Polynomial Regression.txt

import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from datetime import datetime, timedelta

# Generate synthetic weather data for demonstration
np.random.seed(42)
days = 30
dates = [datetime(2024, 1, 1) + timedelta(days=i) for i in range(days)]
temperatures = np.random.randint(0, 30, days)

# Assume that the target variable (y) is temperature
X = np.arange(days).reshape(-1, 1)
y = temperatures

# Add polynomial features
poly_features = PolynomialFeatures(degree=2)
X_poly = poly_features.fit_transform(X)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_poly, y, test_size=0.2, random_state=42)

# Create and train the polynomial regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict temperatures for the next 7 days
future_dates = [dates[-1] + timedelta(days=i) for i in range(1, 8)]
future_X = np.arange(days, days + 7).reshape(-1, 1)
future_X_poly = poly_features.transform(future_X)
future_predictions = model.predict(future_X_poly)

# Plot the results
plt.plot(dates, y, label="Actual Temperatures")
plt.plot(future_dates, future_predictions, label="Predicted Temperatures (Next 7 Days - Polynomial Regression)")
plt.xlabel("Date")
plt.ylabel("Temperature (°C)")
plt.title("Weather Prediction for the Next 7 Days (Polynomial Regression)")
plt.legend()
plt.show()