The Thrilling World of the AFC Women's Champions League Preliminary Round Group A
The AFC Women's Champions League Preliminary Round Group A is the ultimate stage where aspiring football clubs from across Asia showcase their prowess, aiming for glory in the prestigious competition. This stage of the tournament is not just about winning; it's about proving one's mettle against some of the most talented teams in Asia. As each match unfolds, fans and experts alike are treated to a spectacle of skill, strategy, and sheer determination. Stay updated with our daily coverage, where we bring you the latest matches, expert betting predictions, and in-depth analyses to keep you at the forefront of this thrilling competition.
Understanding the Structure of Group A
Group A comprises some of the most competitive teams in Asia, each vying for a spot in the knockout stages. The structure is designed to test every aspect of a team's capabilities, from tactical acumen to individual brilliance. With each team playing against the others in a round-robin format, every match is crucial. The top two teams from each group will advance to the next round, making every game a high-stakes affair.
Daily Match Updates: Stay Informed
Our platform provides daily updates on all matches in Group A. Whether you're a die-hard fan or a casual observer, our comprehensive coverage ensures you never miss a moment of the action. From live scores to post-match analyses, we have you covered.
- Live Scores: Get real-time updates on match scores and key events.
- Match Summaries: Detailed accounts of what transpired on the pitch.
- Player Performances: Highlights of standout players and pivotal moments.
Expert Betting Predictions: Your Guide to Smart Bets
Betting on football can be both exciting and daunting. Our expert analysts provide insights and predictions to help you make informed decisions. Whether you're placing your first bet or are a seasoned punter, our tips can enhance your betting experience.
- Match Predictions: Who will win? Will it be a draw?
- Betting Odds: Updated odds from leading bookmakers.
- Tips and Strategies: Expert advice on how to place smart bets.
In-Depth Match Analyses: Beyond the Scoreline
Every match tells a story beyond just the final score. Our analysts delve deep into the tactics, formations, and key moments that defined each game. Understanding these elements can give you a richer appreciation of the sport and help you anticipate future outcomes.
- Tactical Breakdowns: How did the teams set up? What strategies were employed?
- Key Performances: Who were the standout players? Which substitutions made a difference?
- Potential Upsets: Were there any unexpected results? What could have been done differently?
Player Spotlights: Rising Stars of Group A
The AFC Women's Champions League is not just about team success; it's also a platform for individual brilliance. Each match brings new heroes to the fore, and we spotlight these rising stars who are making waves in Group A.
- Rising Talents: Profiles of young players who are turning heads.
- Milestone Achievements: Celebrating significant career moments.
- Future Prospects: What does the future hold for these promising athletes?
The Role of Coaches: Masterminding Success
Behind every successful team is a visionary coach who orchestrates strategies and motivates players. In Group A, coaching styles vary widely, from defensive masterminds to attacking gurus. We explore how these coaches are shaping their teams' journeys in the tournament.
- Creative Tactics: Innovative approaches that have turned matches around.
- Player Management: How coaches handle player rotations and fitness.
- Inspirational Leadership: Stories of how coaches inspire their squads.
Fan Engagement: Connecting with Supporters Worldwide
Football is more than just a game; it's a global community that brings people together. We connect fans from around the world through interactive content, forums, and social media discussions.
- Fan Forums: Share your thoughts and engage with fellow supporters.
- Social Media Updates: Follow us on platforms like Twitter and Instagram for real-time interactions.
- Poll Participation: Have your say on who you think will be Player of the Tournament.
The Future of Women's Football in Asia
The AFC Women's Champions League is more than just a tournament; it's a catalyst for growth in women's football across Asia. By showcasing talent and providing competitive platforms, it paves the way for future generations.
- Growth Opportunities: How tournaments like this are boosting women's football.
- Sponsorship and Investment: The increasing financial support for women's teams.
- Youth Development Programs: Initiatives aimed at nurturing young talent.
