Golf Project

Table of Contents

1. Description of the Data
2. Data Cleaning
3. Exploratory Data Analysis
4. Machine Learning Model (Classification)
5. Machine Learning Model (Regression)
6. Conclusion

1. Description of the Data

Back to table of Contents Lets import the Libraries we need and the data (csv) file!

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv("golfdata.csv")

# Return first 5 rows
df.head()

Output:

	Player Name	Rounds	Fairway Percentage	Year	Avg Distance	gir	Average Putts	Average Scrambling	Average Score	Points	Wins	Top 10	Average SG Putts	Average SG Total	SG:OTT	SG:APR	SG:ARG	Money
0	Henrik Stenson	60.0	75.19	2018	291.5	73.51	29.93	60.67	69.617	868	NaN	5.0	-0.207	1.153	0.427	0.960	-0.027	$2,680,487
1	Ryan Armour	109.0	73.58	2018	283.5	68.22	29.31	60.13	70.758	1,006	1.0	3.0	-0.058	0.337	-0.012	0.213	0.194	$2,485,203
2	Chez Reavie	93.0	72.24	2018	286.5	68.67	29.12	62.27	70.432	1,020	NaN	3.0	0.192	0.674	0.183	0.437	-0.137	$2,700,018
3	Ryan Moore	78.0	71.94	2018	289.2	68.80	29.17	64.16	70.015	795	NaN	5.0	-0.271	0.941	0.406	0.532	0.273	$1,986,608
4	Brian Stuard	103.0	71.44	2018	278.9	67.12	29.11	59.23	71.038	421	NaN	3.0	0.164	0.062	-0.227	0.099	0.026	$1,089,763

OK, now what?

Lets Clean the data

• First we can look at the data and see what needs to be cleaned

1. Description of the Data

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2. Data Cleaning

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3. Exploratory Data Analysis

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4. Machine Learning Model (Classification)

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5. Machine Learning Model (Regression)

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6. Conclusion

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