Salary analysis updated to 2023 in the field of data science, focusing on the exploratory analysis of the source dataset and the creation of a Machine Learning model for salary prediction, optimizing its performance. 🔍💰📈
- Exploratory analysis of the source dataset 🔍
- Creation of a Machine Learning model for salary prediction 💰
- Performance optimization of the model 📈
Within this dataset are the salaries of several Data Science Fields in the Data Science Domain. The goal is to describe the characteristics of the data and then build a model that allows us to identify what the wage is according to different patterns identified earlier.
Data Science Job Salaries Dataset contains 11 columns, each are:
- work_year: The year the salary was paid.
- experience_level: The experience level in the job during the year
- employment_type: The type of employment for the role
- job_title: The role worked in during the year
- salary: The total gross salary amount paid
- salary_currency: The currency of the salary paid as an ISO 4217 currency code
- salaryinusd: The salary in USD
- employee_residence: Employee's primary country of residence in during the work year as an ISO 3166 country code
- remote_ratio: The overall amount of work done remotely
- company_location: The country of the employer's main office or contracting branch
- company_size: The median number of people that worked for the company during the year
- Import the necessary libraries
- Read the dataset and get an overview
- EDA a.Univariante,b.Bivariante
- Data preprocessing if any
- Define the performance metric and build ML models
- Compare models and determine the best one
- Observations and business insights