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M15 Szczawno stats & predictions

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Welcome to Tomorrow's Tennis Action in Szczawno, Poland!

Get ready for an electrifying day of tennis as the M15 tournament in Szczawno, Poland, heats up with matches scheduled for tomorrow. With a lineup of talented young players vying for the top spot, this event promises thrilling encounters on the court. Whether you're a die-hard tennis fan or a newcomer to the sport, you won't want to miss the action. In this comprehensive guide, we'll dive into the key matches, provide expert betting predictions, and share insights into what makes this tournament so special.

Key Matches to Watch

The tournament features several must-watch matches that will keep fans on the edge of their seats. Here's a breakdown of the top clashes:

  • Match 1: Top Seed vs. Rising Star

    • The top seed in the tournament faces off against a promising young player who has been making waves with his aggressive playing style. This match is expected to be a showcase of skill and strategy, with both players eager to make their mark.

  • Match 2: Local Favorite vs. International Challenger

    • A local favorite takes on an international challenger in what promises to be a fierce battle. The home crowd will be cheering loudly, adding an extra layer of excitement to this match.

  • Match 3: Underdog Story

    • An underdog player who has defied expectations throughout the tournament will face a seasoned opponent. This match could be one of the most unpredictable and thrilling encounters of the day.

Expert Betting Predictions

For those interested in placing bets, here are some expert predictions based on player form, head-to-head records, and other key factors:

  • Match 1 Prediction: Top Seed Wins

    • The top seed has shown consistent form throughout the tournament and is expected to leverage his experience to secure a victory.

  • Match 2 Prediction: Close Call

    • This match is predicted to be a nail-biter, with both players having equal chances of winning. A few crucial points could determine the outcome.

  • Match 3 Prediction: Underdog Triumphs

    • Despite being the underdog, this player has displayed remarkable resilience and could pull off an upset against his opponent.

Tournament Highlights

The M15 tournament in Szczawno is known for its vibrant atmosphere and high-quality tennis. Here are some highlights that make this event stand out:

  • Diverse Talent Pool

    • The tournament attracts players from around the world, showcasing a diverse range of styles and techniques.

  • Fan Engagement

    • Fans play a crucial role in creating an energetic atmosphere, with many local supporters turning up to cheer on their favorite players.

  • Opportunities for Emerging Stars

    • This tournament serves as a launching pad for young players looking to make their mark in professional tennis.

Player Profiles

Get to know some of the standout players competing in tomorrow's matches:

  • Player A: The Top Seed

    • A seasoned player known for his strategic gameplay and mental toughness. He has consistently performed well in previous tournaments.

  • Player B: The Rising Star

    • This young talent has been turning heads with his powerful serves and aggressive baseline play. Keep an eye on him as he seeks to make his mark.

  • Player C: The Local Favorite

    • A hometown hero with strong support from local fans. His familiarity with the court conditions gives him an edge over international opponents.

  • Player D: The Underdog

    • An unlikely contender who has surprised many with his performances so far. His determination and grit make him a formidable opponent.

Tips for Watching Live

If you're planning to watch the matches live, here are some tips to enhance your viewing experience:

  • Arrive Early

    • Gaining entry early allows you to soak in the atmosphere and get comfortable before the action begins.

  • Check Match Schedules

    • Make sure you know the start times for each match so you don't miss any key moments.

  • Engage with Other Fans

    • Talking to fellow fans can add to the excitement and provide different perspectives on the matches.

  • Stay Updated on Scores

    • If you can't watch every match live, follow live updates online or through sports apps to stay informed.

Betting Tips for Enthusiasts

If you're considering placing bets on tomorrow's matches, here are some tips to help you make informed decisions:

  • Analyze Player Form

    • Look at recent performances and head-to-head records to gauge how players might fare against each other.

  • Consider Court Conditions

    • Court surface and weather conditions can impact play styles and outcomes. Factor these into your predictions.

