Merge branch 'main' of https://github.com/PEC-CSS/slowmokit into decision_tree

Author: Sar-thak-3

README.md

@@ -38,6 +38,12 @@ cmake --build build
 cmake --install build --prefix '<ADD PATH OF MINGW INCLUDE HERE (mingw64/include)>'
 ```
 
+> If permission is required to execute, run the following command
+
+```
+chmod 705 ./install
+```
+
 ### Steps
 
 Following are the steps to start contributing to our beautiful library:
@@ -57,6 +63,7 @@ More details [here](./create_model/README.md)
 4. Make the model directory you want to implement in the `src/slowmokit/methods/<model type>/<your_model>`.
     - `<model type>` is basically the class of your model, for example `linear_model` or `cluster`.
 5. Create two files:
+
     - `<model>.hpp` : the header file for the model, create the class of model here and include all the stuff required
       for the model here.
         - Include `core.hpp` for all the basic functionalities already added.

_config.yml

@@ -0,0 +1,8 @@
+title: Slowmokit
+description: ML library in C++.
+remote_theme: pages-themes/hacker@v0.2.0
+plugins:
+    - jekyll-remote-theme
+gem "github-pages", group: :jekyll_plugins
+show_downloads: false
+google_analytics:

index.md

@@ -0,0 +1,35 @@
+---
+layout: default
+title: "Slowmokit"
+permalink: /
+---
+
+# Welcome to our C++ Machine Learning Kit!
+
+Our kit is an open source library that provides a wide range of machine learning features for developers. With a focus on performance and scalability, our kit can handle both small and large datasets with ease. Our kit can be easily installed on macOS, Linux, and Windows platforms, making it a versatile choice for developers.
+
+## Features:
+
+-   **High Performance**: Our kit is designed for high performance, enabling fast processing and training of machine learning models.
+-   **Scalable**: Our kit can handle both small and large datasets with ease, providing developers with a flexible solution.
+    Versatile: Our kit is compatible with a wide range of platforms, including macOS, Linux, and Windows.
+-   **Extensive Library**: Our kit offers an extensive library of algorithms and tools for machine learning, including regression, classification, clustering, and more.
+-   **Easy to Use**: Our kit is user-friendly and easy to implement, with proper documentation and examples included on GitHub repository.
+
+## Installation:
+
+Our kit can be installed on macOS, Linux, and Windows platforms. Detailed installation instructions are available on our GitHub repository.
+
+## Documentation:
+
+We understand the importance of proper documentation, which is why we provide comprehensive documentation for our kit. Our documentation covers everything from installation to implementation and includes detailed examples and use cases.
+
+## GitHub Repository:
+
+Our GitHub repository includes all the necessary resources for developers, including installation instructions, documentation, and examples.
+
+Link : [slowmokit (github)](https://github.com/PEC-CSS/slowmokit/)
+
+## Conclusion:
+
+Our C++ Machine Learning Kit is a powerful, versatile, and user-friendly tool for developers who need to build machine learning models. With a focus on performance and scalability, our kit provides a flexible and reliable solution for both small and large datasets. Try it out today and see for yourself how our kit can help you build better machine learning models!