diabetes.py

# Import necessary libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.preprocessing import StandardScaler

# Load the dataset (replace 'your_dataset.csv' with your actual dataset)
data = pd.read_csv('E:\2024\diabetes prediction system\diabetes data.csv')

# Explore the dataset and preprocess if necessary
# For simplicity, let's assume the dataset has columns like 'feature1', 'feature2', ..., 'target'
# 'target' is the column indicating whether the person has diabetes or not (1 for diabetes, 0 for no diabetes)

# Separate features and target variable
X = data.drop('target', axis=1)
y = data['target']

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize the features (optional but can be beneficial for some models)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Create a Random Forest Classifier (you can use other algorithms as well)
model = RandomForestClassifier(random_state=42)

# Train the model
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)

print(f"Accuracy: {accuracy}")
print("Classification Report:\n", report)