The objective of this Portfolio Project is mining data from a data warehouse, which contains data from the Clothing Store csv file supplied to the class. The Clothing Store file contents are covered in greater detail in Chapters 29-31 of the Data Mining and Predictive Analytics textbook. This data set is large, with over 28,000 records and over 50 fields. You may wish to trim the data in the csv file before moving forward.Below are the summarized tasks for this Portfolio Project.Data Warehouse:Create a data warehouse database, including the fact and dimension tables (star schema).Create the schema for each table.Load the Clothing Store csv into a database (which you will need to create), including the tables and the schema, or retain the data in csv format.Populate the tables using either ETL (Pentaho) or SQL (PostgreSQL).Preprocessing for SAS:Extract data from the data warehouse, creating a file for input into SAS. The format of the file is your choice. Ensure SAS University Edition accepts your selected format.Statistical Analysis Using SAS:Import data created in the preprocessing step.Conduct statistical analysis using the appropriate statistics from each category:Summary statisticsClassificationClusteringAssociationPrepare an analysis report.Using your plan prepared in Module 3, Milestone 1, and leveraging the data warehouse and preprocessing steps in Module 6, Milestone 2, complete the tasks under Statistical Analysis Using SAS. (I have uploaded module 3 and 6 so you can see the what was done).Your analysis report must include:An analysis of each variable in the data setAn analysis to determine which variables could serve as appropriate classifier variablesAn analysis to determine if any variables are candidates for clusteringAn analysis to determine if any variables have associationsAny tables, histograms, or scatterplot graphs necessary to support your analysesA recommendation as to the suitability of this data set for meeting your organization’s business goalYour project must meet the following requirements:Be 6-8 pages in length, not including the cover and references pages.

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.

Q1: calculate 120 credits to graduate in Python coding 1.  Input = Number of credits taken 2. Calculate: 120 - Input 3. Print a statement "You need XXX credits to graduate." Q2: Write the following program. 1.  Create a class for Music and the parameters are genre (for example, pop, rock, classical, jazz) and recording medium (for example, digital, cd, record)  2. Create a child class for musicians;  the parameters would include: artist name, song, song release date 3. Create two instances from the child class (you can make up the data for this instance) 4. Print a sentence for each instance that says: "My favorite {genre} song from {artist name} is {song} released in {date} on {medium}." Q3: Debug and put the correct answer in colab.  Label this Question 16 in colab. prompt = "\nWhat is one of your favorite numbers:" prompt = "\nIf you would like to stop enter quit." while True: number = input(prompt) if number = 'quit' break else: print("What is another of your favorite number:") Q4: Prompt = "\nPlease enter what you would like to order: " Prompt += "\n(Enter 'done' if you are finished with your order.)" while True: Meal == imput(prompt) if meal = 'done' break else: print(F"You would like to order {meal.title()}.") Q5: create a txt file called dog.txt (Some examples could be:  poodle, lab, collie, sheltie, pitbull) Fix this code- filename = 'dog.txt' with open(filename) as dog.object     for dog in dog.object           print(dog.rstrip())