Profile Introduction:

Howdy, I'm Somkietacode! 👋

I'm a passionate and dedicated developer exploring the wonders of the coding universe. My real name is Ouedraogo Somkieta, but you can call me Somkietacode. 😄

As a software enthusiast, my coding journey encompasses a wide range of interests and expertise. I love diving into front-end web development, crafting intuitive user interfaces with HTML, CSS, and JavaScript. I'm equally excited about back-end technologies, where I leverage the power of Python, Node.js, and databases to build robust and scalable applications.

In my interstellar quest for knowledge, I also dabble in mobile app development, experimenting with both Android and iOS platforms. Additionally, I find myself captivated by the potential of data science and machine learning, unraveling patterns and insights from datasets to make data-driven decisions.

With each line of code, I strive to create solutions that not only function flawlessly but also inspire and leave a positive impact on the world. Beyond coding, I'm a firm believer in open-source contributions and the collaborative nature of the developer community.

Come along, and let's embark on an exhilarating coding journey together! 🚀🌌

Feel free to explore my repositories and connect with me. Happy coding! 🚀🔥

P.S. You can also find me on https://m.facebook.com/somkieta.ouedraogo.102/ . Let's connect and learn from each other!

Profile Summary:

With 25 repositories and 5 stars, my work spans across various domains, but I have a particular fondness for deep learning and machine learning applications in the financial market. Let me introduce you to some of my most popular repositories:

1. Pandas Market Predictor:

   • A deep learning API written in Python, built on top of Panda, which facilitates predicting future prices and trends of financial market assets.
   • Developed with a focus on enabling fast experimentation, it empowers users to make data-driven decisions with ease.
   • Core data structures include Historical Market Data and Technical Indicator, and users can construct their dataset using the Pandas-TA library.
   • You can predict the most probable future market trend using the Trend_Detection method, and even estimate asset highs and lows using the Support_Resistance_Estimation_Tool.
   • Example usage provided to get you started on predicting trends from your dataset.

2. Artificial Neural Network:

   • This deep learning API, written in Python, is engineered to foster rapid experimentation in neural network applications.
   • With a focus on efficiency and effectiveness, it streamlines the development process for neural network enthusiasts.

3. ModelSaver:

   • As a dedicated machine learning practitioner, I created ModelSaver to provide a simple and convenient way to save and load machine learning models using pickle.
   • Designed especially for models trained using the Scikit-learn library (sklearn), it's also compatible with other models.

Among my repertoire, "Pandas_Market_Predictor" stands out as the most popular repository. You can visit my GitHub page to explore its full potential and dive into the exciting world of financial market prediction.

Feel free to collaborate, contribute, or reach out for any questions. Let's unlock the potential of code together! 🚀🌌