Overview of the Tennis W75 Le Neubourg Event
The Tennis W75 Le Neubourg tournament in France is set to captivate audiences with its exhilarating matches scheduled for tomorrow. This prestigious event brings together seasoned players competing for glory and titles in a highly anticipated series of matches. As the tournament progresses, expert betting predictions are being crafted to guide enthusiasts in making informed decisions. The upcoming matches promise excitement and strategic gameplay that will keep fans on the edge of their seats.
Expert Betting Predictions
With the tournament just around the corner, expert analysts have been busy evaluating player performances, historical data, and current form to provide comprehensive betting insights. These predictions are not only based on statistical analysis but also consider the nuances of player psychology and environmental factors at the Le Neubourg venue.
- Top Contenders: Identify key players who have consistently performed well in recent tournaments and are expected to excel in tomorrow's matches.
- Underdog Potential: Highlight surprising entries or lesser-known players who could upset the odds with unexpected victories.
- Betting Odds Analysis: Detailed breakdown of odds provided by major bookmakers, offering insights into potential value bets.
The predictions aim to provide a balanced view, catering to both conservative bettors looking for safe bets and adventurous ones seeking high-risk, high-reward opportunities.
Detailed Match Insights
Each match at the Tennis W75 Le Neubourg is a unique narrative unfolding on the court. Below are detailed insights into some of the most anticipated matchups of tomorrow:
Match 1: Veteran vs. Challenger
This match pits a seasoned veteran against an emerging challenger. The veteran's experience and tactical acumen are expected to be tested against the challenger's youthful energy and aggressive playstyle. Betting experts suggest considering both straight-up bets and prop bets related to set scores and match duration.
Match 2: Head-to-Head Rivalry
A classic rivalry returns with these two competitors facing off once again. Historical data shows a close contest between them, making this match a thrilling spectacle. Over/under bets on total games played could offer intriguing opportunities for bettors.
Match 3: Weather Considerations
The forecast suggests possible weather changes during tomorrow's matches. Players with strong indoor performance records might have an advantage if conditions worsen. Betting strategies should account for potential weather disruptions.
Tournament Atmosphere and Venue Details
The Le Neubourg venue is renowned for its vibrant atmosphere and excellent facilities, contributing significantly to the players' performances. The local community's support adds an extra layer of excitement, making it a favorite among players and spectators alike.
- Venue Layout: An overview of the court setup and seating arrangements, enhancing the viewing experience for attendees.
- Audience Engagement: Initiatives taken by organizers to engage fans, including interactive sessions and live commentary.
- Sustainability Efforts: The tournament's commitment to eco-friendly practices, such as waste reduction and energy conservation measures.
The combination of top-tier tennis action and a supportive environment makes this event a must-watch for tennis aficionados.
Player Profiles and Form Analysis
An in-depth look at some of the key players participating in tomorrow's matches, focusing on their recent form, strengths, weaknesses, and playing styles:
- Veteran Player A: Known for his strategic mind and endurance, this player has been steadily climbing the ranks with consistent performances in recent tournaments.
- Newcomer B: A rising star with a powerful serve and aggressive baseline play, making waves with impressive victories against established names.
- All-rounder C: Versatile in adapting to different playing conditions, this player excels both on clay and grass courts.
Understanding these profiles can provide valuable context for betting predictions and enhance the overall viewing experience.
Betting Strategies for Tomorrow's Matches
To maximize your betting potential at the Tennis W75 Le Neubourg, consider employing a mix of strategies tailored to different risk appetites:
- Straight-Up Bets: Place bets on outright winners based on expert predictions and player form analysis.
- Mixing Bets: Combine multiple bets across different matches to spread risk and increase potential returns.
- Liveweight Bets: Monitor live match developments to place timely bets based on unfolding events and player performance during play.
Betting should always be approached responsibly, with due consideration given to budget constraints and risk tolerance levels.
Trends in Women's Tennis Betting
The landscape of women's tennis betting has evolved significantly, with more sophisticated tools and data analytics available to bettors. Key trends include:
- Data-Driven Decisions: Utilizing advanced statistics and machine learning algorithms to predict outcomes with greater accuracy.
- Increase in Prop Bets: Growing popularity of proposition bets related to specific match events like first serve percentage or unforced errors.
- Social Media Influence: Leveraging insights from social media platforms where players share updates about their form and mindset ahead of matches.
Staying informed about these trends can provide bettors with a competitive edge in navigating the dynamic world of tennis betting.
Fan Engagement Activities
In addition to thrilling matches, the Tennis W75 Le Neubourg offers various activities designed to enhance fan engagement:
- Ticket Packages: Special offers including VIP experiences such as meet-and-greets with players and behind-the-scenes tours.
- Social Media Challenges: Interactive contests encouraging fans to share their favorite moments from past tournaments using designated hashtags for a chance to win exclusive prizes.
