https://github.com/nickssilver/irismlpipeline

An example of a machine learning pipeline using the Iris dataset
https://github.com/nickssilver/irismlpipeline

Last synced: 10 months ago
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An example of a machine learning pipeline using the Iris dataset

• Host: GitHub
• URL: https://github.com/nickssilver/irismlpipeline
• Owner: nickssilver
• License: mit
• Created: 2023-04-19T20:52:27.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-04-19T21:14:42.000Z (over 2 years ago)
• Last Synced: 2025-01-22T03:17:43.401Z (12 months ago)
• Language: Python
• Size: 9.77 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Machine Learning Pipeline Example

This repository demonstrates the creation of a machine learning pipeline using scikit-learn with the Random Forest Regression algorithm. The pipeline includes data preprocessing, model training, and evaluation. The sources used for creating this example are as follows:

   - [turing.com: Building an ML Pipeline in Python with scikit-learn](https://www.turing.com/kb/building-ml-pipeline-in-python-with-scikit-learn)

   - [freecodecamp.org: Machine Learning Pipeline](https://www.freecodecamp.org/news/machine-learning-pipeline/)

   - [towardsdatascience.com: Building a Machine Learning Pipeline](https://towardsdatascience.com/building-a-machine-learning-pipeline-3bba20c2352b)

   - [analyticsvidhya.com: Build your first Machine Learning Pipeline using scikit-learn](https://www.analyticsvidhya.com/blog/2020/01/build-your-first-machine-learning-pipeline-using-scikit-learn/)

## Getting Started

   1. Clone this repository to your local machine.

   2. Install the required packages:

pip install scikit-learn pandas numpy

    Run the pipeline script:

python src/pipeline.py

## Pipeline Design

The pipeline is designed in three stages:

    Data preprocessing: The dataset is cleaned by dropping unnecessary columns, filling missing values, and encoding categorical features.

    Model training: The preprocessed data is split into training and testing sets, and a Random Forest Regression model is trained using the training set.

    Model evaluation: The trained model is evaluated on the testing set to measure its performance.

## Data Preprocessing

The data preprocessing stage includes the following steps:

   - Dropping unused columns: df.drop(['record_id', 'casual', 'registered', 'datetime', 'temp'], axis=1, inplace=True)

   - Creating pipelines for numerical and categorical features using Pipeline(steps=[('step name', transform function), …])

   - Filling missing values with SimpleImputer

   - Scaling numerical features with MinMaxScaler

   - Encoding categorical features with OneHotEncoder(handle_unknown='ignore')

## Model Training

In this stage, the preprocessed data is split into training and testing sets, and a Random Forest Regression model is trained using the training set. The pipeline is built using Pipeline(steps=[('scaler', StandardScaler()), ('classifier', RandomForestClassifier())]), and the model is trained with the fit() method.

Model Evaluation

The trained model is evaluated on the testing set using accuracy_score and balanced_accuracy_score from scikit-learn's metrics module. The results are printed to the console.

### Authors

 [Nicks M. Gitobu, Software Engineer](https://www.linkedin.com/in/nicholas-gitobu-973b081b9/)

License

This project is licensed under the **MIT License**.