**Predictive analytics 101- 6 : Summary**

## WHAT CAN YOU LEARN IN THIS LECTURE?

Let us overview method of predictive analytics and provide more advanced models to learn going forward.

## Predictions are key to success in business

I explained linear regression model and logistic regression model in the course. Both of them enable us to predict what is needed to make better business decisions.

- Liner regression model is used to predict metrics (price, temperature, number of customers, sales revenue and cost, and so on)
- Logistic regression model is used to predict probabilities (probability of defaults, probability of purchase, probability of churn, probability of fraud, and so on)

"What is needed" is called "Target" and it should be defined before starting data analysis. It is our job to do. Once "Target" is defined, we can start data analysis to predict "Target".

Once you are familiar with these two models, I am sure you can understand more advanced models, such as neural network by yourself as these two models are used as components of neural network.

## R language is an awesome tool to start data analysis

In practice, analytic tools are very important to analyze data effectively. I recommend "R language" for beginners of data analysis because

- Free, no fee to be paid
- Many materials to learn
- Widely used all over the world

When you are getting familiar with data analysis, R language provides you more advanced methods

- More than 7000 packages are available to analyze data
- An analytic tool for big data called "H2O' can be used from R cocsole

## Let us do it now!

So you have your analytic tools and your data now. All you have to do is just starting data analysis. The more you do, the more you are familiar with it. You can be an expert of data analysis in the future. Good luck!

November 1, 2015

Notice: TOSHI STATS SDN. BHD.