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app.py

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+import streamlit as st
+import pandas as pd
+from predict import InsuranceClaimPredictor
+
+# Initialize the predictor
+predictor = InsuranceClaimPredictor('model/insurance_claim_prediction_model.joblib')
+
+# Title of the Streamlit app
+st.title('Insurance Claim Prediction')
+
+# Sidebar for user input
+st.sidebar.header('User Input Parameters')
+
+def user_input_features():
+    age = st.sidebar.number_input('Age (in years)', min_value=0, max_value=100, value=40)
+    
+    sex = st.sidebar.selectbox('Sex', ['male', 'female'])
+    
+    bmi = st.sidebar.number_input('BMI (Body Mass Index)', min_value=0.0, max_value=50.0, value=25.3)
+    
+    children = st.sidebar.number_input('Number of Children', min_value=0, max_value=10, value=2)
+    
+    smoker = st.sidebar.selectbox('Smoker', ['yes', 'no'])
+    
+    region = st.sidebar.selectbox('Region', ['northeast', 'northwest', 'southeast', 'southwest'])
+
+    charges = st.sidebar.number_input('Medical Charges ($)', min_value=0.0, max_value=100000.0, value=2900.0)
+    
+
+    data = {'age': age,
+            'sex': sex,
+            'bmi': bmi,
+            'children': children,
+            'smoker': smoker,
+            'region': region,
+            'charges': charges}
+    features = pd.DataFrame(data, index=[0])
+    return features
+
+df = user_input_features()
+
+# Button for prediction
+if st.sidebar.button('Predict'):
+    # Make prediction
+    prediction = predictor.predict(df)
+    
+    # Display the prediction
+    if prediction[0] == 1:
+        st.sidebar.success('This person is likely to make an insurance claim.')
+    else:
+        st.sidebar.info('This person is less likely to make an insurance claim.')
+
+# Display user input
+st.subheader('User Input Parameters')
+
+# Add descriptions for each input
+st.write("""
+- **Age**: The age of the individual.
+- **Sex**: The gender of the individual.
+- **BMI**: Body Mass Index, a measure of body fat based on height and weight.
+- **Number of Children**: Number of children or dependents.
+- **Smoker**: Whether the individual is a smoker.
+- **Region**: The geographical region where the individual resides.
+- **Medical Charges**: Annual medical charges billed.
+""")
+
+# Demo data
+st.subheader('Demo Data')
+
+# Sample data for demo
+demo_data = pd.DataFrame({
+    'age': [23, 45],
+    'sex': ['female', 'male'],
+    'bmi': [22.0, 30.0],
+    'children': [0, 2],
+    'smoker': ['no', 'yes'],
+    'region': ['southeast', 'northwest'],
+    'charges': [2000.0, 12000.0],
+    'claim': ['No', 'Yes']
+})
+
+st.write(demo_data)
+
+# Notifications for analysis
+st.subheader('Analysis Dashboard')
+
+claimed_data = demo_data[demo_data['claim'] == 'Yes']
+not_claimed_data = demo_data[demo_data['claim'] == 'No']
+
+
+st.write(f"### Charges Analysis")
+st.write(f"- **Average charges for claims made**: ${claimed_data['charges'].mean():.2f}")
+st.write(f"- **Average charges for claims not made**: ${not_claimed_data['charges'].mean():.2f}")

dataset/insurance2.csv

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model/insurance_claim_prediction_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17ffdd746a24edd2545edbb8fbc45cfb2cb13b34fd54aa6b3b861d1b159af8ed
+size 10344

predict.py

@@ -0,0 +1,55 @@
+# predict_model.py
+
+import pandas as pd
+import joblib
+# predict_model.py
+
+import pandas as pd
+import joblib
+
+class InsuranceClaimPredictor:
+    def __init__(self, model_path):
+        self.model_path = model_path
+        self.model = self.load_model()
+
+    def load_model(self):
+        # Load the model
+        loaded_model = joblib.load(self.model_path)
+        return loaded_model
+
+    def predict(self, data):
+        # Make predictions
+        predictions = self.model.predict(data)
+        return predictions
+
+
+if __name__ == "__main__":
+    predictor = InsuranceClaimPredictor('model/insurance_claim_prediction_model.joblib')
+
+    # # Example of a person who is less likely to make an insurance claim
+    # unseen_data_non_claim = pd.DataFrame({
+    #     'age': [25],          # Younger age
+    #     'sex': ['female'],    # Female (just an example, gender may not significantly affect the outcome)
+    #     'bmi': [22.0],        # Lower BMI   
+    #     'children': [0],      # No children
+    #     'smoker': ['no'],     # Non-smoker
+    #     'region': ['southwest'], # Region (doesn't typically affect claims, chosen arbitrarily)
+    #     'charges': [1000]     # Lower medical expenses
+    # })
+    
+    # predictions = predictor.predict(unseen_data_non_claim)
+    # print("Predictions for the unseen data:", predictions)
+
+    #Example of how to use the function
+    unseen_data = pd.DataFrame({
+        'age': [40],
+        'sex': ['male'],
+        'bmi': [25.3],
+        'children': [2],
+        'smoker': ['no'],
+        'region': ['southeast'],
+        'charges': [2900]
+    })
+
+    predictions = predictor.predict(unseen_data)
+    print("Predictions for the unseen data:", predictions)

