📍 Introduction

Have you ever wondered how businesses can predict their profits based on the population of a city? Using Supervised Learning, specifically Linear Regression, we can build a simple yet powerful model that learns from past data and makes predictions for future inputs. 🚀

In this blog, I’ll walk you through a project where a Linear Regression model was trained on a dataset of city populations and their corresponding profits. The goal? To predict future profits for a business by simply inputting the population of a new city.

📂 Dataset

The dataset consists of 100 samples, each containing:

• Population (in 10,000s)

• Profit (in $10,000s)

Here’s a glimpse of the data:

This data follows a linear trend with some noise — perfect for a supervised learning task.

🔧 Model Training

Using Python, NumPy, and a bit of linear algebra, I trained a Linear Regression model with the following steps:

1. Data Loading: The CSV file is loaded using pandas.

2. Model Initialization: Parameters w (weight) and b (bias) are initialized.

3. Cost Function: The Mean Squared Error (MSE) is calculated to evaluate predictions.

4. Gradient Descent: The parameters are updated iteratively to minimize the cost.

5. Prediction: Once trained, the model predicts profits based on new population values.

💻 Try It Yourself

You can view the full source code, dataset, training logic, and prediction demo here:

👉 🔗 GitHub Repository: Machine Learning Profit-Population Model

Feel free to clone the repo, run the notebook, and experiment with your own population values.

📈 Sample Prediction

python
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population = 7.5  # in 10,000s
predicted_profit = w * population + b
print(f"Predicted profit: ${predicted_profit * 10000:.2f}")

When entering a population of 75,000, the model might predict a profit of around $125,000, depending on the final trained weights.

📚 Conclusion

This project is a classic example of Supervised Learning — training a model on labeled data to predict outcomes. It demonstrates how a simple linear model can help businesses make data-driven decisions.

If you're a beginner in machine learning or data science, this is a perfect project to understand the fundamentals of:

• Data preprocessing

• Gradient descent

• Cost function optimization

• Real-world applications of ML

💬 Got questions or want to collaborate? Drop a comment on GitHub or connect with me on LinkedIn!

Happy Learning! 🤖💡