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Liga Portugal 2 stats & predictions

Unlock the Thrill of Liga Portugal 2: Your Ultimate Guide

Liga Portugal 2 stands as a vibrant showcase of football talent, where emerging teams and players vie for glory. This dynamic league not only offers exhilarating matches but also serves as a fertile ground for expert betting predictions. Our platform is dedicated to providing you with the freshest match updates and insightful predictions, ensuring you stay ahead in the game. Dive into the heart of Portuguese football with our comprehensive coverage, where every match is an opportunity to witness the next big star rise.

Portugal

Liga Portugal 2

Why Liga Portugal 2 is a Must-Watch

Liga Portugal 2 is more than just a league; it's a battleground for aspiring football clubs aiming to make their mark. With teams from across Portugal competing fiercely, the league offers a unique blend of unpredictability and excitement. Each match day brings new stories of triumph, heartbreak, and sheer determination. Whether you're a seasoned fan or new to Portuguese football, Liga Portugal 2 promises an unforgettable experience.

The Teams to Watch

The league boasts a diverse array of teams, each bringing its own style and strategy to the pitch. From traditional powerhouses making a comeback to underdogs defying expectations, Liga Portugal 2 is home to some of the most intriguing matchups in football. Here are a few teams that have been making waves:

  • Portimonense SC: Known for their solid defense and tactical play, Portimonense has consistently been a formidable opponent.
  • Vitória SC: With a rich history and passionate fanbase, Vitória SC continues to be a dominant force in the league.
  • Belenenses SAD: Renowned for their youth development program, Belenenses SAD is always on the lookout for emerging talents.
  • Gil Vicente FC: A team that never backs down, Gil Vicente FC is known for their resilience and fighting spirit.

Match Highlights: Fresh Updates Every Day

Staying updated with Liga Portugal 2 has never been easier. Our platform provides real-time updates on every match, ensuring you never miss out on any action. From last-minute goals to dramatic turnarounds, we cover it all. Here's what you can expect:

  • Live Scores: Get instant access to live scores and match progress.
  • Match Reports: Detailed reports with key moments and player performances.
  • Video Highlights: Watch the best moments from each match at your convenience.

Expert Betting Predictions: Win Big with Confidence

Betting on Liga Portugal 2 can be both thrilling and rewarding. Our expert analysts provide daily predictions to help you make informed decisions. Whether you're a seasoned bettor or new to the scene, our insights can give you an edge. Here's how we help you win big:

  • Detailed Analysis: In-depth analysis of team form, head-to-head records, and player stats.
  • Prediction Models: Advanced models that consider various factors to predict match outcomes.
  • Betting Tips: Daily tips and recommendations tailored to different betting markets.

The Rise of New Stars: Discover Tomorrow's Legends

Liga Portugal 2 is not just about the teams; it's about the players who are destined for greatness. Many of today's football legends began their journey in this very league. Keep an eye on these rising stars who are making headlines:

  • Ricardo Horta: A versatile forward known for his scoring prowess and creativity on the field.
  • Nuno Santos: A dynamic midfielder whose vision and passing ability make him a key player for his team.
  • Dany Mota: A young talent with exceptional speed and dribbling skills, constantly drawing attention from top clubs.
  • Pedro Gonçalves: A prolific striker whose goal-scoring record has made him one of the most exciting prospects in Portuguese football.

The Thrill of Promotion: The Battle for Ascension

The ultimate goal for every team in Liga Portugal 2 is promotion to the top-tier Primeira Liga. The journey is fraught with challenges, but the reward is immense. Here's what makes the promotion race so captivating:

  • Tight Competitions: With several teams vying for limited spots, every match can drastically change standings.
  • Epic Clashes: Derbies between local rivals add an extra layer of intensity to the promotion battle.
  • Dramatic Finale: The final rounds often feature nail-biting finishes that keep fans on the edge of their seats.

The Role of Fans: The Heartbeat of Liga Portugal 2

# -*- coding: utf-8 -*- """ Created on Fri Sep 27 19:56:26 2019 @author: moham """ import pandas as pd import numpy as np from matplotlib import pyplot as plt from sklearn import datasets df = datasets.load_boston() X = df.data y = df.target # import data from csv file # data = pd.read_csv("data.csv") # X = data.iloc[:, :-1].values # y = data.iloc[:, -1].values print("Shape X:", X.shape) print("Shape y:", y.shape) # plot data #plt.scatter(X[:,0], y) #plt.show() def split_data(X,y): """ Split data into training set (70%) and test set (30%). Input: X (ndarray): shape (m,n) y (ndarray): shape (m,) Output: X_train (ndarray): shape (int(m*0.7),n) X_test (ndarray): shape (int(m*0.3),n) y_train (ndarray): shape (int(m*0.7),) y_test (ndarray): shape (int(m*0.3),) """ # shuffle data m = len(y) idx = np.arange(m) np.random.shuffle(idx) # split data m_train = int(m*0.7) X_train = X[idx[:m_train],:] X_test = X[idx[m_train:],:] y_train = y[idx[:m_train]] y_test = y[idx[m_train:]] return X_train,X_test,y_train,y_test X_train,X_test,y_train,y_test = split_data(X,y) print("Shape X_train:",X_train.shape) print("Shape X_test:",X_test.shape) def compute_mse(y,y_pred): """ Compute mean square error. Input: y (ndarray): shape (m,) y_pred (ndarray): shape (m,) Output: mse (float): """ mse = np.mean((y-y_pred)**2) return mse def normalize_data(X): """ Normalize data Input: X (ndarray): shape(m,n) Output: X_normlized(ndarray): shape(m,n) mu(ndarray): shape(1,n) mean values sigma(ndarray): shape(1,n) standard deviation values """ m,n = X.shape mu = np.mean(X,axis=0).reshape(1,-1) sigma = np.std(X,axis=0).reshape(1,-1) X_normalized = (X - mu)/sigma return X_normalized,mu,sigma X_normalized,mu,sigma = normalize_data(X_train) print("Shape mu:",mu.shape) print("Shape sigma:",sigma.shape) def initialize_theta(n): """ Initialize theta Input: n(int): number of features Output: theta(ndarray): shape(n+1,) """ theta = np.zeros(n+1).reshape(-1,1) return theta theta = initialize_theta(X_normalized.shape[1]) print("Shape theta:",theta.shape) def add_ones(X): """ Add ones as first column Input: X(ndarray): shape(m,n) Output: ones_X(ndarray): shape(m,n+1) """ m,n = X.shape ones_X = np.ones((m,n+1)) ones_X[:,1:] = X return ones_X ones_X_normalized = add_ones(X_normalized) print("Shape ones_X_normalized:",ones_X_normalized.shape) def cost_function(theta,X,y): """ Compute cost function Input: theta(ndarray): shape(n+1,) X(ndarray): shape(m,n) y(ndarray): shape(m,) Output: J(float) """ m,_=X.shape h_x = np.dot(X,theta) J =(np.sum((h_x-y)**2))/(2*m) return J J=cost_function(theta,ones_X_normalized,y_train) print("Cost function:",J) def gradient_descent(X,y,alpha,num_iters): """ Gradient descent algorithm Input: alpha(float): learning rate num_iters(int): number of iterations theta(ndarray): shape(n+1,) initial value X(ndarray): shape(m,n) y(ndarray): shape(m,) Output: theta_final(ndarray): shape(n+1,) optimized value J_history(list) : list containing J at each iteration """ m,_=X.shape J_history=[] theta_final=theta.copy() for i in range(num_iters): h_x=np.dot(X,theta_final) theta_final=theta_final-(alpha/m)*np.dot(X.T,h_x-y).reshape(-1,1) J_history.append(cost_function(theta_final,X,y)) return theta_final,J_history