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RealEstateAlg.py
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RealEstateAlg.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
import pickle
# Data cleaning
file_path = r"C:\Users\mahlo\OneDrive\Documents\RealEstateAlgorithm\Real estate.csv"
df = pd.read_csv(file_path)
# Handle missing values using median
df = df.fillna(df.median())
# Encode categorical variables, drop_first to avoid multicollinearity
df = pd.get_dummies(df, columns=['X1 transaction date'], drop_first=True)
# Feature engineering
# Check and handle infinite or NaN values in 'price_per_sqft'
df['price_per_sqft'] = df['Y house price of unit area'] / df['X2 house age']
df['price_per_sqft'].replace([np.inf, -np.inf], np.nan, inplace=True)
df.dropna(subset=['price_per_sqft'], inplace=True)
# EDA
print(df.describe())
print(df.corr()['Y house price of unit area'])
# Visualization
fig, ax = plt.subplots()
ax.scatter(df['X3 distance to the nearest MRT station'], df['Y house price of unit area'], c=df['X2 house age'])
ax.set_title("Price vs Distance to MRT")
plt.savefig('price_vs_distance.png')
# Display the scatter plot in a separate window
plt.show()
# ML model
X = df.drop('Y house price of unit area', axis=1)
y = df['Y house price of unit area']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Check for infinite or NaN values in features before fitting the model
if np.any(np.isfinite(X_train)) and np.any(np.isfinite(y_train)):
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"MSE: {mse}")
print(f"R-squared: {r2}")
# Save model
with open('house_price_model.pkl', 'wb') as file:
pickle.dump(model, file)
else:
print("Input data contains infinite or NaN values. Please check and handle the issue.")