I am working on a dataset, and i would like to create a model that would predict salary.
Columns are as follows:
Index(['ID', 'Salary', 'DOJ', 'DOL', 'Designation', 'JobCity', 'Gender', 'DOB', '10percentage', '10board', '12percentage', '12board', 'CollegeTier', 'Degree', 'Specialization', 'collegeGPA', 'CollegeCityTier', 'CollegeState', 'English', 'Logical', 'Quant', 'Domain', 'ComputerProgramming', 'ElectronicsAndSemicon', 'ComputerScience', 'MechanicalEngg', 'ElectricalEngg', 'TelecomEngg', 'CivilEngg', 'conscientiousness', 'agreeableness', 'extraversion', 'nueroticism', 'openess_to_experience'], dtype='object')
The train dataset contains around 3000 entries. For the feature 'JobCity', there are around 200 unique values. Out of 3000 entries, 180 are cities whose value counts are below 10. For example, the city 'Agra' occurs only twice and 'Ranchi' occurs only once. I plan to encode all the cities to feed into the machine learning (polynomial regression) model. How should the cities with value counts less than 10 be handled?