These initiatives ensure that fans remain connected and invested throughout the tournament.
<|repo_name|>joemiller86/ruby-tetris<|file_sep|>/lib/commands/start.rb
require 'tetris'
class Commands::Start
def initialize(window)
end
def run
end
end<|repo_name|>joemiller86/ruby-tetris<|file_sep|>/lib/board.rb
require 'tetromino'
class Board
attr_reader :grid
def initialize(width = 10 , height = 20)
end
end<|file_sep|># ruby-tetris
A simple tetris game written entirely in Ruby.
## Requirements
- Ruby (tested on Ruby 2.4.0)
## Installation
Clone this repository into your local machine.
git clone [email protected]:joemiller86/ruby-tetris.git
Navigate into `ruby-tetris` directory.
cd ruby-tetris
Install all required gems.
bundle install
## Usage
To start tetris game:
ruby lib/tetris.rb
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
## License
[MIT](https://choosealicense.com/licenses/mit/)
<|repo_name|>joemiller86/ruby-tetris<|file_sep|>/lib/tetromino.rb
class Tetromino
attr_reader :shape,
:color,
:position,
:orientation
def initialize(shape:, color:, position: { x: nil, y: nil }, orientation: nil)
end
end<|repo_name|>joemiller86/ruby-tetris<|file_sep|>/lib/commands/rotate.rb
require 'tetromino'
class Commands::Rotate
def initialize(tetromino)
end
def run
end
end<|file_sep|># frozen_string_literal: true
source "https://rubygems.org"
git_source(:github) {|repo_name| "https://github.com/#{repo_name}" }
gem "gosu", "~>1.0"
gem "rspec", "~>3.0"<|repo_name|>joemiller86/ruby-tetris<|file_sep|>/spec/board_spec.rb
require 'board'
RSpec.describe Board do
describe '#initialize' do
end
end<|repo_name|>joemiller86/ruby-tetris<|file_sep|>/spec/tetromino_spec.rb
require 'tetromino'
RSpec.describe Tetromino do
describe '#initialize' do
end
end<|file_sep|>$LOAD_PATH.unshift File.expand_path('../../lib', __FILE__)
require 'tetris'<|file_sep|># frozen_string_literal: true
class TetrisWindow
include Gosu
def initialize
super(640,480,false)
self.caption = "Ruby Tetris"
@background_image = Gosu::Image.new("assets/images/background.png")
@text_input = Gosu::TextInput.new(self)
end
def needs_cursor?
true
end
def update
if button_down?(Gosu::KbEscape)
close
end
end
def draw
@background_image.draw(0,0,self)
end
def text_input(text)
puts text
end
end
TetrisWindow.new.show <|repo_name|>joemiller86/ruby-tetris<|file_sep|>/lib/commands/left.rb
require 'tetromino'
class Commands::Left
def initialize(tetromino)
end
def run
end
end<|file_sep|># -*- coding: utf-8 -*-
import pandas as pd
def _split(df):
return df[df.columns[:10]], df[df.columns[10:]]
def _calc_diff(df):
df['diff'] = df['target'] - df['predict']
return df
def _calc_error(df):
df['error'] = abs(df['diff'])
return df
def _calc_max_error(df):
return max(df['error'])
def _calc_mean_error(df):
return sum(df['error']) / len(df)
def _calc_accuracy(df):
df = df[df['error'] <= df['threshold']]
return len(df) / len(_split(df)[0])
def evaluate(test_data_file_path,
train_data_file_path=None,
predict_data_file_path=None,
threshold=1):
test_data = pd.read_csv(test_data_file_path)
if train_data_file_path:
train_data = pd.read_csv(train_data_file_path)
if predict_data_file_path:
predict_data = pd.read_csv(predict_data_file_path)
if train_data_file_path:
data = pd.merge(test_data,
train_data,
on=['site_id', 'hour'],
suffixes=('_test', '_train'))
else:
data = test_data.copy()
if predict_data_file_path:
data = pd.merge(data,
predict_data,
on='id',
suffixes=('', '_predict'))
else:
data = data.copy()
data = _split(data)
target_df = data[0]
predict_df = data[1]
predict_df.columns = ['id', 'predict']
target_df.columns = ['id', 'target']
data_df = pd.merge(target_df,
predict_df,
on='id')
data_df['threshold'] = threshold
data_df = _calc_diff(data_df)
data_df = _calc_error(data_df)
max_error = _calc_max_error(data_df)
mean_error = _calc_mean_error(data_df)
accuracy = _calc_accuracy(data_df)
return max_error, mean_error, accuracy
if __name__ == '__main__':
evaluate('data/test.csv',
None,
None)<|repo_name|>GiovanniTosi/kaggle-airbnb-dc-prices-prediction-mlcourse.ai-competition-2019-autumn-edition<|file_sep|>/README.md
# kaggle-airbnb-dc-prices-prediction-mlcourse.ai-competition-2019-autumn-edition
Airbnb DC Prices Prediction (MLCourse.ai Competition)
This repository contains my solution for Airbnb DC Prices Prediction competition held by MLCourse.ai (https://mlcourse.ai).
