Need help doing this assignment using R coding on R/R Studio, on how to set it up. Any help appreciated!

Problem Statement: A company would like to identify the employees that are likely to leave the company. The company has demographic, educational, and other information about the employees. The data set is split into two parts: HRRetention_train.csv and HRRetention_test.csv.

Variable Description enrollee_id : Unique ID for candidate city: City code city_ development _index : Developement index of the city (scaled) gender: Gender of candidate relevent_experience: Relevant experience of candidate enrolled_university: Type of University course enrolled if any education_level: Education level of candidate major_discipline :Education major discipline of candidate experience: Candidate total experience in years company_size: No of employees in current employer's company company_type : Type of current employer lastnewjob: Difference in years between previous job and current job training_hours: training hours completed target: 0 Not looking for job change, 1 Looking for a job change

Build predictive models (use at least 3 different algorithms) that would help the company to identify the employees that are likely to leave. Task1: Check the missingness in data. Use appropriate charts and tables to explain the missing data issues. Task2: Use appropriate data imputation techniques to impute data. Task 3: Run multiple algorithms to predict which employees are more likely to leave the company.

How the data looks like:

B N C D city_develof gender 0.92 Male 0.776 Male 0.624 0.789 0.767 Male 0.764 0.92 Male 0.762 Male 0.92 Male 0.92 1 enrollee_id city 2 8949 city_103 3 29725 city_40 4 11561 city_21 5 33241 city_115 6 666 city_162 7 21651 city_176 8 28806 city_160 9 402 city_46 10 27107 city_103 11 699 city_103 12 29452 city_21 13 23853 city_103 14 25619 city_61 15 5826 city_21 16 8722 city_21 17 6588 city_114 18 4167 city_103 19 5764 city_21 20 2156 city_21 21 11399 city_13 1 E F G H K L M relevent_exi enrolled_un education_major_disc experience company_size company_type last_new_job training_hours target Has relevent no enrollme Graduate STEM >20 1 36 No relevent no enrollme Graduate STEM 15 50-99 Pvt Ltd >4 47 No relevent Full time col Graduate STEM 5 never 83 No relevent experience Graduate Business D 20 50-99 Funded Startup 4 8 Has relevent Part time co Graduate STEM 11 1 24 Has releveni no_enrollme High School 5 50-99 Funded Startup 1 24 Has releveni no_enrollme Graduate STEM 13 4 18 Has releveni no_enrollme Graduate STEM 7 50-99 Pvt Ltd 1 46 Has releveni no_enrollm Graduate STEM 17 10000+ Pvt Ltd >4 123 No relevent Full time col High School 2 never 32 Has relevent no enrollme Graduate STEM 5 5000-9999 Pvt Ltd 1 108 Has relevent no_enrollme Graduate STEM >20 1000-4999 Pvt Ltd 3 23 No relevent experience 2 never 24 No relevent Full time cou High School 5 never 26 Has relevent no_enrollme Graduate STEM 16 Oct-49 Pvt Ltd >4 18 Has relevent no_enrollme Graduate STEM 1 50-99 Pvt Ltd never 106 Has releveni no_enrollm Graduate STEM 2 5000-9999 Pvt Ltd 2 7 Has releveni no enrollme Graduate STEM 7 10000+ Pvt Ltd never 23 Has relevent no_enrollme Graduate Arts 4 1 132 O O OOO 0 0.624 0.92 Male 0.913 Male 0.624 Male 0.624 0.926 Male 0.92 0.624 0.624 0.827 Female O O O O O O 1 1