my app

app.py

@@ -0,0 +1,100 @@
+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]}")