precisionrecall.py

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from sklearn.metrics import precision_score, recall_score
from sklearn.svm import SVC

# Load the dataset
data = pd.read_csv("Dataset/diabetes.csv")


# Define features (X) and target (y)
X = data.drop("Outcome", axis=1)
y = data["Outcome"]

# 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)

# Initialize and fit the model
model = SVC(kernel='linear', C=1.0, random_state=42)
model.fit(X_train, y_train)

# Extract feature names from the dataset
feature_names = X.columns.tolist()

# Collect patient data dynamically based on feature names
patient_data = {}
for feature in feature_names:
    value = input(f"Enter {feature} for the patient: ")
    patient_data[feature] = float(value)

# Create a DataFrame for the patient's data
patient_df = pd.DataFrame(patient_data, index=[0])

# Predict the outcome
prediction = model.predict(patient_df)
print("\nPatient Data:")
print(patient_df)
if prediction[0] == 1:
    print("Diabetes Detected")
else:
    print("Normal")

# Define a function to evaluate the model
def model_evaluation(data):
    X = data.drop("Outcome", axis=1)
    y = data["Outcome"]
    model = SVC(kernel='linear', C=1.0, random_state=42)
    cv_score = cross_val_score(model, X, y, cv=5)
    cv_prediction = cross_val_predict(model, X, y, cv=5)
    cv_accuracy = cv_score.mean()
    cv_precision = precision_score(y, cv_prediction)
    cv_recall = recall_score(y, cv_prediction)
    print("Cross-Validated Accuracy:", cv_accuracy)
    print("Cross-Validated Precision:", cv_precision)
    print("Cross-Validated Recall:", cv_recall)

# Evaluate the model using cross-validation
model_evaluation(data)