Explore Linear Regression and Gradient Descent, the backbone of modern data predictions. Real-life examples and tips included!
Linear regression and Gradient Descent might sound like technical jargon, but together they make a great team for understanding data and making predictions. Whether it’s figuring out house prices or spotting trends, these tools are the unsung heroes of data science.
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#1: What’s Linear Regression, and Why Does Gradient Descent Matter?
Linear regression is all about drawing the best-fit straight line through a scatter of data points. It helps predict how one thing (like house size) relates to another (like price). But finding that “perfect” line isn’t as simple as eyeballing it—this is where Gradient Descent steps in. Gradient Descent is like your GPS for optimisation.
The equation
y = mx + b
Where:
y = Price
x = Size (Square Footage)
m = Change in price per square foot
b = Starting pricedetermines the steepness of the line that models the data. The slope ("m") in the equation "y = mx + b" adjusts the line's steepness to minimise the error, which is the squared difference between the predicted y and actual values of y.
error = (predicted y - actual y)^2
It’s trial and error, but way faster and more efficient (Bishop, 2006).
#2: Real-Life Examples of Linear Regression
House Prices
Real estate platforms use linear regression to estimate property values based on features like size, location, and nearby amenities.
Marketing Analytics
Businesses utilise its features like the impact of ad spending on sales and fine-tune their budgets for better results.
Healthcare
Doctors and researchers use it to track the impact of treatments or predict patient recovery times.
#3: Tips to Get Better Results
Normalise Your Data: Scaling your data helps Gradient Descent work faster by making all the variables the same size.
Set the Right Learning Rate: The learning rate of a gradient descent has to do with how big a step the model takes while adjusting its parameters. A learning rate that’s too high might overshoot the goal, while one that’s too low can drag things out forever (Kingma & Ba, 2015).
Watch for Overfitting: Test your model on fresh data to ensure it’s not just memorising your training set.
Linear regression may seem basic, but when paired with Gradient Descent, it’s an incredibly versatile tool for solving real-world problems. Whether you’re a data enthusiast or just curious, it’s worth diving into how these techniques shape the world of AI.
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