Python Projects
![fifa 20223 world cup logo](https://cdn.prod.website-files.com/611fa7b4e1dc2b2e7c0eca79/645e743a93703af079f86972_fifia%20world%20cup%20logo.jpg)
FIFA 2022 World Cup Predictor - Visit
Background:
For a big data class in my curriculum in university, we needed to create a capstone project utilizing machine learning and statistical analyzes in a real world project. As a fan of soccer (football) I worked as the team lead to create a world cup predictor.
Process:
This was a challenging project. However, it was made easy with a well formatted dataset. Once we acquired quality data, we then followed the same procedure in most ML applications data exploration, preprocessing (i.e dimensionality reduction), and finally, model training. We used a MultiOutputRegressor to predict score, then used the predictions to perform the tournament logic.
![logo for a gift for teaching of osceola county](https://cdn.prod.website-files.com/611fa7b4e1dc2b2e7c0eca79/645e6cabe2b93ba13bc94f47_deep-learning.png)
Reinforcement Learning - Visit
Background:
In this project, we will use reinforcement learning to train an AI-based explorer to play a game.
Process:
The game involves an explorer (represented by the letter "o") trying to find a treasure (represented by the letter "T"), which is located on the far right. The explorer will learn how to move left and right to get the treasure, and will receive a reward when it does so successfully. After several epochs of training, the explorer will learn how to get the treasure faster and will eventually move directly to the right to reach it.
![logo for a gift for teaching of osceola county](https://cdn.prod.website-files.com/611fa7b4e1dc2b2e7c0eca79/645e6cabe2b93ba13bc94f47_deep-learning.png)
Recurrent Neural Network - Visit
Background:
In this assignment, we were tasked with implementing both a basic RNN network and an LSTM network using Keras to solve two problems. The first problem involved using a basic RNN network to predict time series data, while the second problem involved building an LSTM model to conduct sentiment analysis.
Process:
Learning to use RNN and LSTM to solve problems can provide several key takeaways. Firstly, it highlights the importance of leveraging sequential data and understanding how it differs from other types of data. Secondly, it emphasizes the significance of choosing the right architecture and parameters for the problem at hand. Thirdly, it demonstrates the effectiveness of using recurrent networks for tasks such as prediction and classification. Finally, it underscores the need to carefully evaluate models and consider factors such as overfitting and data quality.
![logo for a gift for teaching of osceola county](https://cdn.prod.website-files.com/611fa7b4e1dc2b2e7c0eca79/645e6cabe2b93ba13bc94f47_deep-learning.png)
Convolutional Neural Network - Visit
Background:
In this assignment, we explored some fundamental image processing techniques, including convolutional filtering and max pooling. We then implemented LeNet-5, which is one of the earliest representative convolutional neural networks. Finally, we evaluated the model's performance and analyzed the results.
Process:
Through this project, I learned how to preprocess and transform images to extract meaningful features and train a neural network for image classification tasks.
![logo for a gift for teaching of osceola county](https://cdn.prod.website-files.com/611fa7b4e1dc2b2e7c0eca79/645e6cabe2b93ba13bc94f47_deep-learning.png)
Logistic Regression - Visit
Background:
As part of my machine learning curriculum, I completed a project on logistic regression which involved implementing the sigmoid function, cost function, gradient, and conducting training and evaluation.
Process:
This project provided an opportunity to apply fundamental concepts of machine learning and gain practical experience with data analysis, model training, and evaluation techniques.
![logo for a gift for teaching of osceola county](https://cdn.prod.website-files.com/611fa7b4e1dc2b2e7c0eca79/645e6cabe2b93ba13bc94f47_deep-learning.png)
Linear Regression - Visit
Background:
As a requirement of my machine learning curriculum, I was tasked with implementing Signal Variable Nonlinear Regression and Multiple Variable Linear Regression using gradient descent.
Process:
This project provided a hands-on opportunity to apply theoretical concepts and gain practical experience with gradient descent optimization algorithms.
![logo for a gift for teaching of osceola county](https://cdn.prod.website-files.com/611fa7b4e1dc2b2e7c0eca79/6197b5958326044d248dcaa3_agft%20logo.png)
AGFT Volunteer Portal - Visit
Background:
Foundation Osceola has a program called A Gift For Teaching that provides teacher supplies for Osceola County teachers. Student volunteers are heavily relied upon to aid in program functions.
Process:
I built this volunteer portal as a way to automate the recording of their volunteer hours. It is a full stack application using a Django backend.
![programming language python logo](https://cdn.prod.website-files.com/611fa7b4e1dc2b2e7c0eca79/615f815b69fc321963a200ac_icons8-python-128.png)
Py Blackjack - Visit
Background:
A console based Blackjack game using python.
Process:
This project served as a way for me to used object oriented programming in game development. It was a fun challenge as the game implementation relies heavilily on logic validating.
![Python Icon](https://cdn.prod.website-files.com/611fa7b4e1dc2b2e7c0eca79/615f815b69fc321963a200ac_icons8-python-128.png)
Stock App - Visit
Background:
I was beginning to learn about the stock market and integrate that with my learning of python.
Process:
The app creates bollinger bands on a particular ticker within a selected date range. I thought it would be useful to see how a particular stock has performed over the years.