# Udacity Nanodegree: Data Science 

For term 1, it containes 3 parts: Supervised, Deep, and Unsupervised Learning

The **Exercises** are included in each folder.

The **Projects** are also included in their own folders:

1. [Supervised Learning: finding charity donors, extracted feature importance, using data collected from the 1994 U.S. Census (Naive Bayes, Random Forest, and SVM) ](https://github.com/SophieGarden/DataScience_NanoDegree/tree/master/Project1_finding_donors)



2. [Deep Learning: Flower Species Image Classifier: Designed a flower classifier with PyTorch (ResNet, VGG, CUDA, Command Line App); Achieved 90\% accuracy, performed sanity check with visualization (Matplotlib)) ](https://github.com/SophieGarden/DataScience_NanoDegree/tree/master/Project2_flower_species_image_classifier)

3. [Unsupervised Learning: Customer Identification for Mail-Order Sales Company: Identified core customers by interpreting the differences between the clusters for the general population and that of customers (PCA, K-Means); Built pipeline includes data cleaning, feature engineering, modeling, clustering ](https://github.com/SophieGarden/DataScience_NanoDegree/tree/master/Project3_identify_groups)