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Streamlit / app.py

ARPZ

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	import streamlit as st
	import pandas as pd
	import numpy as np
	from sklearn.model_selection import train_test_split
	from sklearn.preprocessing import StandardScaler, OneHotEncoder
	from sklearn.compose import ColumnTransformer
	from sklearn.pipeline import Pipeline
	from sklearn.ensemble import RandomForestClassifier
	from sklearn.neighbors import KNeighborsClassifier
	from sklearn.tree import DecisionTreeClassifier
	from sklearn.linear_model import LogisticRegression
	from sklearn.svm import SVC
	from sklearn.metrics import accuracy_score, classification_report

	# Load the dataset
	df = pd.read_csv('diabetes_prediction_dataset.csv')
	df = df.drop_duplicates()
	X = df.drop('diabetes', axis=1)
	y = df['diabetes']
	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

	# Define preprocessing steps for numeric and categorical features
	numeric_features = ['age', 'hypertension', 'heart_disease', 'bmi', 'HbA1c_level', 'blood_glucose_level']
	categorical_features = ['gender', 'smoking_history']
	numeric_transformer = Pipeline(
	steps=[
	('scaler', StandardScaler()) # Standardize numeric features
	]
	)

	categorical_transformer = Pipeline(
	steps=[
	('onehot', OneHotEncoder(handle_unknown='ignore')) # Encode categorical features
	]
	)

	# Combine transformers using ColumnTransformer
	preprocessor = ColumnTransformer(
	transformers=[
	('num', numeric_transformer, numeric_features),
	('cat', categorical_transformer, categorical_features)
	]
	)

	# Create a list of classifiers
	classifiers = [
	('K-NN', KNeighborsClassifier()),
	('Decision Tree', DecisionTreeClassifier()),
	('Random Forest', RandomForestClassifier()),
	('Logistic Regression', LogisticRegression()),
	('SVM', SVC())
	]

	# Streamlit app
	st.title("Diabetes Prediction Model")
	st.write("## Predict Diabetes")

	# User input for prediction
	st.write("### Input Features")
	gender = st.radio("Gender", ('Male', 'Female'))
	age = st.number_input("Age", value=30, min_value=1, max_value=120)
	hypertension = st.checkbox("Hypertension")
	heart_disease = st.checkbox("Heart Disease")
	smoking_history = st.radio("Smoking History", ('Never', 'Former', 'Current'))
	bmi = st.number_input("BMI", value=25.0, min_value=10.0, max_value=60.0, step=0.1)
	HbA1c_level = st.number_input("HbA1c Level", value=5.0, min_value=3.0, max_value=20.0, step=0.1)
	blood_glucose_level = st.number_input("Blood Glucose Level", value=100, min_value=0, max_value=500)

	# Create a dictionary of input features
	input_features = {
	'gender': [gender],
	'age': [age],
	'hypertension': [hypertension],
	'heart_disease': [heart_disease],
	'smoking_history': [smoking_history],
	'bmi': [bmi],
	'HbA1c_level': [HbA1c_level],
	'blood_glucose_level': [blood_glucose_level]
	}

	# Convert input to DataFrame and reshape for prediction
	input_df = pd.DataFrame(input_features)

	# Prediction
	if st.button("Predict"):
	st.write("### Prediction")
	for name, classifier in classifiers:
	model = Pipeline(
	steps=[
	('preprocessor', preprocessor),
	('classifier', classifier) # Add the classifier
	]
	)

	# Fit the model on the training data
	model.fit(X_train, y_train)

	# Make prediction
	prediction = model.predict(input_df)
	st.write(f"Prediction using {name}: {prediction[0]}")