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Census Income Dataset Classification
Data Science Notebook on a Classification Task
Objective
In the Jupyter Notebook included in this page, we will using the Census Income Dataset to predict whether an individual's income exceeds $50K/yr based on census data.
The Dataset can be found here:
The Notebook can be found here:
Companion Mindmap/Cheatsheet
This Jupyter Notepad has a companion Mindmap/Cheatsheet that lists most of the Data Science steps that can be found at the following link:
Steps
In this Notebook, we'll perform:
- Feature Exploration (Uni and Bi-variate)
- Feature Imputation
- Feature Selection
- Feature Encoding
- Feature Ranking
- Machine Learning with sklearn and Tensorflow
- Random Search
- Accuracy, Precision, Recall, and f1 calculations
- ROC Curve
Setup
This Notebook has been designed to be run on top of the Jupyter Tensorflow Docker instance found in the link below:
If you haven't downloaded Docker at this point, please visit:
Then, open a shell or terminal session and copy/paste the following:
docker run -itd \
--restart always \
--name jupyter \
--hostname jupyter \
-p 8888:8888 \
-p 6006:6006 \
jupyter/tensorflow-notebook:latest \
start-notebook.sh --NotebookApp.token=''
Upon running the command, docker will automatically pull the images it needs and get the containers going for us.
Give it a minute or so for Jupyter to start, and head to the following URL: http://localhost:8888
You should now have Jupyter running. If after a minute you can't reach the URL, check that the containers are running correctly and the network has been created by typing:
### Check the containers are running
docker ps -a
Loading the Notebook
Download it from this link:
Go back to:
- http://localhost:8888, load your Notebook into Jupyter and run it. That's it!
Troubleshooting Docker
Here's a few useful commands in case something goes wrong with your docker instance:
# Restart Jupyter Docker Container
docker restart jupyter
# Stop Jupyter Docker Container
docker stop jupyter
# Remove Jupyter Docker Container
docker rm jupyter
Feature Exploration (Uni and Bi-variate) Feature Imputation Feature Selection Feature Encoding Feature Ranking Machine Learning Training Random Search Accuracy, Precision, Recall, and f1 calculations ROC Curve
Screenshots
Feature Distribution Analysis
Feature Cleaning
Missing Values is Features
Bivariate Exploration
Feature Correlation
Feature Importance
Feature PCA
Results from Machine Learning Algorithms
ROC for each Algorithm
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