a company would like to identify the employees that are likely to leave the company.

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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. Task 4: What are the most important predictors of whether an employee will leave the company? Clearly state your conclusions and recommendations for the company’s HR department.

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