# Implementing K Nearest Neighbors In Python

2018-07-30

In order to make any predictions, you have to calculate the distance between the new point and the existing points, as you will be needing k closest points.

In this case for calculating the distance, we will use the Euclidean distance. This is defined as the square root of the sum of the squared differences between the two arrays of numbers

Specifically, we need only first 4 attributes(features) for distance calculation as the last attribute is a class label. So for one of the approach is to limit the Euclidean distance to a fixed length, thereby ignoring the final dimension.

Check it out.

## A Primer on Survey Analysis

2019-09-23

Federico Pascual has a long primer on survey analysis: When it comes to customer feedback, you’ll find that not all the information you get is useful to your company. This feedback can be categorized into non-insightful and insightful data. The former refers to data you had already spotted as problematic, while insightful information either helps […]

## Linear Regression in Power BI

2019-09-23

Joseph Yeates shows how to implement linear regression in Power BI: The goal of a simple linear model is to fit a line onto this plot to summarize the shape of the data using the equation above. The “a” value is the slope of the fitted line (rise over run) and the “b” value is […]

July 2018
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