readme.md

@@ -0,0 +1,65 @@
+## Project Structure
+
+```bash
+|-- README.md
+|-- app.py
+|-- train_model.py
+|-- predict_model.py
+|-- model/
+|   |-- insurance_claim_prediction_model.joblib
+|-- dataset/
+|   |-- insurance2.csv
+|-- requirements.txt
+```
+# Insurance Claim Prediction App
+
+This project implements a Streamlit web application for predicting whether an individual is likely to make an insurance 
+claim based on various input parameters. The prediction model is trained using a decision tree classifier.
+
+## Project Structure
+
+- **`app.py`**: Streamlit web application code for user interface.
+- **`train_model.py`**: Python script for training the machine learning model and saving it.
+- **`predict_model.py`**: Python script for loading the trained model and making predictions.
+- **`model/insurance_claim_prediction_model.joblib`**: Saved trained model using joblib.
+- **`dataset/insurance2.csv`**: Dataset used for training and testing the model.
+- **`requirements.txt`**: List of Python dependencies required to run the application.
+
+## Getting Started
+
+### Prerequisites
+
+Ensure you have Python installed. You can install it from [python.org](https://www.python.org).
+
+### Installation
+```bash
+pip install -r requirements.txt
+```
+### Running the App
+To run the Streamlit app, execute the following command:
+```bash
+streamlit run app.py
+```
+This will start a local server and open your default web browser to the app.
+
+### Usage
+User Input Parameters: Adjust the sliders and dropdowns in the sidebar to input different values for age, sex, BMI, 
+children, smoker status, region, and medical charges.
+Predict Button: Click on the "Predict" button in the sidebar to see whether the individual is likely to make an insurance 
+claim.
+Analysis Dashboard: View average medical charges for claims made and not made based on demo data.
+
+### Examples
+#### Example 1: Predicting Insurance Claim Likelihood
+
+Suppose a 40-year-old male with a BMI of 25.3, 2 children, non-smoker from the southeast region, and medical charges of $2900.0 wants to predict the likelihood of making an insurance claim. After inputting these details and clicking "Predict," the app predicts whether this individual is likely to make an insurance claim.
+
+### Dependencies
+
+```bash
+Python 3.x
+pandas
+joblib
+scikit-learn
+streamlit
+```

requirements.txt

@@ -0,0 +1,3 @@
+pandas
+scikit-learn
+streamlit 

train.py

@@ -0,0 +1,65 @@
+# train_model.py
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn import tree
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+import joblib
+
+class InsuranceClaimModelTrainer:
+    def __init__(self, data_path):
+        self.data_path = data_path
+        self.model = None
+
+    def load_data(self):
+        # Load the dataset
+        df = pd.read_csv(self.data_path)
+        # Separate features and target
+        X = df.drop(columns=['insuranceclaim'])
+        y = df['insuranceclaim']
+        return X, y
+
+    def preprocess_data(self, X):
+        # Define preprocessing for numerical features (scaling)
+        numerical_features = ['age', 'bmi', 'children', 'charges']
+        numerical_transformer = StandardScaler()
+
+        # Define preprocessing for categorical features (one-hot encoding)
+        categorical_features = ['sex', 'smoker', 'region']
+        categorical_transformer = OneHotEncoder(handle_unknown='ignore', drop='first')
+
+        # Combine preprocessing steps
+        preprocessor = ColumnTransformer(
+            transformers=[
+                ('num', numerical_transformer, numerical_features),
+                ('cat', categorical_transformer, categorical_features)
+            ])
+
+        return preprocessor
+
+    def train_model(self):
+        # Load and preprocess the data
+        X, y = self.load_data()
+        preprocessor = self.preprocess_data(X)
+
+        # Create a preprocessing and modeling pipeline
+        self.model = Pipeline(steps=[
+            ('preprocessor', preprocessor),
+            ('classifier', tree.DecisionTreeClassifier(random_state=42))
+        ])
+
+        # Split data into training and testing sets
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)
+
+        # Train the model
+        self.model.fit(X_train, y_train)
+
+        # Save the preprocessor and the trained model using joblib
+        joblib.dump(self.model, 'model/insurance_claim_prediction_model.joblib')
+        print("Model trained and saved successfully!")
+
+if __name__ == "__main__":
+    trainer = InsuranceClaimModelTrainer('dataset/insurance2.csv')
+    trainer.train_model()