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Data Science Notebook on a Classification Task, using sklearn and Tensorflow.
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README.md

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:

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

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Feature Cleaning

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Missing Values is Features

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Bivariate Exploration

alt text alt text

Feature Correlation

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Feature Importance

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Feature PCA

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Results from Machine Learning Algorithms

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ROC for each Algorithm

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