e.g., sigmoid function, odds ratio, cost function).  
- Clearly outlines advantages, limitations, and real-world applications of logistic regression.  
- References additional methods or concepts (e.g., feature scaling, regularization, cross-validation) with excellent clarity and context.  

## Marks: 12 - 13.8 (Commendation)
- Offers good definitions and explanations of logistic regression.  
- Includes an overview of key concepts but may lack advanced insight or clarity in some areas.  
- Some mention of advantages, limitations, or applications is provided but not elaborated on.  
- Explanations of supporting methods (e.g., feature scaling) are present but lack depth.  

## Marks: 10 - 11.8 (Pass)
- Definitions and descriptions are basic or somewhat unclear.  
- Limited explanation of mathematical concepts or supporting methods.  
- Few or no references to advantages, limitations, or applications.  

## Marks: 0 - 9.8 (Fail)
- Definitions are unclear, incorrect, or missing.  
- No explanation of key concepts, advantages, limitations, or supporting methods.  
- Technical understanding is absent or very poor.  

# Criteria 3: Quality of Demonstration and Interpretation of Results (60%) [60]
## Marks: 42 - 60 (Distinction)
- Excellent demonstration of model implementation, with clear coding steps in Python/R.  
- Code is well-structured, properly commented, and aligns with best practices.  
- Provides a clear walkthrough of the dataset splitting process, model training, and testing.  
- Includes comprehensive interpretations of evaluation metrics (e.g., accuracy, precision, recall, F1-score, ROC curve, AUC).  
- Explains odds ratios and model coefficients in relation to the dataset and problem context.  
- Includes additional elements like hyperparameter tuning or model validation.  

## Marks: 36 - 41.4 (Commendation)
- Good coding demonstration with clear and understandable steps.  
- Code is mostly well-commented but may lack minor details or best practices.  
- Provides a walkthrough of basic model steps but may not include additional insights (e.g., hyperparameter tuning).  
- Interpretations of results are present but may lack depth or clarity for some metrics.  

## Marks: 30 - 35.4 (Pass)
- Coding demonstration is basic or unclear in parts.  
- Limited commenting or explanation of code.  
- Steps in the model-building process are missing or poorly explained.  
- Interpretation of results is shallow or does not connect results to the problem context.  

## Marks: 0 - 29.4 (Fail)
- Coding demonstration is missing or very poor.  
- Code is poorly written, uncommented, or incomplete.  
- No interpretation of results or evaluation metrics.  
- Presentation lacks planning, structure, or coherence.