Time to apply what you’ve learned! In this hands-on mini project, we’ll use a real dataset (e.g., California Housing or Salary Data) to build a prediction model using Linear Regression.
Use a clean CSV like: California Housing Prices or search Kaggle for Salary/HR data.
🧠 Bonus: Present your project using charts + summary insights.
In this project, you’ll build a simple Linear Regression model using Python to predict house prices based on features like area, bedrooms, and bathrooms.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_scoreUse any clean housing dataset in CSV format (e.g., California Housing Prices).
data = pd.read_csv("data/house_data.csv")
print(data.head())sns.pairplot(data)
sns.heatmap(data.corr(), annot=True)X = data[['area', 'bedrooms', 'bathrooms']]
y = data['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)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"R² Score: {r2}")plt.scatter(y_test, y_pred)
plt.xlabel("Actual Prices")
plt.ylabel("Predicted Prices")
plt.title("Actual vs Predicted")
plt.show()✅ You’ve just completed your first real-life machine learning regression project!
In this mini project, you built a Linear Regression model using Python to predict house prices based on features like area, bedrooms, and bathrooms.
R² Score and MSE✅ You now have hands-on experience building and evaluating a real ML model!