https://github.com/lkethridge/integrated_project_2

Integrated Project 2 from TripleTen
https://github.com/lkethridge/integrated_project_2

anomaly-detection cross-validation data-analytics data-cleaning-and-preprocessing data-science feature-engineering gold-recovery machine-learning metal-purification model-evaluation pandas portfolio-project python scikit-learn smape supervised-learning

Last synced: 2 months ago
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Integrated Project 2 from TripleTen

• Host: GitHub
• URL: https://github.com/lkethridge/integrated_project_2
• Owner: LKEthridge
• License: cc0-1.0
• Created: 2025-01-21T06:00:30.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-22T16:00:17.000Z (over 1 year ago)
• Last Synced: 2025-03-20T15:14:01.310Z (over 1 year ago)
• Topics: anomaly-detection, cross-validation, data-analytics, data-cleaning-and-preprocessing, data-science, feature-engineering, gold-recovery, machine-learning, metal-purification, model-evaluation, pandas, portfolio-project, python, scikit-learn, smape, supervised-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 15 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Integrated_Project_2

## *This was an Integrated skill project for TripleTen. 👩🏽‍💻*

This project developed a machine learning solution for predicting gold recovery at the rougher and final stages of ore processing using datasets with over 80 parameters. A Multi-Output Random Forest Regression model provided the most accurate predictions during training, with Linear Regression as a viable, less computationally intensive alternative. Despite underperforming compared to constant benchmarks on the test set, the models demonstrate the potential for data-driven optimization of industrial processes.

## Skills Highlighted

🐍 Python

👩🏽‍💻 Data Science

🤖 Machine Learning

🧪 Scikit Learn

❌ Cross Validation

🐼 pandas

📊 Data Analytics

👀 Supervised Learning

⚙️ Feature Engineering

💯 Model Evaluation

🕵🏽‍♀️ Anomaly Detection

🧼 Data Cleaning and Preprocessing

## Installation & Usage

* This project uses pandas, numpy, RandomForestRegressor, MultiOutputRegressor, LinearRegression, mean_squared_error, mean_absolute_error, make_scorer, matplotlib.pyplot, shuffle, StandardScaler, seaborn, SimpleImputer, cross_val_score, KFold, and RandomizedSearchCV.  It requires python 3.9.6.  There is one additional file containing the full, unsplit test set that I was unable to upload due to upload limitations.