Linear Regression Machine Learning tutorial
Linear regression is a statistical technique that predicts the relationship between two or more variables using the sklearn.linear.LinearRegression() method.
Linear regression was first developed to analyze changes in international trade of goods and commodities.
The most famous example of this technique is referred to as Regression analysis. For instance, an economist could devise a linear relationship between per-capita consumption of cereal and a forecast for future oil prices issued by the U.S. Energy Information Administration. In other words, for every decade we forecast a 1% decrease in consumption due to price fluctuation issues related to global shifting appetites from one type of food to another since we have grown accustomed to sticking with cereal everywhere around the world - and whatever economic impacts that may have on it are foreseeable within our expectations for future market statistics for pricing fluctuations!!!
In this topic, the instructors will explore some big Linear Regression challenges like understanding coefficients, anticipating possible regressions outcomes from different measurements, intercepts
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