Skewed data is common in data science; skew is the degree of distortion from a normal distribution. For example, below is a plot of the house prices from Kaggle's House Price Competition that is right skewed, meaning there are a minority of very large values. Why do we care if the data is skewed? If the response variable is skewed like in Kaggle's House Prices Competition, the model…

This course is intended to show the student advanced regression techniques that he can use to improve his score on Kaggle's House Price ... This course is intended to show the student advanced regression techniques that he can use to improve his score on Kaggle's House Price competition. He will be given an overview of Python's machine learning library, sklearn, which contains many machine learning functions. In addition, he will learn the code for several of those functions, which will help him to get closer to the machine. The student will study several different regressors, which will be needed to make accurate and low error predictions on the dataset. The student will learn several different techniques to impute null values in the dataframe, which he will need to be able to do to make predictions on it later.

Skewed data is common in data science; skew is the degree of distortion from a normal distribution. For example, below is a plot of the house prices from Kaggle's House Price Competition that is right skewed, meaning there are a minority of very large values. Why do we care if the data is skewed? If the response variable is skewed like in Kaggle's House Prices Competition, the model will be trained on a much larger number of moderately priced homes, and will be less likely to successfully predict the price for the most expensive houses. The concept is the same as training a model on imbalanced categorical classes.

Founded in 2010, Kaggle is a Data Science platform where users can share, collaborate, and compete. One key feature of Kaggle is "Competitions", which offers users the ability to practice on real world data and to test their skills with, and against, an international community. This guide will teach you how to approach and enter a Kaggle competition, including exploring the data, creating and engineering features, building models, and submitting predictions. We'll follow these steps to a successful Kaggle Competition submission: We need to acquire the data for the competition. The descriptions of the features and some other helpful information are contained in a file with an obvious name, data_description.txt. Download the data and save it into a folder where you'll keep everything you need for the competition. We will first look at the train.csv After we've trained a model, we'll make predictions using the test.csv