  • Diversify Your Bets
    • <|repo_name|>jguerra13/AC4DS<|file_sep|>/02-Practices/Practice4.Rmd --- title: "Practice4" author: "Joel Guerra" date: "9/24/2018" output: html_document --- {r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ## Practice4 ### Introduction In this practice we will use machine learning techniques such as Random Forests (RF) and Boosting (GBM) applied in real data sets from UCI Machine Learning Repository. ### Data Set The data set we are going use is called [Pima Indians Diabetes Database](https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes) from UCI Machine Learning Repository. This database contains medical details about Pima Indian women aged between twenty-two and fifty-nine years old. The objective of this database is predicting whether or not they have diabetes based on diagnostic measures. ### Questions 1. Create an R script that reads data from csv file. 2. Use `str()` function in order to see how data looks like. 3. Split data into train set (80%) and test set (20%) using `createDataPartition()` function from `caret` package. 4. Fit RF model using training set. 5. Fit GBM model using training set. 6. Evaluate models performance using test set. 7. Compare models performance using `caret` package. ### Solution #### Read Data {r} library(caret) library(gbm) data <- read.csv("diabetes.csv") #### View Data {r} str(data) #### Split Data {r} set.seed(1234) trainIndex <- createDataPartition(data$Outcome, p = .8, list = FALSE) train <- data[ trainIndex,] test <- data[-trainIndex,] #### Fit RF Model {r} fit.rf <- randomForest(Outcome ~ . , data = train) #### Fit GBM Model {r} fit.gbm <- gbm(Outcome ~ . , distribution = "bernoulli", n.trees =5000, interaction.depth =4, shrinkage =0.01, cv.folds =5, n.cores = NULL, verbose =FALSE, data=train) #### Evaluate Models Performance {r} pred.rf <- predict(fit.rf,newdata=test,type="class") confusionMatrix(pred.rf,test$Outcome) pred.gbm <- predict(fit.gbm,newdata=test,type="response") pred.gbm <- ifelse(pred.gbm >0.5,"1","0") confusionMatrix(pred.gbm,test$Outcome) #### Compare Models Performance {r} results <- resamples(list(RF=fit.rf, GBM=fit.gbm)) summary(results) dotplot(results) ## Conclusion In conclusion both models perform almost equally well.<|file_sep|># AC4DS Applied Computation for Data Science <|repo_name|>jguerra13/AC4DS<|file_sep|>/02-Practices/Practice6.Rmd --- title: "Practice6" author: "Joel Guerra" date: "10/2/2018" output: html_document --- {r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ## Practice6 ### Introduction In this practice we will implement deep neural networks using Keras library. ### Data Set The data set we are going use is called [Pima Indians Diabetes Database](https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes) from UCI Machine Learning Repository. This database contains medical details about Pima Indian women aged between twenty-two and fifty-nine years old. The objective of this database is predicting whether or not they have diabetes based on diagnostic measures. ### Questions 1. Create an R script that reads data from csv file. 2. Use `str()` function in order to see how data looks like. 3. Normalize input features using `preProcess()` function from `caret` package. 4. Split data into train set (80%) and test set (20%) using `createDataPartition()` function from `caret` package. 5. Build deep neural network model using Keras library. 6. Train model using training set. 7. Evaluate model performance using test set. 8. Visualize model performance using accuracy metric. ### Solution #### Load Libraries {r} library(keras) library(caret) library(dplyr) #### Read Data {r} data <- read.csv("diabetes.csv") #### View Data {r} str(data) #### Normalize Input Features {r} data[,1:8] <- preProcess(data[,1:8],method=c("center","scale")) %>% predict(data[,1:8]) #### Split Data {r} set.seed(1234) trainIndex <- createDataPartition(data$Outcome,p=.8,list=FALSE) train <- data[trainIndex,-9] test <- data[-trainIndex,-9] labels.train <- train %>% select(Outcome) %>% unlist() labels.test <- test %>% select(Outcome) %>% unlist() #### Build Deep Neural Network Model Using Keras Library {r} model <- keras_model_sequential() %>% layer_dense(units=32,input_shape=c(8)) %>% layer_activation("relu") %>% layer_dense(units=16) %>% layer_activation("relu") %>% layer_dense(units=1) %>% layer_activation("sigmoid") summary(model) model %>% compile( loss="binary_crossentropy", optimizer="adam", metrics=c("accuracy") ) #### Train Model Using Training Set {r} model %>% fit(train, labels.train, epochs=30, batch_size=128, validation_split=0.2 ) #### Evaluate Model Performance Using Test Set {r} model %>% evaluate(test, labels.test ) #### Visualize Model Performance Using Accuracy Metric {r} history <- model %>% fit(train, labels.train, epochs=1000, batch_size=128, validation_split=0.2, verbose=FALSE ) plot(history$metrics$val_loss,type='l',col='red',ylim=c(0,max(history$metrics$val_loss))) lines(history$metrics$loss,type='l',col='blue') legend('topright',legend=c('val_loss','loss'),col=c('red','blue'),lty=1) plot(history$metrics$val_accuracy,type='l',col='red',ylim=c(0,max(history$metrics$val_accuracy))) lines(history$metrics$accuracy,type='l',col='blue') legend('topleft',legend=c('val_accuracy','accuracy'),col=c('red','blue'),lty=1) #Save Weights