Recent Projects

GitHub Repo gpt.py Once you hook up an OpenAI model (using paid credits or using a free API with limits), you can chat with it inside the terminal, perfect if you hate the buggy web versions and want to integrate as much as you can into the terminal. exe.py The perfect tool for executing commands from English to Bash. If you’re new to the terminal and have enough savy to getting the venv set up but not any other commands, then this program is for you. Using a wrapper, give exe.py the problem you want to solve in your terminal and generate a solution with an OpenAI model. There is a confirmation in any command that’s generated, so don’t worry about rm -rfing your directory.

Credit Card Fraud Detection Project - COMP 379/479 Machine Learning Ololade Akinsanola, Oliver Schramm, Eric Spencer, Tigist Tefera, and Avery Walker# Data Download [#1] import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split import os from imblearn.over_sampling import SMOTE from collections import Counter import xgboost [#2] # 1. Install Kaggle CLI !pip install kaggle # 2. Write your credentials to ~/.kaggle/kaggle.json import os, json # Ensure the directory exists os.makedirs(os.path.expanduser("~/.kaggle"), exist_ok=True) creds = { "username": "ericspencer00", "key": "xxxxxxxxxxxxxxxxx" } # Write and secure the file with open(os.path.expanduser("~/.kaggle/kaggle.json"), "w") as f: json.dump(creds, f) os.chmod(os.path.expanduser("~/.kaggle/kaggle.json"), 0o600) # 3. Point Kaggle CLI at that folder os.environ["KAGGLE_CONFIG_DIR"] = os.path.expanduser("~/.kaggle") # 4. Download & unzip the dataset !kaggle datasets download -d mlg-ulb/creditcardfraud -p . --unzip # 5. Load into pandas import pandas as pd data = pd.read_csv("creditcard.csv") print("Data downloaded successfully.") print(data.head()) Output: