import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt

Load your dataset (replace 'your_dataset.csv' with your data file)

data = pd.read_csv('Real_Estate_Sales_2001-2020_GL.csv')

Get user input for the town and property details

your_town = input('Enter your town: ') residential_type = input('Enter the Residential Type of the property: ')

Filter the data for your town

your_town_data = data[data['Town'] == your_town] your_town_data = your_town_data[data['Residential Type'].notna()] print(your_town_data['Residential Type'].head()) your_resident_data = your_town_data[your_town_data['Residential Type'] == residential_type]

Assume 'X' contains your independent variables (features) and 'y' contains the target variable

For this simplified model, we'll only use a constant feature (intercept) to predict the sale amount

X = np.ones((len(your_town_data), 1)) y = your_town_data['Sale Amount']

Split the 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=42)

Initialize the Linear Regression model

model = LinearRegression()

Fit the model on the training data

model.fit(X_train, y_train)

Make predictions for the training and testing sets

y_train_pred = model.predict(X_train) y_test_pred = model.predict(X_test)

Calculate Mean Squared Error (MSE) for training and testing sets

mse_train = mean_squared_error(y_train, y_train_pred) mse_test = mean_squared_error(y_test, y_test_pred)

Print the MSE for both training and testing sets

print(f'Training MSE: {mse_train:.2f}') print(f'Testing MSE: {mse_test:.2f}')

Create a scatter plot for the training data

plt.figure(figsize=(10, 6)) plt.scatter(X_train, y_train, alpha=0.5, label='Training Data') plt.xlabel('List Year') plt.ylabel('Sale Amount') plt.title(f'Training Data: List Year vs. Sale Amount for {your_town}') plt.legend() plt.grid(True)

Show the plot for training data

plt.show()

Create a scatter plot for the testing data

plt.figure(figsize=(10, 6)) plt.scatter(X_test, y_test, alpha=0.5, label='Testing Data') plt.xlabel('List Year') plt.ylabel('Sale Amount') plt.title(f'Testing Data: List Year vs. Sale Amount for {your_town}') plt.legend() plt.grid(True)

Show the plot for testing data

plt.show()

UncleFame/UncleFame is a ✨ special ✨ repository because its README.md (this file) appears on your GitHub profile. You can click the Preview link to take a look at your changes. --->