Machine Learning Projects Repository 🚀

This GitHub repository contains a collection of machine-learning projects that demonstrate various techniques and algorithms for solving different real-world problems. Each project is designed to showcase a specific aspect of machine learning, from clustering and classification to incorporating text-to-speech functionality using the pyttsx3 library. 🎤

Project List 🗂️

1. Customer Segmentation using K-Means Clustering 📊
   Description: This project explores customer segmentation using the K-Means clustering algorithm. Customer data is grouped into clusters based on similarities in their behavior, allowing for more targeted marketing strategies. 🎯
   Files:

   • customer_segmentation.ipynb: Jupyter Notebook with code and analysis. 📓
   • customer_data.csv: Sample customer data for analysis. 📂

2. Heart Disease Prediction using Logistic Regression with Text-to-Speech ❤️
   Description: In this project, we build a heart disease prediction model using logistic regression. Additionally, we've incorporated a text-to-speech feature using the pyttsx3 library to provide voice-based results and recommendations. 🔊
   Files:

   • heart_disease_prediction.ipynb: Jupyter Notebook with code, analysis, and voice integration. 📘
   • heart_disease_data.csv: Heart disease dataset for training and testing. 📈

3. Talking Diabetes Prediction using a Support Vector Machine (SVM) with Text-to-Speech 🍭
   Description: This project focuses on diabetes prediction using the Support Vector Machine (SVM) algorithm. Similar to the heart disease project, we've added text-to-speech capabilities using the pyttsx3 library to make the results accessible via voice. 🗣️
   Files:

   • diabetes_prediction.ipynb: Jupyter Notebook with code, analysis, and voice integration. 📗
   • diabetes_data.csv: Diabetes dataset for training and testing. 📊

4. Wine Quality Prediction using Random Forest 🍷
   Description: This project tackles wine quality prediction using the Random Forest algorithm. It demonstrates a regression task, predicting wine quality scores based on various attributes. 📏
   Files:

   • wine_quality_prediction.ipynb: Jupyter Notebook with code and analysis. 📖
   • wine_quality_data.csv: Wine quality dataset for training and testing. 📊

5. Spam Mail Prediction using Logistic Regression 📧
   Description: In this project, we create a spam mail prediction model using logistic regression. The goal is to classify emails as either spam or not spam based on their content and features. 🚫
   Files:

   • spam_mail_prediction.ipynb: Jupyter Notebook with code and analysis. 📘
   • spam_mail_data.csv: Dataset containing email features for training and testing. 📨

Getting Started 🚀

1. Clone this repository to your local machine using the following command:

   git clone https://github.com/Chhavimohitkar65/machine-learning-projects.git
   

• Navigate to the project folder of your choice by using cd . 🗂️
• Refer to the respective project's README or Jupyter Notebook for detailed instructions on running the code and understanding the analysis. 📋

Dependencies 📦

Make sure you have the following dependencies installed to run the projects:

• Python 3.x 🐍
• Jupyter Notebook 📓
• NumPy ➕
• pandas 📊
• scikit-learn 🏷️
• matplotlib 📈
• seaborn 🌈
• pyttsx3 (for text-to-speech functionality, only required for specific projects) 🔊

Contributing 🤝

If you would like to contribute to this repository, please follow these guidelines:

Fork the repository. 🍴

• Create a new branch for your feature or bug fix: git checkout -b feature-name. 🌿
• Make your changes and commit them with descriptive messages. 📝
• Push your changes to your fork: git push origin feature-name. ⬆️
• Create a pull request to the main repository. 🔄

License 📜

• This repository is licensed under the MIT License. See the LICENSE file for details. ⚖️
• Feel free to explore, learn, and contribute to these machine-learning projects. Happy coding! 💻