AI/ML

AI-Based House Price Prediction using Linear Regression

Intermediate Difficulty
1 Hr Est. Time
0 items Components Needed

About This Project

House price prediction is one of the most common regression problems in Machine Learning. In this project, we use the Linear Regression algorithm to learn the relationship between house area and price.

The model is trained using historical data and can predict the estimated price for a new house.

This project teaches:

  • Data preprocessing
  • Training ML models
  • Regression
  • Prediction
  • Model evaluation

Technologies Used

  • Python
  • Pandas
  • NumPy
  • Scikit-learn
  • Matplotlib

Components Required

Step-by-Step Instructions

  1. Install libraries : pip install pandas numpy matplotlib scikit-learn
  2. Create Dataset
  3. Train Model
  4. Prediction
  5. Accuracy
  6. Test output

Source Code

Arduino / ESP32 Sketch 
1. Install libraries : 
pip install pandas numpy matplotlib scikit-learn

2. Create Dataset

import pandas as pd

data = {
    "Area":[1000,1200,1500,1800,2000,2500],
    "Price":[20,25,30,36,40,50]
}

df=pd.DataFrame(data)
print(df)

3. Train Model

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

X=df[['Area']]
y=df['Price']

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)

model=LinearRegression()
model.fit(X_train,y_train)


4.Prediction

prediction=model.predict([[1700]])
print("Predicted Price:",prediction[0],"Lakhs")


5. Accuracy

from sklearn.metrics import r2_score

pred=model.predict(X_test)
print("R2 Score:",r2_score(y_test,pred))

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