{"id":1898,"date":"2019-04-07T12:00:00","date_gmt":"2019-04-07T10:00:00","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/machine-learning-101-simple-regression-problem\/"},"modified":"2026-07-05T03:22:36","modified_gmt":"2026-07-05T01:22:36","slug":"machine-learning-101-simple-regression-problem","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/machine-learning-101-simple-regression-problem\/","title":{"rendered":"Machine Learning 101 \u2013 Simple Regression Problem"},"content":{"rendered":"

This is Lecture 4 of the Machine Learning 101. It follows from Lecture 3<\/a>.\u00a0 In this lecture, we would solve some regression problems. So brace up!<\/p>\n

First, What is Regression?<\/strong><\/p>\n

Simply put, regression is a method used to find relationship between two or more variables.<\/p>\n

 <\/p>\n

Problem 1: Marketing\u00a0Ads and Profit<\/b><\/p>\n

You have been hired by a departmental store as a Machine Learning expert. Your task is to predict how much profit they would make is they spent $20.00 on advertising. What if they spend additional $120.00 on advertising, how much profit would they make. To help you, they provide you with a history of how spendings on ads and corresponding profit.<\/p>\n

The record is given below:<\/p>\n\n\n\n\n\n\n\n\n\n
Year<\/strong><\/td>\nSpending ($)<\/strong><\/td>\nProfit ($)<\/strong><\/td>\n<\/tr>\n
2000<\/td>\n40<\/td>\n160<\/td>\n<\/tr>\n
2005<\/td>\n50<\/td>\n250<\/td>\n<\/tr>\n
2010<\/td>\n60<\/td>\n360<\/td>\n<\/tr>\n
2016<\/td>\n70<\/td>\n490<\/td>\n<\/tr>\n
2019<\/td>\n20<\/td>\n?<\/strong><\/span><\/td>\n<\/tr>\n
2020<\/td>\n120<\/td>\n?<\/span><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Solution 1: By Inspection<\/strong><\/p>\n

We first examine the given data.<\/p>\n

For example, in the year 2000, $40.00 yielded a profit of $160.00. This seems like 40 x 40 = 1600 (but not exactly).<\/p>\n

What of (40 x 40) \/ 10? That would give us 1600\/10 = 160.<\/p>\n

If we apply it to the year 2005, we would have (50 x 50) \/ 10 = 250.<\/p>\n

It seems we have it!<\/p>\n

Now we need to find the formula that relates spending\u00a0 to profit. Say we need to find y = f(x)<\/p>\n

Let’s use the variable x to be spending<\/p>\n

Also, let’s use the variable y to be profit<\/p>\n

Let’s also write x = {40, 50, 60, 70, 20, 120)<\/p>\n

y = {160, 250, 360, 490, y5<\/sub>, y6<\/sub>}<\/p>\n

From out analysis, we found out that y = f(x) = x2<\/sup> \/10<\/p>\n

Now it would be easy to find y5<\/sub> and y6<\/sub><\/p>\n

y5<\/sub> = f(20) = 202<\/sup> \/ 10 = 400\/10 = 40<\/p>\n

y6<\/sub> = f(120) = 1202<\/sup> \/ 10 = 14400\/10 = 1440<\/p>\n

Therefore, if the supermarket spends $20.00 in 2019, then they would make a profit of $40.00<\/p>\n

Similarly, if they spend $120.00 in 2020, they would make a profit of $1,440.00<\/p>\n

Congrats!. You have solved a problem using Machine Learning<\/p>\n

 <\/p>\n

A Few Notes<\/strong><\/p>\n

What we just solved is actually a polynomial regression problem. This is because variable x is not linear in function we derived (we have x2).<\/p>\n

Other regression types include linear regression<\/em> and logistic regression<\/em>. We would cover these in subsequent classes.<\/p>\n

In the next lecture we would solve some linear regression problem. Then we would also do the same using python.<\/p>\n

 <\/p>\n

Practicals: How to Plot in Python<\/strong><\/p>\n

To conclude this class, I will show you how to plot this data in Python. I’m sure you have Jupyter Notebook installed.<\/p>\n

Just to remind you:<\/strong> Jupyter notebook provide environment where you write Machine Learning programs using Python syntax.<\/p>\n

The screenshot of the complete program is shown below. Ensure to do it yourself.<\/p>\n

Notice that we rearranged the data and placed (20, 40)\u00a0 in the first position:<\/p>\n

 <\/p>\n

\"Plotting<\/p>\n","protected":false},"excerpt":{"rendered":"

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