Machine Learning
Python Libraries
-
Data Wrangling & Statistics: Pandas, NumPy, Pathlib, Hvplot, & SciPy
-
Data Visualization: Matplotlib, MATLAB, Plotly, & MapReduce,
-
Machine Learning: Tidy verse, Scikit-learn, Imblearn, Sklearn. TensorFlow, and Sklearn - KMeans
Latest Projects
Click on Icon to view ML Results
Click on Icon to view Analysis
SAMPLE 1: Supervised Machine Learning to Assess Credit Risk (Lending Club)
Credit risk is an inherently unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. To obtain the best machine learning algorithm that would assess high credit risk, we ran the following models:
-
Logistics Regression Model
-
Balanced Random Forest model
-
Easy Ensemble Model
Click on Icon to view Analysis
SAMPLE 2: Unsupervised Machine Learning - Cryptocurrency
Utilize Unsupervised machine learning to determine cryptocurrencies trading on the market and how cryptocurrencies could be grouped toward creating a classification for developing this new investment product.
Principal Component analysis (PCA), a statistical technique, was used to speed up machine learning algorithms when the number of input features (or dimensions) is too high. The analysis was done utilizing K-Means approach. Elbow curve showed us 4 cluster as a classification for developing the investment product.
Click on Icon to view Analysis
SAMPLE 3: Neural network
Develop Neural Network and Deep learning Models - Utilizing Python, TensorFlow Playground and Machine learning Algorithms.
Testimonials
“coming Soon.”
Samantha Jones, Project Manager
"Coming Soon"
Samantha Jones, Project Manager