Historical Context: How Far Have We Come?
lauragreene/try_git<|file_sep|>/README.md
# try_git
my first github repo
## subheader
watching tutorial
<|repo_name|>shreyanshmohan/PracticalMachineLearning<|file_sep|>/practicalmachinelearning.Rmd
---
title: "Practical Machine Learning"
author: "Shreyansh Mohan"
date: "February 27,2016"
output:
html_document:
keep_md: yes
---
## Synopsis
Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it.
In this project we will use data from accelerometers on the belt (measured at belt level), forearm (measured at forearm level), arm (measured at arm level), and dumbell (measured at dumbell level) of participants while they perform barbell lifts correctly and incorrectly in five different ways.
The goal is to predict the manner in which they did the exercise.
## Data
The training data for this project are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv
The test data are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv
The data for this project come from this source:
http://groupware.les.inf.puc-rio.br/har
## Loading Libraries
{r echo=TRUE}
library(caret)
library(rpart)
library(rpart.plot)
library(randomForest)
## Reading Data
{r echo=TRUE}
set.seed(12345)
training <- read.csv("C:/Users/SHREYANSH MOHAN/Desktop/pml-training.csv", na.strings=c("NA","#DIV/0!",""))
testing <- read.csv("C:/Users/SHREYANSH MOHAN/Desktop/pml-testing.csv", na.strings=c("NA","#DIV/0!",""))
dim(training)
dim(testing)
## Cleaning Training Data
First we remove variables with near zero variance.
{r echo=TRUE}
nzv <- nearZeroVar(training)
training <- training[, -nzv]
Next we remove variables that have mostly NA values.
{r echo=TRUE}
training <- training[, colSums(is.na(training)) ==0]
dim(training)
Then we remove variables that have no relationship with outcome variable.
{r echo=TRUE}
training <- training[,-c(1:7)]
dim(training)
## Partitioning Data into Training & Testing Sets
Now we partition our training data into two parts using caret package - one part will be used for model training & other part will be used for model testing.
{r echo=TRUE}
inTrain <- createDataPartition(y=training$classe,p=0.7,list=FALSE)
trainData <- training[inTrain,]
testData <- training[-inTrain,]
dim(trainData); dim(testData)
## Model Building & Cross Validation
We will use Random Forest algorithm for prediction as it has been found very useful by many researchers when dealing with large datasets having large number of input variables.
We will use cross validation during model building using caret package.
{r echo=TRUE}
modelFit <- train(classe ~ .,
method="rf",
data=trainData,
trControl=trainControl(method="cv",number=5),
verbose = FALSE)
modelFit$finalModel
## Testing Model Accuracy
Now we will test our model using testData which was not used during model building phase.
{r echo=TRUE}
predictions <- predict(modelFit,testData)
confusionMatrix(predictions,testData$classe)
## Using Model on Test Data
Finally we will use our model on test dataset provided by Coursera.
{r echo=TRUE}
answers <- predict(modelFit,newdata=testing)
answers
<|repo_name|>shreyanshmohan/PracticalMachineLearning<|file_sep|>/practicalmachinelearning.md
# Practical Machine Learning
Shreyansh Mohan
February 27,2016
## Synopsis
Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it.
In this project we will use data from accelerometers on the belt (measured at belt level), forearm (measured at forearm level), arm (measured at arm level), and dumbell (measured at dumbell level) of participants while they perform barbell lifts correctly and incorrectly in five different ways.
The goal is to predict the manner in which they did the exercise.
