Smoking Health Assessment

Smoking Health Assessment/readme.md

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+# Income Classification Model
+
+## Overview
+This project involves building and evaluating various machine learning models to classify gross income based on demographic and smoking-related features. The dataset is processed, and multiple models are trained and evaluated to find the best performing model.
+
+## Dataset
+The dataset includes demographic information, smoking habits, and gross income of individuals. Below is a sample of the dataset:
+
+| | gender | age | marital_status  | highest_qualification | nationality | ethnicity | gross_income       | region    | smoke | amt_weekends | amt_weekdays | type     |
+|---|--------|-----|----------------|-----------------------|-------------|-----------|--------------------|-----------|-------|--------------|--------------|----------|
+| 1 | Male   | 38  | Divorced       | No Qualification      | British     | White     | 2,600 to 5,200     | The North | No    | NA           | NA           |          |
+| 2 | Female | 42  | Single         | No Qualification      | British     | White     | Under 2,600        | The North | Yes   | 12           | 12           | Packets  |
+| 3 | Male   | 40  | Married        | Degree                | English     | White     | 28,600 to 36,400   | The North | No    | NA           | NA           |          |
+| 4 | Female | 40  | Married        | Degree                | English     | White     | 10,400 to 15,600   | The North | No    | NA           | NA           |          |
+| 5 | Female | 39  | Married        | GCSE/O Level          | British     | White     | 2,600 to 5,200     | The North | No    | NA           | NA           |          |
+| 6 | Female | 37  | Married        | GCSE/O Level          | British     | White     | 15,600 to 20,800   | The North | No    | NA           | NA           |          |
+| 7 | Male   | 53  | Married        | Degree                | British     | White     | Above 36,400       | The North | Yes   | 6            | 6            | Packets  |
+| 8 | Male   | 44  | Single         | Degree                | English     | White     | 10,400 to 15,600   | The North | No    | NA           | NA           |          |
+| 9 | Male   | 40  | Single         | GCSE/CSE              | English     | White     | 2,600 to 5,200     | The North | Yes   | 8            | 8            | Hand-Rolled |
+| 10| Female | 41  | Married        | No Qualification      | English     | White     | 5,200 to 10,400    | The North | Yes   | 15           | 12           | Packets  |
+| 11| Male   | 72  | Widowed        | No Qualification      | English     | White     | 10,400 to 15,600   | The North | No    | NA           | NA           |          |
+| 12| Male   | 49  | Married        | No Qualification      | British     | White     | Refused            | The North | No    | NA           | NA           |          |
+| 13| Male   | 29  | Married        | Degree                | English     | White     | Above 36,400       | The North | No    | NA           | NA           |          |
+
+## Data Preprocessing
+1. **Handle Missing Values**:
+    - `amt_weekends` and `amt_weekdays` missing values are filled with `0`.
+    - `type` missing values are filled with `None`.
+
+2. **Data Type Conversion**:
+    - `age`, `amt_weekends`, and `amt_weekdays` are converted to integers.
+    - `gross_income` is converted to a categorical type.
+
+3. **Encoding Categorical Variables**:
+    - Categorical variables (`gender`, `marital_status`, `highest_qualification`, `nationality`, `ethnicity`, `region`, `smoke`, `type`) are one-hot encoded.
+    - `gross_income` is label encoded.
+
+4. **Feature Scaling**:
+    - `age`, `amt_weekends`, and `amt_weekdays` are scaled using `StandardScaler`.
+
+## Exploratory Data Analysis
+- **Age Distribution**:
+    ```python
+    sns.histplot(df['age'], kde=True)
+    plt.title('Age Distribution')
+    plt.show()
+    ```
+- **Gross Income Distribution**:
+    ```python
+    sns.boxplot(x=df['gross_income'])
+    plt.title('Gross Income Distribution')
+    plt.show()
+    ```
+
+## Model Training and Evaluation
+The dataset is split into training (80%) and testing (20%) sets. Various machine learning models are trained and evaluated:
+
+1. **Random Forest**
+2. **Gradient Boosting**
+3. **Logistic Regression**
+4. **Support Vector Classifier**
+5. **K-Nearest Neighbors**
+6. **Naive Bayes**
+
+For each model:
+- Training and predictions are performed.
+- Accuracy, classification report, confusion matrix, and ROC AUC scores are calculated.
+
+### Model Performance
+Performance metrics are stored in a dictionary and printed for each model. Example:
+```python
+for model_name, performance in model_performance.items():
+    print(f"Model: {model_name}")
+    print(f"Accuracy: {performance['accuracy']}")
+    print(f"ROC AUC: {performance['roc_auc']}")
+    print("Classification Report:")
+    print(pd.DataFrame(performance['classification_report']).transpose())
+    print("Confusion Matrix:")
+    print(performance['confusion_matrix'])
+    print("\n")