As the new Chief Technology Officer, you have been tasked with evaluating the impact of SaaS on enterprise software licensing and strategies. Your analysis must include the cost, scalability, and customization considerations, and the impact SaaS will have on network operations.Your well-written paper must meet the following requirements:Your paper must be 4-6 pages in length and conform to the CSU Global Writing Center (Links to an external site.). Be sure to include an introduction, a body with at least two fully developed paragraphs, and a conclusion. Also include at least four scholarly references in addition to the required readings. The CSU Global Library is a good place to find these resources.Write clearly, using excellent grammar and style techniques. Be concise and logical. You are being graded, in part, on the quality of your writing. If you need assistance with your writing style, start with Tools for Effective Writing at the CSU Global Library (Links to an external site.), accessible from the Library's homepage.

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ITS 632FINAL DATA MINING PROJECTA. The ProjectThis project has 2 parts:• 1st part is for everyone as your Weekly Assignment.• 2nd part is optional if you want to make up your midterm score.You can get up to 20 points added to your exam score.To make it easier on you I will allow you to submit it as a group. The groups can be up to 4 people.But you can submit is alone too. Just let me know your groups in advance.You can use Decision Trees which we cover or other more sophisticated methods like RandomForests or Logistics Regression (These two algorithms might generate better results.)• Part1: You will develop a Classification/Prediction model that predicts whether or not apatient has a disease or is healthy (Part 1).• Part2: You will do same for individual diseases. If a patient has that disease or not.B. Data1. You are provided with an Excel file that has 11 Worksheets.2. The first one (Hormones_Diseases) has the Hormones vs Diseases table. This table listsall Hormonal Measurement values of babies (patients) and their relationship (correlationlevel) with each disease. These are coming from doctors (Endocrinologists) who areexperts on these diseases but they are the ones who need help to better diagnose thepatients. So, use these correlation or relationship values as starting points or givingweights to your attributes (hormones) but do not completely ignore the other hormonesthat show no relationship. After all the data might suggest that some of those blank onesare good predictors in predicting whether or not a genetic disease exists.3. The Up and Down arrows here shows the direction and strength of these relationship andcorrelation between diseases and hormones according to the doctors. For example,↓↓↓↓ means this hormone is strongly but negatively correlated with this diseasewhere ↑ means they are mildly but positively correlated. Blank ones are not consideredcorrelated by the doctors but they also think they are important. We already removed 3hormones which they deemed no importance at all. Again, please do not ignore thehormones that are blanks. Make sure you give more importance to the ones with morearrows (using weights is one option, positive value for Up and negative for Down).4. The other 10 Worksheets are the measurements of the hormonal levels of each hormonefor the patients that has that disease. For example, all the patients in 2) 21OHD-C Diseaseworksheet has the 21OHD-C Disease and these are their measurements. All otherworksheets are same way.C. Data Preparation1. For Part 1: you will need to create a Dataset where Patients are listed in tabular formatand their information (attributes) as columns and 1 final attribute which is the class. Icreated a worksheet as a last worksheet titled Dataset Format to help you get started butyou can choose your own format. (you can either create this dataset by copy/paste withTranspose or create a small Macro if you know VBScript) (if you are not going to do Part2, you do not need to have a column called Disease.)2. For Part 2: If you are going to Part2, you can either;a. Create a Classification Model that predicts the Disease (Disease is the class attributeand this is a multi-class problem).b. It would be simpler if you develop a Classification Model just like in Part 1, but thistime train and test it with only data that has this disease and some from Healthy.c. Or you can keep the ones that has the disease as “yes” in Sick? attribute and changeeveryone else to no including patients who have other diseases. (meaning that theydo not have that certain genetic diseases)D. Project & Submission1. Part 1: Develop a Classification Model that predicts whether or not a patient has adisease, improve your model to achieve better predication accuracy & submit following:a) A report that describes the Classification Algorithm you choose and Parametersettings, the Accuracy and Confusion Matrixb) The final code either as Jupyter Notebook (preferably) or .py file(s) (Make sure youexplain any dependencies since I need to run your code and see the same results)c) The dataset you created from Excel file and used in your model training and testing.2. Part2: Submit the same only this time develop a Model that predict if the patient hascertain disease as explained in section A. Project above.