kwasganguly / knn-to-predict-nba-scores Goto Github PK

1. Introduction This assignment will help you to consolidate the concepts learnt in the session.

2. Problem Statement

In this assignment, students will be using the K-nearest neighbors algorithm to predict how many points NBA players scored in the 2013-2014 season. A look at the data Before we dive into the algorithm, let’s take a look at our data. Each row in the data contains information on how a player performed in the 2013-2014 NBA season. Download 'nba_2013.csv' file from this link: https://www.dropbox.com/s/b3nv38jjo5dxcl6/nba_2013.csv?dl=0 Here are some selected columns from the data: player - name of the player pos - the position of the player g - number of games the player was in gs - number of games the player started pts - total points the player scored There are many more columns in the data, mostly containing information about average player game performance over the course of the season. See this site for an explanation of the rest of them. We can read our dataset in and figure out which columns are present: import pandas

with open("nba_2013.csv", 'r') as csvfile: nba = pandas.read_csv(csvfile)

NOTE: The solution shared through Github should contain the source code used and the screenshot of the output.

3. Output

N/A