Machine learning - Kaggle competition

The goal of the competition is to obtain the lowest RMSE from machine learning predictions of a data jobs.

Competition bases

Predict the salary for data Jobs with machine learning
Data:
- Salaries_data.csv (Working data)
- Testeo.csv (Predicting data)
- Muestra.csv (Example for Kaggle)
Data will be uploaded to Kaggle and RMSE will be calculated as a score.
Lowest score wins the competition

Salary and salary_currancy: both were eliminated from the table (salary_in_usd, the column-to-predict, proceed from both).
Company location: Grouped by 'US' or 'Not US'. Too many values to get a good correlation value and to work with.
Employee residence: Grouped by 'US' or 'Not US'. Too many values to get a good correlation value and to work with.
Employment type: Grouped by 'FT' or 'Not FT'. Too many values to get a good correlation value and to work with.
Job tittle: restricted to 'Data Scientist’, 'Data Analyst’, 'Machine Learning Engineer’, 'Research Scientist’ and ‘Other’
Categorical to numeric. With 'get_dummies', I transformed categorical columns to numeric colums to work with them.

Correlation values were improved with these changes.

RMSE values were obtained with its own training:

🥉 Third ranking position from my class!

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