documentation.md

Here's a comprehensive code documentation for the provided script:

# Diabetes Prediction and Pedigree Function Estimation

## Overview
This script performs two main tasks using machine learning:
1. **Estimates the Diabetes Pedigree Function** based on various health metrics using linear regression.
2. **Predicts the likelihood of diabetes** using a Support Vector Machine (SVM) classifier.

The script is built to handle a dataset containing health metrics related to diabetes and includes functionality for imputing missing values, training models, and making predictions for new patient data.

## Dependencies
The following Python libraries are required to run this script:
- `numpy`: For numerical operations.
- `pandas`: For data manipulation and analysis.
- `scikit-learn`: For machine learning model training, evaluation, and data preprocessing.
- `xgboost`: For additional machine learning models (though not used in this script).
- `google.colab`: For file handling and Google Drive integration (specific to Google Colab environment).

## Code Components

### 1. Importing Libraries
```python
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVC
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score, r2_score
from google.colab import drive

• Purpose: Import the necessary libraries for data manipulation, machine learning, and Google Drive integration.

2. Mounting Google Drive

drive.mount('/content/drive')

• Purpose: Mount Google Drive to access the dataset stored there (specific to Google Colab users).

3. Loading the Dataset

diabetes_data = pd.read_csv("/content/drive/path/to/diabetes.csv")

• Purpose: Load the diabetes dataset from Google Drive into a Pandas DataFrame.

4. Data Preparation

X = diabetes_data.drop(columns=["DiabetesPedigreeFunction", "Outcome"])
y_diabetes = diabetes_data["Outcome"]
y_pedigree = diabetes_data["DiabetesPedigreeFunction"]

imputer = SimpleImputer(strategy='mean')
X_imputed = imputer.fit_transform(X)

• Purpose:
  • Separate the features (X) from the targets (y_diabetes and y_pedigree).
  • Use mean imputation to handle missing values in the features.

5. Train-Test Split

X_train, X_test, y_diabetes_train, y_diabetes_test, y_pedigree_train, y_pedigree_test = train_test_split(
    X_imputed, y_diabetes, y_pedigree, test_size=0.2, random_state=42
)

• Purpose: Split the dataset into training and testing sets with 80% of the data used for training and 20% for testing.

6. Diabetes Pedigree Function Estimation (Linear Regression)

lin_model = LinearRegression()
lin_model.fit(X_train, y_pedigree_train)
y_lin_pred = lin_model.predict(X_test)

accuracy_lin_reg = r2_score(y_pedigree_test, y_lin_pred)
print(f"Linear Regression R² score: {accuracy_lin_reg:.2f}")

• Purpose:
  • Train a linear regression model to estimate the Diabetes Pedigree Function.
  • Evaluate the model's performance using the R² score.

7. Diabetes Prediction (SVM Classifier)

svm_clf = SVC()
svm_clf.fit(X_train, y_diabetes_train)
y_svm_pred = svm_clf.predict(X_test)

accuracy_svm = accuracy_score(y_diabetes_test, y_svm_pred)
print(f"Support Vector Machine Accuracy: {accuracy_svm:.2f}")

• Purpose:
  • Train an SVM classifier to predict diabetes.
  • Evaluate the model's performance using accuracy.

8. Making Predictions for New Patients

patient1 = {
     'Pregnancies': 3,
     'Glucose': 148,
     'BloodPressure': 72,
     'SkinThickness': 35,
     'Insulin': 0,
     'BMI': 33.6,
     'Age': 50
 }

patient2 = {
     'Pregnancies': 1,
     'Glucose': 85,
     'BloodPressure': 66,
     'SkinThickness': 29,
     'Insulin': 0,
     'BMI': 26.6,
     'Age': 31
 }

• Purpose: Define two sample patients for prediction:
  • patient1: Represents a patient likely to have diabetes.
  • patient2: Represents a patient likely not to have diabetes.

9. Prediction Function

def new_ptn(analysis):
    new_patient_df = pd.DataFrame([analysis])
    new_patient_imputed = imputer.transform(new_patient_df)

    predicted_pedigree = lin_model.predict(new_patient_imputed)
    print("Predicted DiabetesPedigreeFunction:", predicted_pedigree)

    predicted_outcome = svm_clf.predict(new_patient_imputed)
    print("Predicted Outcome status:", predicted_outcome)

new_ptn(patient1)
new_ptn(patient2)

• Purpose:
  • new_ptn: Function to predict both the Diabetes Pedigree Function and the diabetes outcome for a new patient.
  • Make predictions for patient1 and patient2.

Output

The script prints the R² score for the linear regression model and the accuracy of the SVM classifier. It also prints the predicted Diabetes Pedigree Function and diabetes status for two sample patients.

This documentation provides a clear and detailed explanation of each component in the script, making it easier for users to understand the purpose and functionality of the code.