This repository is dedicated to my machine learning journey with Python. It contains a collection of code files and projects related to various machine learning algorithms and techniques.
- Introduction
- Usage
- Dependencies
- Projects
- Contributing
- License
In this repository, I will be uploading my code files and projects as I explore different machine learning algorithms and techniques using Python. Each code file or project focuses on a specific concept or application within the field of machine learning.
Feel free to explore the code and projects to gain insights into machine learning algorithms, data preprocessing techniques, model evaluation methods, and more. You can use the code as a reference, modify it, or build upon it for your own machine learning projects.
To use the code in this repository, simply clone or download the repository to your local machine. Each code file is self-contained and can be executed independently. Make sure you have the necessary dependencies installed (mentioned in the next section) before running the code.
The code in this repository depends on the following Python libraries:
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
Make sure you have these libraries installed in your Python environment before running the code. You can install them using pip:
pip install numpy pandas scikit-learn matplotlib
This project focuses on analyzing breast cancer data using the K-Nearest Neighbors (KNN) algorithm. The goal is to classify breast cancer samples as either malignant or benign based on various features.
The project includes the following files:
-
generate.py: This file generates random samples of breast cancer data and saves them to a CSV file for analysis. -
breast_cancer_analysis.py: This file loads the breast cancer dataset, splits it into training and testing sets, applies the KNN algorithm for classification, and evaluates the model's performance. It saves the predicted class names to a CSV file. -
random_samples.csv: This CSV file contains randomly generated breast cancer samples. -
store.csv: This CSV file stores the predicted class names from the analysis.
This project focuses on predicting the presence or absence of diabetes using a Naive Bayes classifier. The dataset used contains features related to glucose and blood pressure. The goal is to classify individuals as diabetic or non-diabetic based on these features.
The project includes the following files:
-
dbts_NB.py: This file contains the code to load the diabetes dataset, split it into training and testing sets, create a Naive Bayes classifier, train the classifier, make predictions on the test set, and evaluate the model's accuracy. It also visualizes the data using a scatter plot. -
Naive-Bayes-Classification-Data.csv: This CSV file contains the diabetes dataset with the following columns: glucose, bloodpressure, and diabetes. -
NB_Readme.md: This file provides an overview of the project, its purpose, and instructions on how to use the code.
This project involves the classification of Iris flowers using the Support Vector Machine (SVM) algorithm and feature dimensionality reduction with Principal Component Analysis (PCA). The Iris dataset is loaded, split into training and testing sets, and an SVM classifier is trained on the features. PCA is then applied to reduce the feature dimensionality to 2 columns for visualization purposes. The project aims to accurately classify Iris flowers based on their features using SVM and explore the data in a 2D scatter plot.
Feel free to explore the project and its files for more details.
Contributions to this repository are welcome. If you have any suggestions, improvements, or additional code files related to machine learning, feel free to open a pull request. Please make sure to follow the repository's code style and guidelines.
This repository is licensed under the MIT License. Feel free to use the code provided in this repository for your own learning purposes or projects.