Feesout

#####Linear regression 
# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Prepare the data
data = {'Size': [1000, 1500, 1200, 2000, 1800, 1300, 1600, 1400, 1700, 1100],
        'Price': [150000, 200000, 180000, 250000, 220000, 190000, 210000, 200000, 225000, 170000]}

df = pd.DataFrame(data)

# Separate features (X) and target variable (y)
X = df[['Size']]  # Independent variable (size)
y = df['Price']   # Dependent variable (price)

# Create a linear regression model
model = LinearRegression()

# Train the model on the data
model.fit(X, y)

# Make a prediction for a new house size
new_size = 1850
predicted_price = model.predict([[new_size]])
print("Predicted price for a house of 1850 sq ft:", predicted_price[0])

# Visualize the results
plt.scatter(X, y, color='blue', label='Actual Data')
plt.plot(X, model.predict(X), color='red', label='Regression Line')
plt.xlabel("Size (sq ft)")
plt.ylabel("Price (USD)")
plt.title("House Price Prediction")
plt.legend()
plt.show()



###### Ploolynomial Regression 

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

# Read the data from a CSV file
df = pd.read_csv('house_prices.csv')

# Separate features (X) and target variable (y)
X = df[['Size']]  # Independent variable (size)
y = df['Price']   # Dependent variable (price)

# Create polynomial features (e.g., degree 2)
poly_features = PolynomialFeatures(degree=2)
X_poly = poly_features.fit_transform(X)

# Create a linear regression model
model = LinearRegression()

# Train the model on the polynomial features
model.fit(X_poly, y)

# Make a prediction for a new house size
new_size = 1850
new_size_poly = poly_features.transform([[new_size]])
predicted_price = model.predict(new_size_poly)
print("Predicted price for a house of 1850 sq ft:", predicted_price[0])

# Visualize the results
plt.scatter(X, y, color='blue', label='Actual Data')
plt.plot(X, model.predict(poly_features.fit_transform(X)), color='red', label='Polynomial Regression')
plt.xlabel("Size (sq ft)")
plt.ylabel("Price (USD)")
plt.title("House Price Prediction")
plt.legend()
plt.show()