The problem is available here: https://www.kaggle.com/c/mlcourseai-spring-2019.
The competition was divided into two phases:
1) Predicting price per listing per hour.
2) Predicting number of bookings per hour.
The goal was getting best score according evaluation metric described below.
The evaluation metric was same for both phases.
The metric was based on MAE (mean absolute error).
For phase one it was calculated separately for each listing id as follows:
MAE(i) = sum(abs(y_pred(i)_t - y_true(i)_t)) / count(y_true(i)_t)
where i - listing id.
t - hour index.
y_pred(i)_t - predicted price for listing i at hour t.
y_true(i)_t - true price for listing i at hour t.
Count(y_true(i)_t) - count of non-missing values y_true(i)_t.
Then metric was calculated as follows:
metric1(phase one) = sum(MAE(i)) / count(MAE(i))
For phase two metric was calculated similarly but there were some additional restrictions.
For each listing id i there were two groups of hours:
1) Non-missing values y_true(i)_t >0.
2) Non-missing values y_true(i)_t ==0.
MAE group one (i) was calculated as above.
MAE group two (i) was calculated as follows:
MAE group two (i) =
sum(abs(y_pred(i)_t - y_true(i)_t)) / count(y_true(i)_t >0)
Then final metric was calculated as follows:
metric1(phase two) =
sum(MAE group one (i)) / count(MAE group one (i)) +
sum(MAE group two (i)) / count(MAE group two (i))
There were additional restrictions which I will describe later.
I used python3.x programming language.
I used following libraries: numpy pandas matplotlib seaborn scipy statsmodels lightgbm sklearn xgboost catboost mlxtend imblearn pyod tsfresh tensorflow keras etc.
Phase One:
The best solution I've got achieved MAE=5.15.
I started from simple baseline which was average price per listing per hour over whole training set.
It achieved MAE=21.33 which was not good enough.
I noticed that prices vary significantly between different neighborhoods so I decided use neighborhood_id as categorical feature instead of average prices per neighborhood per hour which were available in train data set.
I tried several approaches:
1) Simple linear regression model without any feature engineering which achieved MAE=12.29.
2) LightGBM model without any feature engineering which achieved MAE=11.49.
3) LightGBM model with several feature engineering approaches which achieved MAE=9.84.
I started from simple linear regression model because it gave me intuition about feature importance.
Feature engineering included following steps:
1) Creating features related to day period such as day type (weekend/weekday), month period etc.
2) Creating features related to seasonality such as number of days from summer/winter start etc.
3) Creating features related to holidays such as number of days from holidays etc.
4) Creating features related time series such as lag features etc.
Then I applied LightGBM model instead linear regression model because it gave me better result without any feature engineering approach applied.
Finally I combined several feature engineering approaches together which gave me best result.
For all models I've got best results when I applied scaling by subtracting mean values per listing id from prices before training models then adding mean values back after prediction phase.
Phase Two:
The best solution I've got achieved MAE=4.07 + 0.36 which corresponds to rank #83 out of #275 teams at competition leaderboard during submission period (see phase_two_solution.csv file).
I started from simple baseline which was average number of bookings per listing per hour over whole training set.
It achieved MAE=14.73 + 0.57 which was not good enough.
Then I tried several approaches similar to phase one but they gave much worse results than baseline so I decided try another approach using neural networks.
For neural network models I've got best results when I used log scale target variable because it gave me much better results than linear scale target variable even though it violates restriction described below:
In addition there were several additional restrictions which needed be considered during prediction phase:
1) In each test sample there should be no missing values so missing values were imputed by zeros during prediction phase but zero value means no bookings so imputing zeros leaded that neural network models predicted zero bookings even though there were non-zero bookings during real life so instead imputing zeros during prediction phase I've used zero padding technique similar like Keras pad_sequences function during training phase then zero padded sequences were converted back after prediction phase but zero padding technique didn't give much improvement so I decided use log scale target variable instead even though it violates restriction described below:
In addition there were several additional restrictions which needed be considered during prediction phase:
1) In each test sample there should be no missing values so missing values were imputed by zeros during prediction phase but zero value means no bookings so imputing zeros leaded that neural network models predicted zero bookings even though there were non-zero bookings during real life so instead imputing zeros during prediction phase I've used zero padding technique similar like Keras pad_sequences function during training phase then zero padded sequences were converted back after prediction phase but zero padding technique didn't give much improvement so I decided use log scale target variable instead
