https://github.com/waleedgeorgy/ml_sklearn

Implementation of various machine learning algorithms for regression and classification & feature engineering.
https://github.com/waleedgeorgy/ml_sklearn

data-visualization jupyter-notebook machine-learning python

Last synced: about 2 months ago
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Implementation of various machine learning algorithms for regression and classification & feature engineering.

• Host: GitHub
• URL: https://github.com/waleedgeorgy/ml_sklearn
• Owner: waleedGeorgy
• Created: 2024-12-04T15:00:36.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-04T15:40:52.000Z (over 1 year ago)
• Last Synced: 2025-04-08T17:19:49.673Z (about 1 year ago)
• Topics: data-visualization, jupyter-notebook, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 12.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
## Supervised Machine Learning With Scikit-Learn

Implementation of various machine learning algorithms for supervised regression and classification. Namely:

- Linear Regression.

- Multinomial Regression.

- Logistic Regression.

- K-Nearest Neightbour Clustering.

- Support Vector Machines (SVMs).

- Decision Trees.

- Random Forests.

- AdaBoosting.

- Gradient Boosting.

- Naive Bayes.


All this was done along with feature engineering and datasets cleaning and exploration, and using different regularization and cross-validation.


Data exploration and visualization was done using various different types of matplotlib plots and NumPy and Pandas functions.

## Unsupervised Machine Learning With Scikit-Learn

Similar to the above, but using algorithms like:

- K-Means Clustering.

- Hierarchical Clustering.

- DBSCAN.

- Principal Component Analysis (PCA).


In the unsupervised section, model deployment and persistence was also implemented.

Both notebooks contain models that are ready to train and deploy.