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--- | |
title: "Project3" | |
author: "Jurgen de Jager" | |
date: "August 12, 2016" | |
output: html_document | |
--- | |
```{r, echo = FALSE} | |
#loading packages | |
library(randomForest) | |
library(ggplot2) | |
library(rpart) | |
library(rpart.plot) | |
library(rattle) | |
library(party) | |
library(RColorBrewer) | |
library(MASS) | |
library(GGally) | |
library(dplyr) | |
library(d3heatmap) | |
library(doBy) | |
library(stargazer) | |
``` | |
```{r, echo = FALSE} | |
#data prep | |
nba = read.csv("nba.csv") | |
nba$Position = as.character(nba$Position) | |
which(nchar(nba$Position)>3) | |
nba = nba[1:342,] | |
nba = nba[-c(159,260,324,327),] | |
nba$random = runif(338, 0, 1) | |
nba.train = nba[nba$random < .4,] | |
nba.test = nba[nba$random >= .4,] | |
``` | |
# VIZ | |
```{r} | |
nba.temp = nba[,c('Position', 'TRB', 'AST', 'STL', 'BLK', 'PER', 'TOV', 'USG', 'TS')] | |
ggpairs(nba.temp, aes(col = Position)) | |
#points | |
ggplot(nba, aes(x = PER)) + geom_density(aes(fill = Position, alpha = 0.2)) | |
#true shootings score | |
ggplot(nba, aes(x = TS)) + geom_density(aes(fill = Position, alpha = 0.2)) | |
#Offensive Rebounds | |
ggplot(nba, aes(x = TRB)) + geom_density(aes(fill = Position, alpha = 0.2)) | |
#Defensive Rebounds | |
ggplot(nba, aes(x = DRB)) + geom_density(aes(fill = Position, alpha = 0.2)) | |
#Steals | |
ggplot(nba, aes(x = STL)) + geom_density(aes(fill = Position, alpha = 0.2)) | |
#heatmap by position | |
viz.data = nba[,c(1:2,6:21)] | |
vd = viz.data %>% | |
group_by(Position) %>% | |
summarise_each(funs(mean)) | |
name = vd$Position | |
vd = vd[,2:17] | |
row.names(vd) = name | |
d3heatmap(vd, scale="column", dendrogram = "none") | |
## heatmap of every player | |
nba3 = nba[,c('Player', 'TRB', 'AST', 'STL', 'BLK', 'PER', 'TOV', 'USG', 'TS')] | |
name2 = nba3$Player | |
row.names(nba3) = name2 | |
d3heatmap(nba3[,c(2:9)], scale="column", yaxis_font_size = "4pt", k_row = 5,dendrogram = "row") | |
``` | |
#Decision Tree - Good Fit | |
```{r} | |
#model2 | |
fit = rpart(Position ~., data = nba.train[,c(2,6:21)], method="class") | |
#fancyRpartPlot(fit) | |
#prediction | |
nba.test$Prediction <- predict(fit, nba.test, type = "class") | |
#tabling results | |
table(nba.test$Position, nba.test$Prediction) | |
prop.table(table(nba.test$Position,nba.test$Prediction),1) | |
``` | |
#Decision Tree - Over Fit | |
```{r} | |
#model2 | |
fit = rpart(Position ~., data = nba.train[,c(2,6:21)], method="class", control=rpart.control(minsplit=3, cp=0.001)) | |
plot(fit$variable.importance) | |
#fancyRpartPlot(fit) | |
#prediction | |
nba.test$Prediction <- predict(fit, nba.test, type = "class") | |
#tabling results | |
table(nba.test$Position, nba.test$Prediction) | |
prop.table(table(nba.test$Position,nba.test$Prediction),1) | |
``` | |
#RF | |
```{r} | |
#model 1 | |
rf.model = randomForest(as.factor(Position) ~ . , data= nba.train[,c(2,6:21)], ntree= 500, mtry = round(sqrt(ncol(nba)))) | |
mean(rf.model$err.rate) | |
plot(rf.model) | |
#predicting using test set | |
nba.test$pred.pos.rf = predict(rf.model, nba.test, type="response") | |
#tabling resutls | |
table(nba.test$Position,nba.test$pred.pos.rf) | |
#proportion table | |
prop.table(table(nba.test$Position,nba.test$pred.pos.rf),1) | |
layout(matrix(c(1,2),nrow=1), | |
width=c(4,1)) | |
par(mar=c(5,4,4,0)) #No margin on the right side | |
plot(rf.model) | |
par(mar=c(5,0,4,2)) #No margin on the left side | |
plot(c(0,1),type="n", axes=F, xlab="", ylab="") | |
legend("top", colnames(rf.model$err.rate),col=1:6,cex=0.8,fill=1:6) | |
``` | |
#table | |
```{r} | |
ave.table = summaryBy(. ~ factor(Position) , data = nba[,c(2,6:21)], FUN = mean) | |
stargazer(ave.table[,-c(2,13,14,15)], summary = F, font.size = "small", column.sep.width = "1pt", covariate.labels = c("ID", "Position", "PER", "TS", "ORB", "DRB", "TRB", "AST", "BLK", "TOV", "USG", "ORtg", "DRtg","STL")) | |
stargazer(nba) | |
``` | |
#finding best parameters(ntree and mtry) | |
```{r} | |
bestmtry <- tuneRF(nba[,c(6:21)], factor(nba$Position), stepFactor=1.5, improve=1e-5, ntree=500) | |
print(bestmtry) | |
plot(rf.model) | |
``` | |
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#!/usr/bin/env python3 | |
import requests | |
from bs4 import BeautifulSoup | |
import csv | |
import re | |
import pandas as pd | |
import sys # used to dump the whole dataframe | |
r = requests.get('https://www.statcrunch.com/grabdata.php?dataid=1096769&_=1470977876486') # making GET request | |
data = r.text.splitlines() | |
headers = data.pop(0).split() # extracting headers | |
with open('statcrunch_dump.csv', 'wb') as write_file: # file to which data will be written | |
writer = csv.writer(write_file) | |
for line in data: | |
# extracting values | |
name = re.split("\" ", line)[0].strip('"') | |
data = re.split("\" ", line)[1].split() | |
# combining data | |
meta = data[:0] + [name] + data[0:] | |
meta = [d.encode('utf-8') for d in meta] # reason for encoding: remove u | |
writer.writerow(meta) # writing to file | |
df = pd.read_csv('statcrunch_dump.csv') # inserting in pandas | |
print df.to_csv(sys.stdout) # printing from pandas |
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