https://github.com/dmarks84/coursework_project_ml-model-eval-refine

Project for IBM Data Science course on ML Models & Analysis -- Read in large dataset of home sales and utilized polynomial linear regression analysis to make predictions of future home sales prices
https://github.com/dmarks84/coursework_project_ml-model-eval-refine

classification communication data-modeling dataframes machine-learning matplotlib numpy pandas programming python regression scikit-learn scipy seaborn supervised-ml visualization

Last synced: 11 days ago
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Project for IBM Data Science course on ML Models & Analysis -- Read in large dataset of home sales and utilized polynomial linear regression analysis to make predictions of future home sales prices

• Host: GitHub
• URL: https://github.com/dmarks84/coursework_project_ml-model-eval-refine
• Owner: dmarks84
• License: bsd-3-clause
• Created: 2024-01-17T17:16:37.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-01-17T23:22:16.000Z (12 months ago)
• Last Synced: 2024-12-23T13:17:13.915Z (11 days ago)
• Topics: classification, communication, data-modeling, dataframes, machine-learning, matplotlib, numpy, pandas, programming, python, regression, scikit-learn, scipy, seaborn, supervised-ml, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 147 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## Project(Project_ML-Model-Eval-Refine)

### Part of the Coursera series: IBM Data Science

    

## Summary

In this project, I took in data related to home sales in order to develop a model to predict future home sales.  We had to wrnagle the data and transform it, perform EDA and look at various correlations between features in order to set up a machine learning (polynomial linear regression) model on which to train and then create predictions.  We performed feature engneering and scaling in the process.

## Skills (Developed & Applied)

Programming, Python, Databases, Statistics, Probability, SciPy, Numpy, Pandas, Seaborn, Matplotlib, Scikit-learn, Data Modeling, EDA, Data Visualization, Data Summarization, Data Reporting, Regression, Supervised ML, Communication