Predicting House Prices Using Linear Regression in Python

Veröffentlicht am: 26 Februar 2025
auf dem Kanal: Upgrade2python

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Github Link: https://github.com/Jagadeesh-Surendra...
#shorts

#program :
import pandas as pd
from typing import List, Dict
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

def predict_house_prices(df: pd.DataFrame, new_data: List[Dict[str, int]]) -[Greater than symbol ] List[float]:
Extract features and target variable
X = df[["square_feet", "num_bedrooms", "num_bathrooms"]]
y = df["price"]

Split the dataset 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)

Train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

Predict prices for new data
new_df = pd.DataFrame(new_data)
predictions = model.predict(new_df)

return predictions.tolist()

Example data
data = [
{"square_feet": 1500, "num_bedrooms": 3, "num_bathrooms": 2, "price": 300000},
{"square_feet": 2000, "num_bedrooms": 4, "num_bathrooms": 3, "price": 400000},
{"square_feet": 2500, "num_bedrooms": 4, "num_bathrooms": 2, "price": 450000},
{"square_feet": 1200, "num_bedrooms": 2, "num_bathrooms": 1, "price": 200000}
]
new

#explanation

Based on the trained linear regression model, the predicted prices for the new houses are approximately ₹3,50,000 for a house with 1800 square feet, 3 bedrooms, and 2 bathrooms, and ₹4,20,000 for a house with 2300 square feet, 4 bedrooms, and 3 bathrooms.

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