Okay, so, for this cause I, for these cyber tar bodies, data mining process, this data mining process is actually from this IBM, Chris dm. So in this Christy m model, we have Lisa business understanding stage. So in this business understanding stage, we actually are try to, let's say, Fine, who will be the person who will do the data mining project data analysis to the person can be, let's say our data analysis, data scientists, and then who are the decision makers, maybe the higher management I know who can, let's say, advice or let's say, give some better opinions on on the data. So there will be the domain expert, so Who will be the domain expert? And then actually, what we try to what we want to get from this data mining project, what are the goals for this data mining project. So, this will be the business understanding stage.

So, in the next stage is the data understanding stage, which is here. So, for data understanding stage so let's say we already get our data. So we want to understand more about our data. So, we can use some of these are descriptive statistics. Some of these are inferential statistics. Some of these are regression analysis to understand more about the data.

We can also use some of the data visualizations like a histogram, the bar chart, a line chart or the scatterplot or less a scatter plot matrix, we can use all these data visualizations to help us understand the data. We can use a statistic like descriptive statistics to understand about the mean the medium of these data. You can also use some other t tests or inferential statistics to let's say, understand about that see how similar it is to data. And then we can use regression analysis to analyze on the relation between the two variables or we can also use a correlation to understand about the relation between two variables. So, this is in the data understanding stage. So, after we understand about the data, so, we can go into this data preparation stage So, in this data preparation stage, we can try to remove the rows with missing values or we can try to replace the missing values with the mean of the variable or we can replace the missing values with the more of the variables, we can also try to do some of the normalization or some of the trauma, some of the transformation.

So, this is in the data preparation stage. So, these are data preparation stage you can say is data processing some of the cleaning data data cleaning stage. So, in this data preparation, we try to improve on the data quality or improve on the data for our modeling stage. So in these are modally. We can try to use or some of These are statistical and you know machine learning algorithms to lesya develop create the classification model or the prediction model. So, for classification model we will try to predict categorical variables.

For prediction models we will try to predict those are numeric or those are continuous variables. So, regression models is also to predict a numeric or categorical or the continuous variable. Then, for evaluation stage let's see after we develop or we create a few models. Then we will want to use the evaluation stage to let's say, try to find the accuracy or the precision and recall the models and then based on the accuracy of the models we can select our essay which most orders to be put into this deployment stage. So in this deployment stage, it can be a report or it can be some PowerPoint slides, or it can be. Let's see, after we train the models and then we select the models with the highest accuracy and we use the models to create a self software system that's a prediction or recommendation system in this deployment stage.