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# Import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

# Load the dataset
dataset = pd.read_csv('/mnt/data/HeightVsWeight1.csv')  # Adjust path if needed
X = dataset.iloc[:, 0:1].values  # Height (independent variable)
y = dataset.iloc[:, 1].values    # Weight (dependent variable)

# Train a Linear Regression model
linearReg = LinearRegression()
linearReg.fit(X, y)

# Train a Polynomial Regression model (Degree 4 for a smooth curve)
polyReg = PolynomialFeatures(degree=4)
X_poly = polyReg.fit_transform(X)
polyLinearReg = LinearRegression()
polyLinearReg.fit(X_poly, y)

# Visualize the Linear Regression Results
plt.scatter(X, y, color='red', label='Data Points')  # Original data points
plt.plot(X, linearReg.predict(X), color='blue', label='Linear Regression')  # Linear regression line

# Visualize the Polynomial Regression Results
plt.plot(X, polyLinearReg.predict(X_poly), color='green', label='Polynomial Regression (Degree 4)')  # Polynomial regression curve
plt.title('Height vs Weight')
plt.xlabel('Height')
plt.ylabel('Weight')
plt.legend()
plt.show()

# Predict weights for specific heights (replace with any heights you want to test)
heights = [[60], [65], [70]]  # Example heights
predictions_linear = linearReg.predict(heights)
predictions_poly = polyLinearReg.predict(polyReg.fit_transform(heights))

# Display predictions
for height, pred_lin, pred_poly in zip(heights, predictions_linear, predictions_poly):
    print(f"Height: {height[0]} -> Linear Prediction: {pred_lin:.2f}, Polynomial Prediction: {pred_poly:.2f}")