Metis Final Project: Predicting Kickstarter Projects Successes using Neural Networks
We just completed our final project at Metis in Seattle, Washington, USA. These past 12 weeks went by quickly and were filled with many lessons. It is going to take a while to process them and I am glad for the period of growth.
For this project, we were tasked to individually select a passion project and use our newly learned machine learning algorithm skills on them.
After much discussions with our very fine instructors, I decided to focus on predicting successes of Kickstarter data using neural networks and logistic regression.
Tools Used:
MongoDB for storing data
Python for coding
TensorFlow and Keras for neural networks
Tableau for data Visualization
Flask app for displaying the website
The code and data for this project can be found on Github.
Challenge:
“Why did Football Heroes, a Mobile game company using Kickstarter achieve its goal of 12,000 while CowBell Hero another company that used Kickstarter did not? The goal of this project is to help campaigns succeed as they raise their funds“
This is a Tough Problem
This is a tough problem because Kickstarter data contains both text data such as the project title and description. Tabular data such as the Goal and duration of the project. Hence my project needed to account for this problem.
Data:
The data was collected from Kickstarter web scraper called webroot.io.
Steps:
Preprocess data using MongoDB
Split data into text data (title, description ) and tabular data.
Pass the text data into an LSTM neural network and the tabular data into a regularized logistic regression.
Combine all the models together in an ensemble model. The accuracy score of my result was 78.3%
Build a website using Flask app to allow users interact with the output.
Steps for Kickstarter Prediction
Video Demo
Future Work:
Presently, I have a working website that predicts campaign successes and failures. Future work include improving the model names and websites.
Thank You:
I want to use this medium to thank my family, friends and colleagues at Metis for their lessons, patience and opportunity to grow. All of my successes at Metis is because of the positive impact they had on me and my projects. So Thank you.
PowerPoint Slides: