how do we solve tthis or how do we make this project

Project description.pdf - The discussion sessions will be scheduled during the 143' week of the semester. Each group will be assigned 10 min to answer questions related to their work. Weight of the work: This work weighs 15% of the total mark for this course. The mark will be given according to the report content and the discussion with the team members. The group mark will be given to all members unless I found out that the contributions of the team members are not equal. In this case, I will ask all members to submit a peer assessment sheet and give the mark accordingly. So, please make sure you all work equally. Available data gegptign: Datasetl: Supermarket Sales The growth of supermarkets in most populated cities are increasing and market competitions are also high. This dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. The le contains the following data: Invoice 1D, branch (3 branches: A, B and C], city where the supercenter is located, type of customers recorded by members for customers using member card and normal for those without a card, gender, product line (general item categorization groups (Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel), unit price in 5, quantity sold, tax (5% tax fee for customer buying}, total price including tax, date of purchase, purchase time (10am to 9pm), payment method used (3 methods available; Cash, Credit card and Ewallet), COGS (Cost of goods sold), gross margin percentage, gross income, customer stratication rating on their overall shopping experience (A scale ofl - 10) Dataset 2: Human Resources Data Set The dataset revolves around a ctitious company and the core data set contains names, DOBs, age, gender, marital status, date of hire, reasons for termination, department, whether they are active or terminated, position title, pay rate, manager name, and performance score, absences, the most recent performance review date, and employee engagement score. Dataset 3: Loan Application Data Among all industries, Banking domain has the largest use of analytics & data science methods. This data set would provide you enough taste of working on data sets from insurance companies and banks, what challenges are faced, what strategies are used, etc. The data has 615 rows and 14 features to predict weather loan approved or not approved. Company wants to automate the loan eligibility process (real time) based on customer detail provided while lling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specically target these customers. 11:12 7