n this video, we’ll explore the basics of linear regression, one of the most common techniques used in data science and statistics. Linear regression is a method used to model the relationship between a dependent variable (what you want to predict) and an independent variable (the input you use to make predictions). The goal is to find the best-fitting straight line through the data points. <br /><br />We’ll discuss how the line is determined using the formula: <br /><br />y=mx+b <br />Where: <br /><br /><br />y is the dependent variable (the value you’re predicting), <br /><br />x is the independent variable (the input data), <br /><br />m is the slope of the line (how much <br /><br />y changes with <br /><br />b is the y-intercept (the value of <br /><br />By fitting this line to your data, you can make predictions and understand trends. We’ll break down the concept with examples and explain how it’s applied in real-world scenarios like predicting prices, trends, and more. <br /><br />Watch till the end to see how simple linear regression can be used to make predictions with just a few data points! <br /><br />Linear Regression Explained Simply <br />"Understanding Linear Regression: The Basics (For Beginners)" <br />"What is Linear Regression? A Simple Explanation" <br />"Learn Linear Regression in 5 Minutes <br />"Mastering Linear Regression: A Beginner’s Guide" <br />"Linear Regression Demystified: How It Works in Simple Terms" <br />"The Power of Linear Regression: Simple Explanation & Examples" <br />"Predicting with Linear Regression: <br />"What is Linear Regression and Why It’s Important for Data Science" <br />"Linear Regression 101: The Essentials <br />#LinearRegression <br />#DataScience <br />#MachineLearning <br />#Statistics <br />#DataAnalysis <br />#ArtificialIntelligence <br />#PredictiveModeling <br />#DataScienceForBeginners <br />#LearnDataScience <br />##StatisticalModeling <br />#DataVisualization <br />#AI <br />#Machine
