Reference no: EM132628991

Assignment 1

Martin leads the customer relationship team for a telecom giant. He analyzed the historical data for service request and found that there have been frequent requests for plan change by customers. As a proactive measure to resolve this issue, Martin contacted the Data Analytics and Business Insights team to build a tool on Orange that can estimate the right bill for the Customers. Data analytics team proposed to build a regression model based on certainparameters that can be collected at the time of opening theaccount.

Now suppose, he approaches you and request for your help to complete the assignment. Help Martin in answering following things:

1. Which algorithm(s) can be used to solve above problem?

2. What are the steps to be followed in solving the above problem?

3. Solve the problem in Orange with abovesteps.

4. What is the parameter(s) to measure accuracy of the result, calculate the same forall algorithm(s)?

Data is provided in csv file named: "Assignment - 1.csv"

Assignment 2

Romanov, an Analytics consultant works with Credit One Bank. His manager gave him data having "Credit" and personal information of a group of customers. Some of the customers had defaulted in making the payment on balance due. He asked him to "identify" and "quantify" the factors responsible for defaults and find out the probability of default corresponding to each of the customers. Now suppose, he approaches you and request for your help to complete the assignment. Let's help Romanov in solving the problem.

Case- Multivariate Logistic Regression

1. Which algorithm(s) can be used to solve aboveproblem?

2. What are the steps to be followed in solving the above problem?

3. Solve the problem in Orange with abovesteps.

4. What is the parameter(s) to measure accuracy of the result, calculate the same forall algorithm(s)?

Data is provided in csv file named: "Assignment - 2.csv"

Additional details are provided in pdf file: "Assignment - 2_Details.pdf"

Assignment 3

Answer all thequestions:

1. What is Multicollinearity, why it is important to check the same and how we detect if it exists?

2. What is confusion matrix and why it isimportant?

3. What is the benefit Random Forest has over other DecisionTrees?

4. When do we use Linear Regression and when do we use LogisticRegression?

Attachment:- Data Science and Model Building Assignment.rar