AI & ML Toolkit

Welcome to the AI & ML Toolkit repository. This toolkit is designed to provide a comprehensive suite of tools and algorithms for machine learning and artificial intelligence applications. It includes a variety of classification, regression, and clustering techniques, along with utilities for model selection and evaluation.

Features

Classification Techniques

This toolkit includes several classification algorithms, each suited for different kinds of data and use cases:

1. Logistic Regression: A fundamental technique for binary classification problems.
2. K-Nearest Neighbors (K-NN): A non-parametric method used for classification and regression.
3. Support Vector Machine (SVM): Effective for high-dimensional spaces.
4. Kernel SVM: An extension of SVM that uses kernel functions.
5. Naive Bayes: A simple yet powerful probabilistic classifier.
6. Decision Tree Classification: A tree-like model of decisions.
7. Random Forest Classification: An ensemble of decision trees, typically used for tackling overfitting.

Artificial Neural Network

Implementation of neural network architecture for deep learning applications, adaptable for various types of data.

Boost Techniques

Methods to enhance the performance of weak learning models, turning them into stronger ones.

Model Selection

Utilize the Confusion Matrix and k-Fold Cross Validation techniques for evaluating and selecting the most suitable models.

Regression Techniques

A set of tools for analyzing and predicting continuous data:

1. Multiple Linear Regression: To model the linear relationship between a dependent variable and two or more independent variables.
2. Polynomial Regression: An extension of linear regression that fits a non-linear relationship between the value of x and the corresponding conditional mean of y.
3. Decision Tree Regression: A decision support tool that uses a tree-like model of decisions.
4. Random Forest Regression: An ensemble learning method for regression.
5. Support Vector Regression (SVR): An adaptation of SVM for regression problems.

Model selection can be performed via the R-Squared Mean.

Clustering Techniques

1. Using the Elbow Method: To find the optimal number of clusters.
2. Visualizing Clusters: Tools and techniques to visualize the data clusters effectively.

Getting Started

To get started with the AI & ML Toolkit, clone this repository and install the required dependencies.

git clone https://github.com/davykiash/ai-ml-toolkit.git
cd ai-ml-toolkit
pip install -r requirements.txt

Tutorial and Case Study

For a practical demonstration of how to apply these techniques, check out our blog post titled "Leveraging AI/ML for Predictive Analytics in Business: A Case Study on Customer Default Prediction." This post provides an in-depth case study and step-by-step guide on using various AI and ML tools for predictive analytics in business contexts.

Read the Blog Post: Leveraging AI/ML for Predictive Analytics in Business: A Case Study on Customer Default Prediction.

This blog post provides step-by-step tutorials and best practices for effectively utilizing the tools and algorithms in this toolkit.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

Special thanks to the following resources for their invaluable contributions and references that helped shape this toolkit:

• Super DataScience - For providing extensive materials and courses on Machine Learning.
• Scikeras - For offering a scikit-learn compatible wrapper for Keras, facilitating the integration of deep learning models into traditional ML pipelines.
• Datasets used in this toolkit:
  • Default of Credit Card Clients Dataset - For analysis and modeling in financial risk management.
  • Productivity Prediction of Garment Employees Dataset - For workforce productivity analysis.