theta_final,J_history=gradient_descent(ones_X_normalized,y_train,alpha=0.01,num_iters=1000) print("Theta final:",theta_final) plt.plot(J_history,'r') plt.xlabel('Number of iterations') plt.ylabel('Cost J') plt.show() def predict(theta,X,mu,sigma): """ Predict house price using linear regression model Input: theta(ndarray): shape(n+1,) optimized value from gradient descent algorithm mu(ndarray): mean values used for normalization sigma(ndarray): standard deviation values used for normalization x(ndarray) : input features unnormalized Output: pred_y(float) : predicted house price """ x_normlized=(x-mu)/sigma ones_x=add_ones(x_normlized.reshape(1,-1)) pred_y=np.dot(ones_x,theta).flatten()[0] return pred_y x=[5.,20.,4.,500.,15.,10.,6.,200.,5.] pred_y=predict(theta_final,x,mu,sigma) print("Predicted house price: ",pred_y)<|repo_name|>MohamedMoneer/ML<|file_sep|>/Logistic_Regression.py # -*- coding: utf-8 -*- """ Created on Tue Oct 15 11:41:31 2019 @author: moham """ import numpy as np import pandas as pd from sklearn import datasets from matplotlib import pyplot as plt df=datasets.load_iris() X=df.data[:,[2,3]] y=(df.target==2).astype(np.int) # import data from csv file #data=pd.read_csv('data.csv') #X=data.iloc[:, :-1].values #y=data.iloc[:, -1].values print('Shape X:',X.shape) print('Shape Y:',y.shape) def split_data(X,y): """Split data into training set(70%) and test set(30%). Input: X(ndarrya) :shape(m,n) y(ndarrya) :shape(m,) Output: x_train(xdarrya) :shape(int(m*0.7),n) x_test(xdarrya) :shape(int(m*0.3),n) y_train(xdarrya) :shape(int(m*0.7),) y_test(xdarrya) :shape(int(m*0.3),) """ m=len(y) idx=np.arange(m) np.random.shuffle(idx) #split data m_train=int(m*0.7) x_train=X[idx[:m_train],:] x_test=X[idx[m_train:],:] y_train=y[idx[:m_train]] y_test=y[idx[m_train:]] return x_train,x_test,y_train,y_test x_train,x_test,y_train,y_test=split_data(X,y) print('Shape xtrain:',x_train.shape) print('Shape xtest:',x_test.shape) def normalize_data(x): """Normalize data Input: x(xdarrya) :shape(m,n) Output: x_normalized(xdarrya) :shape(m,n) mu(xdarrya) :shape(1,n) mean values sigma(xdarrya) :shape(1,n) standard deviation values """ m,n=x.shape mu=np.mean(x,axis=0).reshape(1,-1) sigma=np.std(x,axis=0).reshape(1,-1) x_normalized=(x-mu)/sigma return x_normalized,mu,sigma x_normlized,mu,sigma=normalize_data(x_train) print('Shape mu:',mu.shape) print('Shape sigma:',sigma.shape) def initialize_theta(n): """Initialize theta Input: n(integer) : number of features Output: theta(xdarrya) :shape(n+1,) """ theta=np.zeros(n+1).reshape(-1,1) return theta theta=initialize_theta(x_normlized.shape[1]) print('Shape theta:',theta.shape) def add_ones(x): """Add ones as first column Input: x(xdarrya) :shape(m,n) Output: ones_x(xdarrya) :shape(m,n+1) """ m,n=x.shape ones_x=np.ones((m,n+1)) ones_x[:,1:]=x return ones_x ones_x_normlized=add_ones(x_normlized) print('Shape ones_x_normlized:',ones_x_normlized.shape) def sigmoid(z): """Compute sigmoid function Input: z(float or xdarrya or numpy array float or xdarrya ) s=np.array([-10,-5,-5,-4,-3,-4,-4,-5]) sigmoid(s) def cost_function(theta,x,y): """Compute cost function Input: theta(xdarrya ) :shape(n+1,) x(xdarrya ) :shape(m,n ) y(xdarrya ) :shape(m,) Output: J(float ) """ m,_=x.shape h_x=sigmoid(np.dot(x,theta)) first_term=-y*np.log(h_x).T second_term=(1-y)*np.log(1-h_x).T J=np.sum(first_term-second_term)/m