Kaggle competitions
Here’s the code and description for my Kaggle competitions I participated in so far
Competition 1: Natural Language Processing with Disaster
Competition Description
Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencies are interested in programmatically monitoring Twitter (i.e. disaster relief organizations and news agencies).
in this competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t. You’ll have access to a dataset of 10,000 tweets that were hand classified
GitHub Link
Kaggle Link
Competition 2: KC house data price and prediction
Competition Description
Online property companies offer valuations of houses using machine learning techniques. The aim of this report is to predict the house sales in King County, Washington State, USA using Multiple Linear Regression (MLR). The dataset consisted of historic data of houses sold between May 2014 to May 2015. We will predict the sales of houses in King County with an accuracy of at least 75-80% and understand which factors are responsible for higher property value - $650K and above.
GitHub Link
Kaggle Link
Competition 3: Kaggle California Housing Prices Analysis And prediction
Competition Description
This data was initially featured in the following paper: Pace, R. Kelley, and Ronald Barry. “Sparse spatial autoregressions.” Statistics & Probability Letters 33.3 (1997): 291-297. The data contains information from the 1990 California census. So although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning.