Ever wondered how machines predict prices, trends, or sales with stunning accuracy? Thesecret often lies in regression in machine learning—a powerful technique used to forecast continuous outcomes. From estimating house prices to predicting customer spending, regression models are everywhere. But for many, the term still feels complex, even intimidating.That’s where most learners struggle. They dive into machine learning but skip overregression. As a result, their predictions fall short. Their models underperform. Luckily, youdon’t have to stay stuck. In this guide, we break down regression in the simplest waypossible. You’ll learn what it is, why it matters, how it works, and even how to code it. Readyto unlock smarter predictions? Let’s dive in.Sourcel.toLowerCase().replace(/\s+/g,"-")" id="9a307553-0153-4f82-97b0-6fe79f88e82f" data-toc-id="9a307553-0153-4f82-97b0-6fe79f88e82f">What is Regression?Regression is a supervised machine-learning technique. It helps predict a continuous valuebased on input features. In simple terms, it finds the relationship between a target variableand one or more input variables. The target is what we want to predict. The inputs are thefactors that influence it. This relationship is modelled using training data.For example, think about predicting house prices. Factors like square footage, number ofbedrooms, and location play a big role. These are the input variables. The house price is thetarget. Using regression, we can make accurate predictions based on this data.l.toLowerCase().replace(/\s+/g,"-")" id="e4fad8bb-9988-4acc-8db6-3983ca11ed08" data-toc-id="e4fad8bb-9988-4acc-8db6-3983ca11ed08">Why is Regression Important in Machine Learning?Regression plays a key role in many machine-learning tasks. In short, regression in machinelearning is not just helpful—it’s essential. It’s the foundation for reliable predictions, deepinsights, and smarter models. Here's why it matters:● Reveals relationships between variablesRegression helps identify how one variable affects another. For example, howadvertising budget influences sales. This insight is crucial for data-driven decisions.● Detects trends and patternsIt makes it easier to spot trends in data over time. Whether it’s rising costs orchanging customer behaviour, regression brings patterns to light.● Supports forecasting and future planningRegression models are often used to predict outcomes. From estimating revenue toforecasting demand, they help businesses plan with confidence.● Forms the base for complex algorithmsMany advanced machine learning models are built . It’s a starting point for techniques like support vector machines and neural networks.l.toLowerCase().replace(/\s+/g,"-")" id="2ca11f1f-f0f8-403d-a7ca-4a3e6387f973" data-toc-id="2ca11f1f-f0f8-403d-a7ca-4a3e6387f973">How Regression Works?Regression works by finding a relationship between input and output variables. It starts withhistorical data. This data includes known inputs and their corresponding outputs. The modelstudies this data during training. Then, it tries to draw a line or curve that best fits the data points. This line represents thepredicted values. The goal is to make these predictions as close as possible to the actualresults. To do this, the model adjusts its internal parameters. These parameters define theslope and position of the prediction line. Every time the model makes a guess, it checks howfar off it is. This gap between the predicted and actual values is called the error.To measure the error, regression uses a loss function. One common example is MeanSquared Error (MSE). It calculates the average of the squared differences betweenpredicted and actual values.The model keeps adjusting until it finds the combination of parameters that minimises thiserror. As a result, it becomes more accurate over time. In the end, the trained model cantake new input data and predict the output. This is how regression helps in making reliable,data-driven predictions.l.toLowerCase().replace(/\s+/g,"-")" id="69bbcc0d-4be9-4dbf-8ded-231a03d4c6f0" data-toc-id="69bbcc0d-4be9-4dbf-8ded-231a03d4c6f0">Types of RegressionIn machine learning, there isn’t just one kind of regression. Different problems need differentapproaches. That’s why there are several types of regression techniques. Each is designedto handle specific data patterns, structures, and complexities.● Linear Regression: The most basic and widely used type. It models a straight-linerelationship between the input (independent) and output (dependent) variables. Itworks best when the data has a linear trend.● Polynomial Regression: Extends linear regression by allowing the relationship to becurved. It adds polynomial terms to better-fit data with non-linear patterns.● Logistic Regression: Despite the name, this is used for classification, not predictionof continuous values. It estimates the probability of a binary outcome, likesuccess/failure or spam/not spam.● Ridge Regression: A regularised version of linear regression. It adds a penalty toreduce model complexity and prevent overfitting, especially when features are highlycorrelated.● Lasso Regression: Similar to the ridge, but can shrink some feature coefficients tozero. This makes it useful for simplifying machine learning models and performing automatic feature selection.Each of these regression methods tackles different challenges in data. In the next sections,we’ll explore how they work and when to use them effectively.l.toLowerCase().replace(/\s+/g,"-")" id="3135c8c1-8393-462e-981e-884e66117924" data-toc-id="3135c8c1-8393-462e-981e-884e66117924">1. Linear Regression in Machine LearningLinear regression is one of the most straightforward and widely used techniques in machinelearning. It assumes that there is a linear relationship between the input variable (orfeatures) and the output variable (or target). In simple terms, it tries to draw a straight linethrough the data points that best represents the relationship between the input and output.The formula for simple linear regression is y = mx + bWhere:● y is the predicted output value. This is what we want to predict.● m is the slope of the line. It shows how much y changes with a change in x.● x is the input feature (independent variable) that influences the outcome.● b is the intercept, the value of y when x is 0. It’s where the line crosses the y-axis.The goal is to find the values of m and b that minimise the error between the predictedvalues and the actual values. This is often done using a method called least squares, whichminimises the sum of squared differences between the predicted and actual values.Linear regression is widely used for problems where the relationship between the input andoutput is approximately linear, such as predicting house prices, sales, or stock prices.Sourcel.toLowerCase().replace(/\s+/g,"-")" id="5bdf5338-dc98-4b35-884a-0938f7f6e2e1" data-toc-id="5bdf5338-dc98-4b35-884a-0938f7f6e2e1">Simple Linear Regression in Machine LearningSimple linear regression is used when we want to predict a dependent variable using asingle independent variable. It assumes that the relationship between the two variables islinear, meaning that as the input changes, the output changes at a constant rate. Theformula for simple linear regression is typically represented as y = mx + b, where y is thepredicted output, m is the slope of the line, x is the input feature, and b is the intercept.For example, predicting salary based on years of experience is a simple linearregression problem. As experience increases, salary generally increases, and a straight-linerelationship can be observed. This makes simple linear regression a good fit for problemswith one input variable and a clear linear trend.l.toLowerCase().replace(/\s+/g,"-")" id="da94564a-cd5c-4cb0-89e9-0c371c02dff9" data-toc-id="da94564a-cd5c-4cb0-89e9-0c371c02dff9">Multiple Linear Regression in Machine LearningMultiple linear regression takes things further by using two or more independent variables topredict the dependent variable. This method captures more complex relationships betweenthe input variables and the output. It works well when the relationship is influenced bymultiple factors.For instance, predicting house prices is often a multiple linear regression problem. Factorslike location, size, number of bedrooms, and age of the property all influence the price. Themodel combines these variables to make a more accurate prediction.l.toLowerCase().replace(/\s+/g,"-")" id="18695975-3909-4d1c-8478-1eb6f6bfe65b" data-toc-id="18695975-3909-4d1c-8478-1eb6f6bfe65b">Linear Regression in Python CodeHere’s a simple example using scikit-learn:python from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split import pandas as pd # Sample dataset data = pd.read_csv("housing.csv") X = data[['sqft', 'bedrooms', 'bathrooms']] y = data['price'] # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # Train model model = LinearRegression() model.fit(X_train, y_train) # Predict predictions = model.predict(X_test) # Output sample predictions print(predictions[:5]) l.toLowerCase().replace(/\s+/g,"-")" id="cc23fe3b-1a34-445d-b6ba-90238dcae9b3" data-toc-id="cc23fe3b-1a34-445d-b6ba-90238dcae9b3">2. Polynomial Regression in Machine LearningPolynomial regression is used when the relationship between variables is nonlinear. Unlikesimple or multiple linear regression, which fits a straight line to the data, polynomialregression adds polynomial terms (like x², x³, etc.) to the equation. This allows the model tocapture more complex, curved relationships between the input and output variables.Polynomial regression helps when data shows a clear curve or trend, making it a betterchoice than linear models for capturing complex relationships.l.toLowerCase().replace(/\s+/g,"-")" id="f6d6470f-9099-49fa-9676-6f16c40145ba" data-toc-id="f6d6470f-9099-49fa-9676-6f16c40145ba">Example:Modelling population growth over time is a typical example. Population growth doesn't followa straight line; it usually increases at an accelerating rate, forming a curve. By addinghigher-degree polynomial terms, the model can fit the data more accurately, reflecting thechanging growth pattern.l.toLowerCase().replace(/\s+/g,"-")" id="d3aa6d1e-6e92-4d4e-8f60-98fd2d74bee4" data-toc-id="d3aa6d1e-6e92-4d4e-8f60-98fd2d74bee4">3. Logistic Regression in Machine LearningDespite its name, logistic regression is used for classification problems, not regression. It predicts the probability of a categorical dependent variable, often in binary form (e.g., 0 or 1, true or false). Unlike linear regression, which predicts continuous values, logistic regression outputs values between 0 and 1, representing the probability of a certain class. Logistic regression is commonly used for binary classification tasks, but can be extended to multi-class classification using techniques like softmax regression.l.toLowerCase().replace(/\s+/g,"-")" id="b23f74da-aa63-496c-8b52-9f76da7b8531" data-toc-id="b23f74da-aa63-496c-8b52-9f76da7b8531">Example:An example of logistic regression would be predicting whether an email is spam or not. Themodel evaluates various features of the email (like keywords or sender) and predicts theprobability that the email is spam (1) or not spam (0).Sourcel.toLowerCase().replace(/\s+/g,"-")" id="18588af2-9007-4eef-bba6-420ace3753fc" data-toc-id="18588af2-9007-4eef-bba6-420ace3753fc">4. Ridge and Lasso RegressionRidge and Lasso regression are regularised versions of linear regression designed toprevent overfitting. Overfitting occurs when a model learns too much from the training data,capturing noise and fluctuations that don't generalise well to new data. Regularisation helpsby adding a penalty term to the loss function, which discourages overly complex models.Both methods are valuable in ensuring that models perform well on unseen data.l.toLowerCase().replace(/\s+/g,"-")" id="1e74f1e0-701c-4cb9-bc47-275cb46d3783" data-toc-id="1e74f1e0-701c-4cb9-bc47-275cb46d3783">Ridge Regression:Ridge regression adds L2 regularisation, which involves the squared magnitude of themodel's coefficients. The penalty term is the sum of the squares of the coefficients, multipliedby a regularisation parameter (λ). By doing this, ridge regression reduces the size of thecoefficients, which helps in making the model more general and less sensitive to smallfluctuations in the training data.● Adds L2 regularisation: Shrinks the coefficients evenly.● Prevents large coefficients: Helps manage multicollinearity (when features are highlycorrelated).● Improves model generalisation: Reduces overfitting.l.toLowerCase().replace(/\s+/g,"-")" id="be6a45c8-fdd3-416e-9107-9ab4471e0f6d" data-toc-id="be6a45c8-fdd3-416e-9107-9ab4471e0f6d">Lasso Regression:Lasso regression adds L1 regularisation, which uses the absolute values of the coefficients.The penalty term is the sum of the absolute values of the coefficients, multiplied by theregularisation parameter (λ). Lasso is unique because it can shrink some coefficients tozero, effectively performing feature selection. This means Lasso not only helps preventoverfitting but also reduces the number of features used in the model, making it moreinterpretable.● Adds L1 regularisation: Can shrink coefficients to zero.● Performs feature selection: Eliminates irrelevant features.● Helps in sparse models: Results in simpler models with fewer features.l.toLowerCase().replace(/\s+/g,"-")" id="750a5d8a-390a-4878-abe1-1880e8e91e0c" data-toc-id="750a5d8a-390a-4878-abe1-1880e8e91e0c">Applications of RegressionRegression plays a significant role in several industries by helping predict continuousoutcomes and uncovering patterns from data. Here are some key areas where regressiontechniques are widely applied:l.toLowerCase().replace(/\s+/g,"-")" id="5b1950ac-deda-4f6d-a8e4-2c26371472c6" data-toc-id="5b1950ac-deda-4f6d-a8e4-2c26371472c6">FinanceIn finance, regression is crucial for making predictions and assessing risks:● Predicting Stock Prices: Regression models, especially linear and polynomialregression, are often used to predict future stock prices based on historical data. Byanalysing trends, volume, and other market indicators, models can forecast pricechanges.● Credit Scoring: Financial institutions use regression to assess the likelihood of loanrepayment. Based on factors such as income, employment history, and debt,regression helps predict a borrower’s creditworthiness.● Risk Management: Regression models are also employed to identify and quantifyrisk in investment portfolios. By understanding how certain variables affect marketconditions, investors can manage risks more effectively.l.toLowerCase().replace(/\s+/g,"-")" id="de14f9ca-c369-4e2b-bc7d-d45b65b0c050" data-toc-id="de14f9ca-c369-4e2b-bc7d-d45b65b0c050">Marketing and SalesRegression is essential in marketing and sales for decision-making and strategydevelopment:● Estimating Campaign Effectiveness: Marketers use regression to evaluate theeffectiveness of advertising campaigns. By analysing factors like spending, targetdemographics, and conversions, regression models can predict the return oninvestment (ROI).● Customer Lifetime Value (CLV): Regression models help businesses estimate thetotal value a customer will bring over their lifetime. By analysing purchase history,customer behaviour, and demographics, businesses can optimise marketingstrategies.l.toLowerCase().replace(/\s+/g,"-")" id="2f258c0c-f5ed-439c-b248-0a44db799155" data-toc-id="2f258c0c-f5ed-439c-b248-0a44db799155">HealthcareIn healthcare, regression models are vital for improving patient care and operationalefficiency:● Predicting Patient Outcomes: Regression is used to predict patient outcomesbased on various health metrics, like blood pressure, age, and medical history. Ithelps doctors make informed decisions about treatment plans.● Disease Progression: Regression models are also applied to understand howdiseases progress over time, helping physicians track the development of chronicconditions and adjust treatments accordingly.Sourcel.toLowerCase().replace(/\s+/g,"-")" id="ff49f8ee-cd5d-4a7e-90d1-eda28c8f8fea" data-toc-id="ff49f8ee-cd5d-4a7e-90d1-eda28c8f8fea">Advantages of RegressionRegression has several key advantages and disadvantages that make it suitable for certainapplications while posing challenges in others.● Easy to Understand and Interpret:Regression models, especially linear regression, are simple and easy to understand.The relationship between the dependent and independent variables is straightforwardand can be visualised, making it a good choice for beginners and those needing clearinsights.● Computationally Efficient:Regression techniques are computationally efficient and don't require significantprocessing power. This makes them ideal for large datasets or scenarios wherecomputing resources are limited.● Forms the Basis of Many Other Complex Algorithms:Regression is a foundational concept for more advanced algorithms, like logisticregression for classification or regularised models such as Ridge and Lasso.Understanding basic regression paves the way for learning and applying these morecomplex methods.l.toLowerCase().replace(/\s+/g,"-")" id="e3371c18-b5d7-4db3-887c-62a6006fab25" data-toc-id="e3371c18-b5d7-4db3-887c-62a6006fab25">Summary of Machine Learning RegressionRegression is a powerful technique in machine learning, primarily used for predictingcontinuous outcomes. It plays a crucial role in understanding the relationships betweenvariables and making data-driven decisions. Whether you’re just starting or looking to refineyour skills, mastering regression is key to solving real-world problems across variousindustries.● Regression is a key technique in supervised learning for predicting continuous variables.● It ranges from simple linear regression to complex forms like regularised andnonlinear regression.● Each type of regression is suited for different data complexities and real-worldmachine-learning problems. ● Understanding when to use each type is crucial for effective problem-solving.● Mastering regression is essential for both beginners and experienced dataprofessionals.● It is foundational for building many machine learning models.● Regression is critical for making accurate, data-driven decisions.● Gaining expertise in regression helps tackle diverse challenges across variousindustries.l.toLowerCase().replace(/\s+/g,"-")" id="e76afe68-9068-479e-afb9-1b7689f37c4b" data-toc-id="e76afe68-9068-479e-afb9-1b7689f37c4b">Machine Learning Regression FAQ’sQ1: What is regression in machine learning?A: Regression in machine learning is a technique used to predict continuous values basedon input data. It's commonly used in forecasting and trend analysis.Q2: What are the types of regression in machine learning?A: Common types include linear regression, polynomial regression, logistic regression,ridge regression, and lasso regression. Each fits different data patterns.Q3: When should I use linear regression?A: Use linear regression when the relationship between input and output is linear. It’s idealfor simple predictions like sales or prices.Q4: Is logistic regression used for regression tasks?A: No, despite the name, logistic regression is used for classification tasks like spamdetection or fraud prediction.