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# CDC Diabetes Health Indicators Analysis

## Project Overview
This project aims to explore the CDC Diabetes Health Indicators Dataset to identify key health, lifestyle, and socio-economic factors associated with diabetes prevalence.  
We perform **exploratory data analysis (EDA)**, **data cleaning**, and **Logistic Regression modeling** on balanced data.  
The goal is to extract actionable insights, understand class imbalances, and identify the most influential predictors of diabetes.devoir


## Dataset

- **Name**: CDC Diabetes Health Indicators Dataset  
- **Source**: Downloaded from Kaggle using `kagglehub`  
- **Content**: Over 250,000 observations of health and lifestyle factors  
- **Format**: CSV, with multiple columns corresponding to health indicators, lifestyle choices, and socio-economic factors  

**Initial Verification**: The dataset contains `df.shape[0]` rows and `df.shape[1]` columns.


## 2.2 Data Cleaning

- **Duplicate Handling**: Checked for exact duplicate rows across all features and removed them to ensure each observation is unique.  
- **Missing Values Check**: Verified that there are no missing values in the dataset.  
- **Result**: The dataset is clean, complete, and ready for analysis.


## 2.3 Target Variable Analysis: Class Imbalance

- **Objective**: Assess the distribution of the target variable `Diabetes_binary` to detect potential class imbalance.  
- **Quantification**: Calculated counts and percentages of each class.  
- **Visualization**: Bar plot illustrating the number of non-diabetic vs diabetic/pre-diabetic individuals.  

**Observation**: The dataset exhibits a class imbalance, which is common in health datasets and must be considered for model training.

**Class Imbalance Visualization**:  
![Class Imbalance](classibalance.png)


## 4.1 Socio-Economic Factor: Income vs. Diabetes

- **Objective**: Explore the relationship between income levels (1 = lowest, 8 = highest) and diabetes prevalence.  
- **Calculation**: Average diabetes rate per income category.  
- **Visualization**: Bar plot showing diabetes prevalence across income levels.

**Observation**:  
- Diabetes prevalence decreases steadily with increasing income.  
- Low-income categories (1 and 2) show ~25% diabetes prevalence, while the highest income category (8) shows ~10%.  
- Confirms the hypothesis that socio-economic status strongly affects diabetes risk.

**Income vs Diabetes Visualization**:  
![Income vs Diabetes](income.png)


## 4.2 Socio-Economic Factor: Education vs. Diabetes

- **Objective**: Assess how education level (1 = lowest, 6 = highest) correlates with diabetes prevalence.  
- **Calculation**: Average diabetes rate per education level.  
- **Visualization**: Bar plot comparing prevalence across educational attainment.

**Observation**:  
- Lowest education levels (1 and 2) have the highest diabetes prevalence (~30%).  
- Highest education levels (5 and 6) show significantly lower prevalence.  
- Reinforces the finding from the income analysis: socio-economic factors are strong, interconnected predictors.

**Education vs Diabetes Visualization**:  
![Education vs Diabetes](education.png)


## 4.3 Lifestyle Factors: Smoking and Physical Activity

- **Objective**: Evaluate how key lifestyle factors affect diabetes prevalence.  
- **Factors Analyzed**: Smoking status and regular physical activity.  
- **Visualization**: Side-by-side bar plots comparing diabetes prevalence by smoking status and physical activity.

**Observations**:  
- Smokers have a higher diabetes rate than non-smokers.  
- Physically active individuals have a substantially lower diabetes rate.  
- Confirms that harmful habits increase risk while healthy behaviors are protective.

**Visualization**:  
![Smoking and Physical Activity](smokingandphysical.png)


## 4.4 Physiological Factor: Body Mass Index (BMI)

- **Objective**: Examine BMI distributions by diabetes status.  
- **Visualization**: Box plot comparing BMI between diabetic and non-diabetic individuals.  
- **Reference**: Horizontal line at BMI = 30 (obesity threshold).

**Observations**:  
- Diabetic individuals have a substantially higher median BMI than non-diabetics.  
- Most diabetic individuals lie above BMI 30, confirming BMI as a high-impact predictor.  
- The distributions are well separated, indicating BMI is highly discriminative.

**Visualization**:  
![BMI Distribution](BMI.png)


## 4.5 Physiological Factor: Age

- **Objective**: Compare age distributions between diabetic and non-diabetic individuals.  
- **Visualization**: Box plot of age by diabetes status.

**Observations**:  
- Diabetic individuals are older on average, with a wider upper quartile.  
- Non-diabetics are younger, with a tighter distribution.  
- Age is an important demographic predictor of diabetes.

**Visualization**:  
![Age Distribution](Age.png)


## 5.1 Logistic Regression: Balanced Training

- **Objective**: Train a Logistic Regression model on **balanced and scaled training data** to address class imbalance.  
- **Evaluation**: Tested on the original (unbalanced) test set.  
- **Metrics**: Confusion matrix, classification report (precision, recall, F1-score), and ROC AUC score.

**Key Insights**:  
- Balancing the training data improves recall for the diabetic class, reducing false negatives.  
- F1-score shows improved balance between precision and recall for minority class.  
- ROC AUC confirms strong discriminative power of the model.  
- Suggests Logistic Regression is an effective baseline; further exploration with non-linear models could improve performance.


## 5.2 ROC Curve and AUC Analysis

- **Objective**: Visualize model performance and evaluate discriminative power using the ROC curve.  
- **Visualization**: Orange line = model performance; Blue diagonal = random classifier baseline (AUC = 0.50).

**Observation**:  
- The model’s ROC curve lies well above the baseline, confirming strong discriminative ability.  
- Final AUC score quantitatively supports model effectiveness.  
- Confirms that balancing the training data yields a robust baseline model for diabetes prediction.

**Visualization**:  
![ROC Logistic Regression](ROC_Logistic_Regression.png)


## Final Insights

- Socio-economic factors (Income, Education) are strongly correlated with diabetes prevalence.  
- Lifestyle factors such as Smoking and Physical Activity significantly influence diabetes risk.  
- Physiological factors (BMI, Age) are dominant predictors.  
- Logistic Regression trained on balanced data provides a robust and interpretable baseline model.  
- Future work could include testing non-linear models, ensemble methods, or feature engineering to further improve predictive performance.


## How to Use This Dataset and Notebook

- The dataset can be used to explore health, lifestyle, and socio-economic predictors of diabetes.  
- The notebook provides step-by-step EDA and visualization examples.  
- Logistic Regression serves as a baseline classification model.  
- All plots are included and can be reused in reports or presentations.