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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]}")
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