Reference no: EM133214633

Data Science model Building

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 certain parameters that can be collected at the time of opening the account.
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 above steps.
4. What is the parameter(s) to measure accuracy of the result, calculate the same for all 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. Lets help Romanov in solving the problem.
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 above steps.
4. What is the parameter(s) to measure accuracy of the result, calculate the same for all algorithm(s)?

Assignment 3:

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 is important?
3. What is the benefit Random Forest has over other Decision Trees?
4. When do we use Linear Regression and when do we use Logistic Regression?

Attachment:- Data Science model Building.rar