## Data
The training data for this project are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv
The test data are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv
The data for this project come from this source:
http://groupware.les.inf.puc-rio.br/har
## Loading Libraries
r
library(caret)
library(rpart)
library(rpart.plot)
library(randomForest)
## Reading Data
r
set.seed(12345)
training <- read.csv("C:/Users/SHREYANSH MOHAN/Desktop/pml-training.csv", na.strings=c("NA","#DIV/0!",""))
testing <- read.csv("C:/Users/SHREYANSH MOHAN/Desktop/pml-testing.csv", na.strings=c("NA","#DIV/0!",""))
dim(training)
## [1] 19622 160
r
dim(testing)
## [1] 20 160
## Cleaning Training Data
First we remove variables with near zero variance.
r
nzv <- nearZeroVar(training)
training <- training[, -nzv]
Next we remove variables that have mostly NA values.
r
training <- training[, colSums(is.na(training)) ==0]
dim(training)
## [1] 19622 100
Then we remove variables that have no relationship with outcome variable.
r
training <- training[,-c(1:7)]
dim(training)
## [1] 19622 93
## Partitioning Data into Training & Testing Sets
Now we partition our training data into two parts using caret package - one part will be used for model training & other part will be used for model testing.
r
inTrain <- createDataPartition(y=training$classe,p=0.7,list=FALSE)
trainData <- training[inTrain,]
testData <- training[-inTrain,]
dim(trainData); dim(testData)
## [1] 13737 93
## [1] 5885 93
## Model Building & Cross Validation
We will use Random Forest algorithm for prediction as it has been found very useful by many researchers when dealing with large datasets having large number of input variables.
We will use cross validation during model building using caret package.
Note : I am using randomForest package rather than caret package because caret package takes longer time than randomForest package when dealing with large datasets having large number of input variables.
Note : We don't need tune parameter while using randomForest algorithm because caret package automatically performs tuning during model building phase itself.
Note : While building random forest model I am getting below warning message -
"**fitting to entire tree(s) didn't converge **"
But I don't think this warning message affects my final prediction as my final accuracy is quite high i.e., **99%** . So I am ignoring this warning message.
Note : I am setting verbose = FALSE so that progress bar doesn't show up while building model.
r
modelFit <- train(classe ~ .,
method="rf",
data=trainData,
trControl=trainControl(method="cv",number=5),
verbose = FALSE)
modelFit$finalModel
##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: min(29, round(p/3)) = 9
##
## OOB estimate of error rate: 0.24%
## Confusion matrix:
## A B C D E class.error
## A 3907 2 0 0 0 0.0005128
## B 11 2654 2 0 0 0.0042518
## C 0 13 2396 2 0 0.0056895
## D 0 1 10 2249 2 0.0055854
## E 0 0 1 11 2542 0.0051134
Note : Random forest algorithm gives us following output : OOB estimate error rate i.e., out-of-bag estimate error rate which indicates how well our model can predict new data.
Out-of-bag estimate error rate : **OOB error**
Note : It can also be calculated using following code :
`predict(modelFit,newdata=testData)$err.rate`
Note : When running above code I got below error message -
"**Error in eval(expr, envir, enclos): object 'err.rate' not found**"
I searched on internet but couldn't find any solution regarding above error message.
So I ignored above error message as I already have OOB error calculated by random forest algorithm itself.
### Plotting Random Forest Model
Note : Below plot shows importance score calculated by random forest algorithm itself.
Note : According to above plot there are total **14** features which have importance score greater than **10**.
Note : So if required we can further reduce number features by selecting only those features which have importance score greater than **10**.
Note : As per caret package documentation ,
>"A measure between **0** and **100** indicating how important each variable was in predicting outcomes across all trees."
### Plotting Decision Tree Model
Note : Below plot shows decision tree built using rpart algorithm.
Note : Decision tree algorithm gives us following output - confusion matrix which shows accuracy value calculated based on test dataset.
Note : According to above confusion matrix accuracy value is **99%**.
### Plotting Decision Tree Model Using Pruned Tree
Note : In order to reduce complexity let us prune decision tree built using rpart algorithm.
Note : Below plot shows pruned decision tree.
Note : Pruning decision tree has no effect on accuracy value as per confusion matrix built using