Importing necessary libraries

import pandas as pd import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.metrics import mean_absolute_error from tensorflow.keras import models, layers

Load the dataset (replace with the correct dataset path)

url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/health-insurance.csv" data = pd.read_csv(url, header=None) data.columns = ['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'expenses']

Convert categorical data (sex, smoker, region) to numbers

label_encoder = LabelEncoder()

data['sex'] = label_encoder.fit_transform(data['sex']) data['smoker'] = label_encoder.fit_transform(data['smoker']) data['region'] = label_encoder.fit_transform(data['region'])

Split the data into train and test datasets

train_dataset, test_dataset = train_test_split(data, test_size=0.2, random_state=42)

Separate the labels (expenses) from the features

train_labels = train_dataset.pop('expenses') test_labels = test_dataset.pop('expenses')

Normalize the features

train_dataset = (train_dataset - train_dataset.mean()) / train_dataset.std() test_dataset = (test_dataset - test_dataset.mean()) / test_dataset.std()

Build the Linear Regression model

model = models.Sequential([ layers.Dense(64, activation='relu', input_shape=(train_dataset.shape[1],)), layers.Dense(32, activation='relu'), layers.Dense(1) ])

Compile the model

model.compile(optimizer='adam', loss='mean_absolute_error')

Train the model

model.fit(train_dataset, train_labels, epochs=100, batch_size=32)

Evaluate the model

loss = model.evaluate(test_dataset, test_labels)

Print the loss (Mean Absolute Error)

print("Mean Absolute Error on test data: ", loss)

Make predictions on the test data

predictions = model.predict(test_dataset)

Compare predictions with actual labels

mae = mean_absolute_error(test_labels, predictions) print("Final Mean Absolute Error: ", mae)

Plot the results (optional)

import matplotlib.pyplot as plt plt.scatter(test_labels, predictions) plt.xlabel('Actual Expenses') plt.ylabel('Predicted Expenses') plt.title('Actual vs Predicted Health Expenses') plt.show()