Machine Learning in Business

Background

This project is part of the Data Scientist training program from Practicum by Yandex. More info in link below:

https://practicum.yandex.com/data-scientist

Objective

You work for the OilyGiant mining company. You have data on oil samples from three regions. Parameters of each oil well in the region are already known. Build a Linear Regression model that will help to pick the region with the highest profit margin. Analyze potential profit and risks using the Bootstrapping technique.

Data description

Geological exploration data for the three regions are stored in files:

• geo_data_0.csv
• geo_data_1.csv
• geo_data_2.csv
• id — unique oil well identifier
• f0, f1, f2 — three features of points (their specific meaning is unimportant, but the features themselves are significant)
• product — volume of reserves in the oil well (thousand barrels).

Conditions:

• Only linear regression is suitable for model training (the rest are not sufficiently predictable).
• When exploring the region, a study of 500 points is carried with picking the best 200 points for the profit calculation.
• The budget for development of 200 oil wells is 100 USD million.
• One barrel of raw materials brings 4.5 USD of revenue The revenue from one unit of product is 4,500 dollars (volume of reserves is in thousand barrels).
• After the risk evaluation, keep only the regions with the risk of losses lower than 2.5%. From the ones that fit the criteria, the region with the highest average profit should be selected.
• The data is synthetic: contract details and well characteristics are not disclosed.

Libraries Used

• Pandas
• Matplotlib.pyplot
• scipy.stats
• numpy
• sklearn

Models Evaluated

• LinearRegression