Kurt "The Cyberguy" Knutsson speaks on the anticpation of automated gas stations that are already refueling cars in Finland. Do you find filling up your car with gas a chore? How about letting a robot do it for you? A Denmark based company called Autofuel has introduced a new robotic refueling system that can fill up your car without you ever getting out of the comfort of your front seat. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS When you sign up for the Autofuel system, you put in your car details such as make, model and license plate, what kind of fuel you want, and your payment details.

Let's set up the required configuration and start MindsDB. If you are following this tutorial with your own data, you can skip to the next section. For this example we will use the Audi Car Price dataset from the 100k used cars scraped data. The dataset contains information on price, transmission, mileage, fuel type, road tax, miles per gallon (mpg), and engine size of the used cars in the UK. The idea is to predict the price depending on the above features. The first thing we need to do is to create the table.

The model predicts or estimates price (target) as a function of engine size, horsepower, and width (predictors). The model has all the predictors as numeric values. What if there are qualitative variables? How can the qualitative variables be used in enhancing the models? How are the qualitative variables interpreted? These are the few questions this blog post will answer.

Let's walk through an example of predictive analytics using a data set that most people can relate to:prices of cars. In this case, we have a data set with historical Toyota Corolla prices along with related car attributes. In predictive models, there is a response variable(also called dependent variable), which is the variable that we are interested in predicting. The independent variables(the predictors also called features in the machine learning community) are one or more numeric variables we are using to predict the response variable. Given we are using a linear regression model, we are assuming the relationship between the independent and dependent variables follow a straight line.

Let's walk through an example of predictive analytics using a data set that most people can relate to:prices of cars. In this case, we have a data set with historical Toyota Corolla prices along with related car attributes. Let's load in the Toyota Corolla file and check out the first 5 lines to see what the data set looks like: Price, Age, KM(kilometers driven), Fuel Type, HP(horsepower), Automatic or Manual, Number of Doors, and Weight in pounds are the data collected in this file for Toyota Corollas. In predictive models, there is a response variable(also called dependent variable), which is the variable that we are interested in predicting. The independent variables(the predictors also called features in the machine learning community) are one or more numeric variables we are using to predict the response variable.

Let's walk through an example of predictive analytics using a data set that most people can relate to:prices of cars. In this case, we have a data set with historical Toyota Corolla prices along with related car attributes. Let's load in the Toyota Corolla file and check out the first 5 lines to see what the data set looks like: Price, Age, KM(kilometers driven), Fuel Type, HP(horsepower), Automatic or Manual, Number of Doors, and Weight in pounds are the data collected in this file for Toyota Corollas. In predictive models, there is a response variable(also called dependent variable), which is the variable that we are interested in predicting. The independent variables(the predictors also called features in the machine learning community) are one or more numeric variables we are using to predict the response variable.

This is part two of the series. In part one, we used linear regression model to predict the prices of used Toyota Corollas. There are some overlap in the materials for those just reading this post for the first time. For those who read the part 1 of the series using linear regression, then you can safely skip to the section where I applied neural networks to the same data set. In this post, we will use neural networks!