Hi there
, I'm Jay
🔭 I’m currently studying on Artificial Intelligence,Machine Learning, Quantum Technology and Biology
👯 I’m looking to collaborate on any Data Science, LLM and Web3 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
🎮 I'm a Dallas Mavericks fan since 2011, guess my idol 🤫
🌐 Kindly visit my other GitHub profile: flexyledger for more content related to blockchain development
📫 How to reach me flexycode.dev@gmail.com, flexycode@protonmail.com, flexyledger@gmail.com
⚡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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| Icons |
|---|
| ♻️ - refactor getGithubUserContribution |
| ✨ - 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 :
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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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