Search for a command to run...
Random Forest and XGBoost classification prediction python model. Predicting which passengers will be transported to an alternate dimension.
I am an Industrial Engineer utilizing the power of python to gain deeper insights in data.
I am currently learning Deep learning with TensorFlow
No comments yet. Be the first to comment.
I recently built an interactive A3 process improvement app using Claude Sonnet 4 - and here's the interesting part: the app itself uses Claude's API to analyze completed A3 documents. It's essentially Claude helping to build a tool that leverages Cla...
Comparing Mistral 7B vs. LLaMA-2 7B using HuggingFace
Implementing a RAG model
This took a bit of time to get the certificate, but circling back to follow up on a previous accomplishment — I officially completed an AI/ML Apprenticeship! [U.S. Department of Labor apprenticeship completion certificate for the AI/ML Fundamentals P...
A time-series forecasting and anomaly-detection tool
Using the Kaggle Spaceship Titanic dataset, I created a machine learning model in python to predict which passengers in the data would be transported to an alternate dimension.
Check out the Kaggle notebook
The first step was loading and performing some data exploration
A pair plot is a helpful visual to spot correlations.
Here is a visual of the age of those transported
During the data exploration I used the get_dummies function on the 'HomePlanet' and 'Destination' columns. I also performed other methods to clean and transform the data.
df_data_final = pd.get_dummies(df_data_final, columns=['HomePlanet', 'Destination'], drop_first=False)
I created a tableau dashboard to help visualize the data. This wasn't necessary but helped me see and interact with the data.
The target variable is the 'Transported' column, the rest of the columns are the features.
# Define the y (target) variable.
y = df_data_final['Transported']
# Define the X (predictor) variables.
X = df_data_final.drop(['Transported'], axis = 1)
Random Forest Model Accuracy: 0.781048758049678
XGBoost Model Accuracy: 0.7828886844526219
The XGBoost model performed slightly better on the training data.
If you are interested, the Kaggle notebook can be found here