This repository contains a collection of machine learning projects focusing on data preprocessing and visualization techniques.

• preprocessing.ipynb: Contains data preprocessing techniques including:

  • Data loading and exploration
  • Min-Max scaling with sklearn
  • Standardization (0 mean, 1 standard deviation)

• plots.ipynb: Contains data visualization techniques including:

  • Basic data shape and statistics
  • Skewness analysis
  • Histograms for feature distributions

• feature_selection.ipynb: Contains feature extraction techniques including:

  • Univariate Feature Selection
  • Recursive Feature Elimination (RFE)
  • Principal Component Analysis (PCA)

The project uses the Pima Indians Diabetes Dataset, which includes several health metrics and a binary classification for diabetes. The dataset has the following features:

• 'preg': Number of pregnancies
• 'plas': Plasma glucose concentration
• 'pres': Blood pressure
• 'skin': Skin thickness
• 'test': Insulin level
• 'mass': BMI
• 'pedi': Diabetes pedigree function
• 'age': Age
• 'class': Binary outcome (diabetes or not)

To run these notebooks, you'll need:

• Python 3.x
• Jupyter Notebook
• Required packages: pandas, numpy, sklearn, matplotlib

1. Clone this repository
2. Install the required packages:

   pip install pandas numpy scikit-learn matplotlib jupyter
   

3. Start Jupyter Notebook:

   jupyter notebook
   

4. Open and run the notebooks in your browser

This project is open source and available under the MIT License.

Harrison Miller - GitHub Profile