README.md

MachineLearningSOL

README

Prerequisites

• Python 3.10

Setup

1. Create a virtual environment:

   python3.10 -m venv venv

2. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

Building a Forecast Model for Healthcare Data

Objectives of this Repository:

• Data Structuring: Organizing the healthcare dataset into a suitable format for analysis and modeling.
• Data Cleaning: Handling missing values, correcting inconsistencies, and preparing the data for use.
• Data Mining and Visualization: Extracting insights from the data using visualizations and exploratory data analysis techniques.
• Model Training: Developing and evaluating machine learning models to forecast healthcare-related outcomes.

Learning Material:

• Pandas DataFrame operation:
  • Pandas Documentation: https://pandas.pydata.org/docs/
  • DataFrame Indexing: https://pandas.pydata.org/docs/user_guide/indexing.html
• Matplotlib for data visualization:
  • Matplotlib Documentation: https://matplotlib.org/stable/contents.html
  • Creating subplots: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html
• NumPy for numerical operations:
  • NumPy Documentation: https://numpy.org/doc/stable/
  • NumPy arrange function: https://numpy.org/doc/stable/reference/generated/numpy.arange.html
• Data visualization techniques:
  • Grouped bar charts: https://matplotlib.org/stable/gallery/lines_bars_and_markers/barchart.html
  • Customizing plots: https://matplotlib.org/stable/tutorials/introductory/customizing.html
• scikit-learn Documentation:
  • Main page: https://scikit-learn.org/stable/
  • RandomForestClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
  • GridSearchCV: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
• Model Evaluation:
  • Classification metrics: https://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics
  • accuracy_score: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
  • classification_report: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html
• Data Splitting:
  • train_test_split: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
• Joblib for model persistence:
  • Joblib documentation: https://joblib.readthedocs.io/en/latest/
  • Persisting scikit-learn models: https://scikit-learn.org/stable/model_persistence.html
• IPython Display:
  • IPython display module: https://ipython.readthedocs.io/en/stable/api/generated/IPython.display.html
• Feature Engineering:
  • Pandas get_dummies for one-hot encoding: https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html
• Python Standard Library:
  • Exception handling: https://docs.python.org/3/tutorial/errors.html
• ** Transformers **:
  • Transformers Documentation: https://huggingface.co/docs/transformers/index
  • Tokenizer: https://huggingface.co/transformers/main_classes/tokenizer.html
  • Trainer: https://huggingface.co/transformers/main_classes/trainer.html

[1] https://pandas.pydata.org/docs/ [2] https://pandas.pydata.org/docs/user_guide/indexing.html

Note

Dataset: National Health and Nutrition Examination Survey (NHANES) – Vision and Eye Health Surveillance This dataset is sourced from Centers for Disease Control and Prevention. Vision Classification model: https://huggingface.co/Quexoo/vision-classifier