And Model Structure model %>% save_model_hdf5('model.h5') #Save Weights And Model Structure Separately saveRDS(model,toFile='model.rds') #Load Weights And Model Structure Separately new_model <- load_model_hdf5('model.h5') new_model_2 <- readRDS('model.rds') <|file_sep|># Install required libraries install.packages("knitr") install.packages("dplyr") install.packages("readxl") install.packages("tidyr") install.packages("ggplot2") install.packages("stringi") install.packages("zoo") install.packages("xts") install.packages("tidyverse") # Install tidyquant if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("tidyquant") # Install devtools if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") # Install quantmod devtools::install_github(repo = "joshuaulrich/quantmod") # Install RMySQL if (!requireNamespace("RMySQL", quietly = TRUE)) install.packages("RMySQL") # Install RPostgreSQL if (!requireNamespace("RPostgreSQL", quietly = TRUE)) install.packages("RPostgreSQL") # Install RSQLite if (!requireNamespace("RSQLite", quietly = TRUE)) install.packages("RSQLite") # Install rvest if (!requireNamespace("rvest", quietly = TRUE)) install.packages("rvest") # Install scales if (!requireNamespace("scales", quietly = TRUE)) install.packages("scales") # Install caret if (!requireNamespace("caret", quietly = TRUE)) install.packages("caret") # Install e1071 if (!requireNamespace("e1071", quietly = TRUE)) install.packages("e1071") # Install randomForest if (!requireNamespace("randomForest", quietly = TRUE)) install.packages("randomForest") # Install caretEnsemble if (!requireNamespace("caretEnsemble", quietly = TRUE)) install.packages('caretEnsemble') # Install gbm if (!requireNamespace('gbm', quietly = TRUE)) install.packages('gbm') # Install xgboost if (!requireNamespace('xgboost', quietly = TRUE)) install.packages('xgboost') # Install keras install_keras() <|repo_name|>jguerra13/AC4DS<|file_sep|>/01-Lectures/06-MachineLearning.md --- title: "Machine Learning" author: "Joel Guerra" date: "9/17/2018" output: ioslides_presentation: widescreen: true --- ## Machine Learning ### Definition - **Machine learning** is a method of data analysis that automates analytical model building. - It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. ### Types There are three types of machine learning: - Supervised learning - Unsupervised learning - Reinforcement learning ## Supervised Learning - Supervised learning algorithms build a mathematical model based on sample data called _training data_. - Each item in training data consists of one or more inputs objects (represented as vectors) along with a correct output value (also called label). - Training algorithm builds model by mapping inputs to desired outputs. - After model is trained it can be used for mapping new inputs to desired outputs. ## Unsupervised Learning - Unsupervised learning algorithms are used when information used to train is neither classified nor labeled. - Unsupervised learning studies how systems can infer a function to describe hidden structure from unlabeled data. ## Reinforcement Learning - Reinforcement learning is concerned with how software agents ought take actions in an environment so as maximize some notion of cumulative reward. - Reinforcement learning differs from supervised learning because correct input/output pairs are never presented but instead reinforcement signal (or feedback signal) is presented which give measure of success of actions taken by agent. ## Regression vs Classification Two main types of supervised learning problems are regression problems and classification problems: - **Regression problems**: attempt to predict results within a continuous output variable given one or more input variables. - **Classification problems**: attempt to predict results within a discrete output variable given one or more input variables. ## Regression Example ![Linear Regression Example](../images/regression.png) Image Source: [Wikipedia](https://en.wikipedia.org/wiki/Linear_regression#/media/File:Simplified_linear_regression.svg) ## Classification Example ![Decision Tree Example](../images/classification.png) Image Source: [Wikipedia](https://en.wikipedia.org/wiki/Decision_tree#/media/File:Decision_tree_classification_svg.png) ## Steps Involved In Machine Learning 1. Define Problem Statement 2. Collect Relevant Data 3. Prepare Data For Analysis 4. Analyze Data Using Machine Learning Algorithms 5. Select Best Algorithm Based On Results Obtained From Step #4 6. Present Results To Stakeholders ## Problem Statement Problem statement defines what problem you want your machine learning algorithm(s) solve. ## Collecting Relevant Data Once problem statement is defined relevant data should be collected so that it can be used later on during analysis phase. ## Preparing Data For Analysis Preparing collected raw data involves cleaning up dirty data (i.e., removing incomplete records), transforming it into appropriate format (i.e., converting strings into numbers), normalizing it if necessary etc... ## Analyzing Data Using Machine Learning Algorithms Once prepared dataset should be analyzed by applying various machine learning algorithms such as decision trees or support vector machines etc... ## Selecting Best Algorithm Based On Results Obtained From Step #4 After analyzing prepared dataset