# susanli2016/machine-learning-with-python

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4,583 stars · 4,774 forks · Jupyter Notebook

## Links

- GitHub: https://github.com/susanli2016/Machine-Learning-with-Python
- awesome-repositories: https://awesome-repositories.com/repository/susanli2016-machine-learning-with-python.md

## Description

This project is a Python machine learning library and data science toolkit designed for building predictive models and analyzing complex datasets. It provides a collection of implementations for common supervised and unsupervised algorithms using the Scikit-Learn framework.

The toolkit includes a predictive modeling suite for generating predictions from historical data and a statistical analysis framework for applying Bayesian modeling and causality tests. It also features a data visualization suite based on Matplotlib for rendering static charts and graphs to interpret classifier boundaries and data trends.

The project covers data clustering workflows for identifying patterns and segments, exploratory data analysis, and the preprocessing of data using Pandas and NumPy.

## Tags

### Artificial Intelligence & ML

- [Python Machine Learning Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/python-machine-learning-libraries.md) — Provides a comprehensive collection of machine learning algorithms and data science tools implemented in Python.
- [Scikit-Learn Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/scikit-learn-implementations.md) — Provides a comprehensive collection of common supervised and unsupervised machine learning algorithms implemented via the Scikit-Learn API.
- [Clustering Algorithms](https://awesome-repositories.com/f/artificial-intelligence-ml/k-means-clustering/clustering-algorithms.md) — Implements various unsupervised grouping techniques, including k-means, to identify segments within datasets.
- [Predictive Machine Learning Analytics](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/algorithms/predictive-machine-learning-analytics.md) — Executes machine learning algorithms to generate predictions from historical data patterns. ([source](https://github.com/susanli2016/machine-learning-with-python#readme))
- [Predictive Model Development](https://awesome-repositories.com/f/artificial-intelligence-ml/predictive-model-development.md) — Implements machine learning algorithms in Python to design, train, and test predictive models.
- [Predictive Modeling](https://awesome-repositories.com/f/artificial-intelligence-ml/predictive-modeling.md) — Provides a toolkit for applying mathematical algorithms to datasets to predict future outcomes or classify data.
- [Exploratory Data Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/data-preparation/exploratory-data-analysis.md) — Provides tools for loading, cleaning, and visualizing datasets to understand their structure before modeling.
- [Hyperparameter Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/model-parameter-tuning/hyperparameter-tuning.md) — Implements systematic hyperparameter adjustment through repeated training cycles to refine model accuracy.
- [Statistical Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/statistical-analysis.md) — Employs descriptive and inferential statistics, including Bayesian modeling, to interpret complex datasets.

### Data & Databases

- [Data Science Toolkits](https://awesome-repositories.com/f/data-databases/data-science-toolkits.md) — Provides a collection of scripts using Pandas and NumPy for cleaning, preprocessing, and analyzing complex datasets.
- [Tabular Data Manipulations](https://awesome-repositories.com/f/data-databases/tabular-data-manipulations.md) — Utilizes Pandas to structure raw datasets into tabular dataframes for efficient cleaning and preprocessing.

### Graphics & Multimedia

- [General Data Clustering](https://awesome-repositories.com/f/graphics-multimedia/point-cloud-clustering/general-data-clustering.md) — Groups similar data points using clustering techniques to identify hidden patterns and segments. ([source](https://github.com/susanli2016/machine-learning-with-python#readme))
- [Matplotlib](https://awesome-repositories.com/f/graphics-multimedia/chart-generators/matplotlib.md) — Generates static two-dimensional charts and graphs to represent data distributions and classifier boundaries using Matplotlib.

### Scientific & Mathematical Computing

- [Vectorized Data Processing](https://awesome-repositories.com/f/scientific-mathematical-computing/numpy-array-integration/vectorized-data-processing.md) — Uses NumPy vectorized operations on contiguous memory arrays to ensure high computational efficiency for mathematical operations.
- [Bayesian Statistical Modeling](https://awesome-repositories.com/f/scientific-mathematical-computing/bayesian-statistical-modeling.md) — Applies Bayesian modeling and causality tests to extract insights and identify relationships in complex datasets. ([source](https://github.com/susanli2016/machine-learning-with-python#readme))
- [Statistical Analysis Libraries](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-mathematical-foundations/statistics-probability/statistical-analysis-libraries.md) — Ships a framework for applying Bayesian modeling and causality tests to extract insights from complex datasets.

### Software Engineering & Architecture

- [Data Trend Visualizations](https://awesome-repositories.com/f/software-engineering-architecture/composable-architectures/visualization-patterns/data-trend-visualizations.md) — Creates graphical representations of datasets and classifiers to identify and interpret data trends. ([source](https://github.com/susanli2016/machine-learning-with-python#readme))

### Part of an Awesome List

- [Machine Learning](https://awesome-repositories.com/f/awesome-lists/ai/machine-learning.md) — Jupyter notebooks for machine learning algorithms.