Deep Learning Nanodegree

Deep Learning

Project: Predicting Bike Sharing Patterns

Project for Udacity's Deep Learning Nanodegree program. In this project, I developed code for a deep learning model from scratch, using only matrix manipulation with the NumPy library.

Install

This project requires Python 3.x and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• sys

You will also need to have software installed to run and execute an iPython Notebook

I recommend installion Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

Code

Template code is provided in the Your_first_neural_network.ipynb notebook file and in the my_answers.py file.

Run

In a terminal or command window, navigate to the top-level project directory Predicting-Bike-Sharing-Patterns/ (that contains this README) and run one of the following commands:

ipython notebook Your_first_neural_network.ipynb

or

jupyter notebook Your_first_neural_network.ipynb

This will open the iPython Notebook software in your browser.

Data

The data for this project is present within the Bike-Sharing-Dataset folder.

Number of Cases: The dataset contains a total of 17379 cases.

Features

• instant: record index
• dteday : date
• season : season (1:winter, 2:spring, 3:summer, 4:fall)
• yr : year (0: 2011, 1:2012)
• mnth : month ( 1 to 12)
• hr : hour (0 to 23)
• holiday : weather day is holiday or not (extracted from [Web Link])
• weekday : day of the week
• workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
• weathersit :-
  • 1: Clear, Few clouds, Partly cloudy, Partly cloudy
  • 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
  • 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
  • 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
• temp : Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)
• atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale)
• hum: Normalized humidity. The values are divided to 100 (max)
• windspeed: Normalized wind speed. The values are divided to 67 (max)
• casual: count of casual users
• registered: count of registered users
• cnt: count of total rental bikes including both casual and registered

Certification