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