🔭 I’m currently studying on Artificial Intelligence, Machine Learning and Biology
👯 I’m looking to collaborate on any Data Science and Large Language Model projects
🤝 I’m looking for help to work with Cloud Computing, Artificial Intelligence, Machine Learning, and Blockchain Development
🤝 I would love to level-up my knowledge in BioInformatics, Cyber Security, Quantum Computing, Robotic Process Automation
🌱 I’m currently learning more about Rust, Java and other Blockchain EVM
💬 Ask me about Artificial Intelligence and Machine Learning
🌐 Kindly visit my other GitHub profile for more content related to Full-stack development and blockchain development
📫 How to reach me flexyzephyrus@gmail.com
⚡Fun fact : I'm good at learning new things and adapting easily
⚡Fun fact : I always read documentation everyday before I begin to code
⚡Fun fact : I love Final Fantasy, Science Fiction, Biology, Architecture, Mutants and Galaxy Adventure
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React |
Python |
JavaScript |
C++ |
Webpack |
MySQL |
TypeScript |
AWS |
C# |
Django |
Github |
Git |
Laravel |
HTML5 |
CSS |
Bootstrap |
Tailwind |
jQuery |
|
MongoDB |
Nodejs |
PHP |
VsCode |
WordPress |
Vue |
Sass |
GraphQL |
PostgreSQL |
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- ♻️ - refactor getGithubUserContribution
- ⬆️ - bump dependencies
- ✨ - read contribution calendar from github api or remove some attributes
- 📓 - commit or add readme
- 👷 - add manual run, repair
- 🚑 - import or also commit
- 🔨 - fix algorithm priority
- 🚀 - add emojis and style
- 🤫 - smiley face can also use for indicator for running or stopping some container
- ⛓️ - for linking file or repo
- 💱 - using solidity, hardhat or crypto related function
- 🧊 - blockchain
- 🌐 - networking setting, YAML file
- 📋 - List of Content
🖨️ Technologies Icons :
| Flexycode | Flexyledger |
|---|---|
| ➡️ 🚍 Communication | ➡️ 🧮 Fortran |
| ➡️ 🧰 Version Control | ➡️ ☎️ Erlang/Elixir |
| ➡️ 🔨 Tools | ➡️ 🧪 Testing |
| ➡️ 🌐 Web Dev | ➡️ 📱 Mobile Dev |
| ➡️ 📜 JavaScript | ➡️ ✨ UI/UX |
| ➡️ ☕ Java | ➡️ 🧊 Apache |
| ➡️ ©️ C/C++ | ➡️ 🎮 Game Development |
| ➡️ 🪒 C# | ➡️ 🔬 Analytics |
| ➡️ 🐍 Python | ➡️ 🤖 AI |
| ➡️ 🐘 PHP | ➡️ 💾 Database |
| ➡️ 💎 Ruby | ➡️ ☁️ Cloud |
| ➡️ 🦾 Rust | ➡️ 🖥️ Operating system |
| ➡️ 🐿️ Go | ➡️ 🤿 DevOps |
| ➡️ 🍼 How to use this icons? | ➡️ 🚶 Contribution |
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# Import the necessary libraries for AI
import numpy as np
import pandas as pd
import tensorflow as tf
# Define the AI model architecture
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64, activation='relu', input_dim=10))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
# Compile and train the AI model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=32)
# Use the AI model for predictions
predictions = model.predict(X_test)
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# Import the necessary libraries for ML
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# Load the dataset
data = pd.read_csv('data.csv')
X = data.drop('target', axis=1)
y = data['target']
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)
# Make predictions on the test set
predictions = model.predict(X_test)
# Calculate the accuracy of the model
accuracy = accuracy_score(y_test, predictions)
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