Machine Learning Frameworks

Overview

This repository is a collection of machine learning projects focused on classification, clustering, and segmentation tasks. Each project demonstrates the application of various machine learning algorithms and techniques to solve real-world problems using Python. The repository is structured to provide a comprehensive understanding of different machine learning frameworks, from basic classification to advanced clustering and risk classification.

Projects Included

1. Iris Flower Classification

   • This project classifies iris flowers into three species—Setosa, Versicolor, and Virginica—based on their sepal and petal measurements. The classification is achieved using various machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, and Support Vector Machine. The project emphasizes data exploration, preprocessing, model training, and evaluation.

2. Customer Segmentation for a Wholesale Distributor

   • This project focuses on segmenting customers of a wholesale distributor based on their annual spending across various product categories. It involves data cleaning, exploratory data analysis (EDA), feature selection, clustering using KMeans, and dimensionality reduction using PCA. The project also implements an XGBoost classifier to predict customer segments and evaluates its performance using cross-validation.

3. Customer Clustering and Credit Risk Classification Using Machine Learning

   • In this project, customers are clustered and loan applicants are classified into "Good" or "Bad" credit risk categories. The project leverages KMeans clustering for grouping customers and employs machine learning models like Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine for credit risk classification. The performance of the models is optimized through hyperparameter tuning using GridSearchCV.

Repository Structure

• Iris Flower Classification: Classifies iris flowers into three species based on their physical measurements using various machine learning models.
• Customer Segmentation for a Wholesale Distributor: Segments wholesale customers based on their spending patterns using clustering and classification techniques.
• Customer Clustering and Credit Risk Classification Using Machine Learning: Clusters customers and classifies loan applicants into credit risk categories using machine learning models.

Key Learnings

• Classification Techniques: Learn how to apply various classification algorithms to different datasets and evaluate their performance using accuracy, precision, recall, and F1-score.
• Clustering and Segmentation: Understand how clustering algorithms like KMeans can be used for customer segmentation and how dimensionality reduction techniques like PCA can aid in visualizing and interpreting the clusters.
• Model Optimization: Gain insights into hyperparameter tuning using GridSearchCV and cross-validation to optimize model performance.

Installation

To explore any of the projects, follow these steps:

1. Clone the desired project repository:

   git clone https://github.com/krishnapatel1722/Machine-Learning-Framework.git

2. Navigate to the project directory:

   cd {project-repo}

3. Run the Jupyter Notebook:

   jupyter notebook {project-notebook}.ipynb

Contribution

Contributions are welcome! If you have suggestions, bug fixes, or improvements, feel free to submit a pull request or open an issue.

License

This repository is licensed under the MIT License. See the LICENSE file for more details.