https://github.com/lkethridge/machine_learning_in_business

Machine Learning in Business project for TripleTen
https://github.com/lkethridge/machine_learning_in_business

bootstrapping business-metrics confidence-interval conversion cross-validation data-collection data-labelling data-sources funnels machine-learning margin net-profit-margin operating-profit python return-on-investment revenue sklearn

Last synced: 6 months ago
JSON representation

Machine Learning in Business project for TripleTen

• Host: GitHub
• URL: https://github.com/lkethridge/machine_learning_in_business
• Owner: LKEthridge
• License: cc0-1.0
• Created: 2025-01-20T23:52:20.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-21T01:11:47.000Z (about 1 year ago)
• Last Synced: 2025-03-20T15:14:05.950Z (about 1 year ago)
• Topics: bootstrapping, business-metrics, confidence-interval, conversion, cross-validation, data-collection, data-labelling, data-sources, funnels, machine-learning, margin, net-profit-margin, operating-profit, python, return-on-investment, revenue, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 12.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Machine_Learning_in_Business

## *This was a Machine Learning in Business project for TripleTen. 👩🏽‍💻*

This project leveraged machine learning and bootstrapping to identify an optimal region among three options for fictional energy company OilyGiant’s expansion, focusing on maximizing profit and minimizing risk. Using a linear regression model and a dataset of 100,000 data points, Region 2 emerged as the best choice, with an average potential profit exceeding $4 million, a 95% confidence interval predicting positive returns, and only a 1.8% risk of loss. These findings provide a data-driven framework for OilyGiant to allocate resources effectively and maximize profitability.

## Skills Highlighted

🐍 Python and sklearn

🤖 Machine Learning and Cross-Validation

👩🏽‍💻 Data Collection and Labelling

💰 Business Metrics: Calculating Revenue, Operating Profit, Margin, and Return on Investment

📊 Statistical Methods: Bootstrapping and Confidence Intervals

💿 Data Sources

## Installation & Usage

* This project uses pandas, numpy, train_test_split, StandardScaler, shuffle, LinearRegression, accuracy_score, mean_squared_error, and matplotlib.pyplot.  It requires python 3.11.