Techniques for working with Traditional Methods

1. You have prepared the BMI reports and dashboards and the executives have extracted insights about the business what do you do with the information you use it to predict some futuer values as accurately as possible. That’s why at this stage you stop dealing with analysis and start applying analytics more precisely predictive analytics. Remember that we separate predictive analytics into two branches traditional methods which comprises classical statistical methods for forecasting and machine learning. What are the techniques involved when applying traditional data science methods though there are many a word we often come across is regression.
2. Regression: In business in statistics a regression is a model used for quantifying causal relationships among the different variables included in your analysis.
3. Look at this dataset in one column. We have house prices in dollars while the other house sizes measured in square feet every row on the data table is an observation and each can be plotted on this graph as a dot the house size is measured along the horizontal line in its price. On the vertical line the further to the right an observation is the larger the house size and further up the higher the price so once we’ve plotted all 20 observations from our dataset our graph will appear like this. The thing is there is a straight line called a regression line that goes through these dots while being as close as it can be to all of them simultaneously. This means that it more accurately represents the distribution of the observations.
4. Logistic regression is a common example of a nonlinear model. In this case unlike the house prices example the values on the vertical line won’t be arbitrary integers. The values on the vertical line will be 1s and 0s only. They’ll be ones or zeros only such a model’s useful during a decision making process. Let’s elaborate on that. Companies apply logistic regression algorithms to filter job candidates during their screening process. If the algorithm estimates the probability that a prospective candidate will perform well and the company is above 50 percent it would predict one or a successful application. Otherwise it wll predict zero therefore the nonlinear nature of the logistic regression is nicely summarized by its Graph very different from the linear regression.
5. Cluster allows us to conclude that location is a significant factor when pricing a house.
6. Time series: Use this technique especially if you are working in economics or finance in these fields you will have to follow the development of certain values over time such as stock prices or sales volume you can associate time series with plotting values against time. Time will always be on the horizontal line as time is independent of any other variable therefore such a graph can end up depicting a few lines that illustrate the behavior of your stocks over time.
7. factor analysis can be used technique to implement if you want to reduce the dimensionalty of a certain statistical problem.
8. User experience (UX) and sales forcasting can be used in the traditional methods.