Tunisia football predictions today

Etoile Sportive du Sahel vs Club Africain

This classic rivalry promises an intense showdown on the pitch. With both teams vying for top position in the league, every moment is crucial. Our prediction analysis suggests a high-scoring game with Etoile Sportive du Sahel having a slight edge.

Tunisien Club vs Espérance Sportive de Tunis

In another thrilling encounter, Tunisien Club faces off against Espérance Sportive de Tunis. Known for their tactical prowess, both teams will be looking to assert dominance. Our experts predict a closely contested match with potential for an upset.

C.S.S (Club Sportif Sfaxien) vs Avenir Sportif de La Marsa

This match is set to be a tactical battle between two formidable opponents. C.S.S’s strong defense will be tested against Avenir Sportif de La Marsa’s attacking flair. Expect a strategic game with opportunities on both ends.

Etoile Sportive du Sahel vs Club Africain

• Prediction: Etoile Sportive du Sahel to win
• Total Goals: Over 2.5 goals
• Betting Tips: Consider backing Etoile Sportive du Sahel with a handicap due to their recent form.

Tunisien Club vs Espérance Sportive de Tunis

• Prediction: Draw
• Total Goals: Under 2.5 goals
• Betting Tips: A draw bet could be lucrative given both teams' defensive records this season.

C.S.S (Club Sportif Sfaxien) vs Avenir Sportif de La Marsa

• Prediction: C.S.S to win
• Total Goals: Over 1.5 goals
• Betting Tips: Back C.S.S with an Asian Handicap for better odds.

How often are predictions updated?

Predictions are updated daily to ensure you have the latest insights before each match starts.

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You can download our app for offline access to all predictions and analyses.

What should I consider when placing bets?

Beyond predictions, consider factors like odds value, bankroll management, and personal risk tolerance.

Are there any risks involved in betting?

Betting involves risks, so it’s important to gamble responsibly and only wager what you can afford to lose.

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< li >Instagram: @tunisianfootballpredictions < <|file_sep|># -*- coding: utf-8 -*- """ Created on Sun Dec @author: David Sandler """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import numpy as np import pandas as pd from keras.utils import Sequence class DataGenerator(Sequence): def __init__(self,data_df,batch_size=32,dim=(224,224),n_channels=1, n_classes=10,is_train=False,augment=False, shuffle=True): 'Initialization' self.dim = dim self.batch_size = batch_size self.data_df = data_df self.labels = data_df['label'].values self.list_IDs = np.arange(len(data_df)) self.n_channels = n_channels self.n_classes = n_classes self.shuffle = shuffle self.augment = augment if augment: self.augmentor = ImageDataGenerator( rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') if is_train: self.on_epoch_end() def __len__(self): 'Denotes the number of batches per epoch' return int(np.floor(len(self.data_df) / self.batch_size)) def __getitem__(self,index): 'Generate one batch of data' # Generate indexes of the batch indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size] # Find list of IDs list_IDs_temp = [self.list_IDs[k] for k in indexes] # Generate data X,y = self.__data_generation(list_IDs_temp) return X,y def on_epoch_end(self): 'Updates indexes after each epoch' self.indexes = np.arange(len(self.list_IDs)) if self.shuffle == True: np.random.shuffle(self.indexes) # 'Generates data containing batch_size samples' # # # Initialization # X = np.empty((self.batch_size,self.dim[0],self.dim[1],self.n_channels)) # y = np.empty((self.batch_size), dtype=int) # # # Generate data # list_IDs_temp = [self.list_IDs[k] for k in indexes] # # # Generate data # for i,ID in enumerate(list_IDs_temp): # # # Store sample # X[i,:] = np.load(os.path.join(self.img_dir,ID+'.npy')) # # # Store class # y[i] = self.labels[ID] # # return X,y <|repo_name|>davidsandler/Music-Classification<|file_sep|>/README.md # Music-Classification This project is focused on creating an end-to-end pipeline capable of classifying music genres from audio files using spectrograms as inputs. ## Structure The project is divided into several modules: ### Preprocessing This module contains functions used during preprocessing such as converting mp3 files into numpy arrays containing spectrograms. ### Model This module contains all code related to training models such as CNN architectures used during training. ### Inference This module contains code used during inference such as plotting confusion matrices. <|repo_name|>davidsandler/Music-Classification<|file_sep|>/model.py from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import timeit import numpy as np import pandas as pd import tensorflow.keras.backend as K from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.layers import Dropout from tensorflow.keras.layers import Flatten from tensorflow.keras.layers import Conv2D from tensorflow.keras.layers import MaxPooling2D from tensorflow.keras.optimizers import Adam from sklearn.metrics import confusion_matrix class MusicClassifier(): def __init__(self,img_dir): if not os.path.exists(img_dir): raise Exception('Directory does not exist') if not img_dir[-1] == '/': img_dir += '/' self.img_dir = img_dir def cnn_model(img_shape,n_classes): def train_model(X_train,y_train,X_val,y_val,n_epochs,n_batch,img_shape,n_classes): def test_model(X_test,y_test,model,img_shape,n_classes): def plot_confusion_matrix(cm,y_true,y_pred,class_names,title='Confusion matrix', cmap=plt.cm.Blues): def plot_metrics(history): <|repo_name|>davidsandler/Music-Classification<|file_sep|>/preprocessing.py from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import glob import numpy as np import matplotlib.pyplot as plt import librosa import librosa.display def mp3_to_npy(mp3_file,npy_file,sr=22050,duration=None): def get_spectrogram(audio,sr,duration=None): def plot_spectrogram(spectrogram,sr,duration=None): def convert_mp3_to_npy(mp3_dir,npy_dir,sr=22050,duration=None): <|repo_name|>LuizSiqueiraDev/realtime-monitoring-metrics-docker-kubernetes<|file_sep|>/docker-compose.yml version: "3" services: prometheus: image: prom/prometheus:v2.17.2 volumes: - ./prometheus.yml:/etc/prometheus/prometheus.yml command: - '--config.file=/etc/prometheus/prometheus.yml' - '--storage.tsdb.path=/prometheus' - '--web.console.libraries=/usr/share/prometheus/console_libraries' - '--web.console.templates=/usr/share/prometheus/consoles' ports: - "9090:9090" networks: monitoring-network: aliases: - prometheus grafana: image: grafana/grafana:7.1.5 environment: GF_AUTH_ANONYMOUS_ENABLED: true ports: - "3000:3000" networks: monitoring-network: aliases: - grafana cadvisor: image: google/cadvisor:v0.35.0 volumes: - /:/rootfs:ro,rslave # Required by cAdvisor itself. - /var/run:/var/run:rw,rslave # Required by cAdvisor itself. - /sys:/sys:ro,rslave # Required by cAdvisor itself. - /var/lib/docker/:/var/lib/docker:ro,rslave # Required by cAdvisor itself. ports: - "8080:8080" networks: monitoring-network: aliases: - cadvisor networks: monitoring-network:<|repo_name|>LuizSiqueiraDev/realtime-monitoring-metrics-docker-kubernetes<|file_sep|>/kubernetes/prometheus-pvc.yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: prometheus-pvc spec: accessModes: - ReadWriteOnce resources: requests: storage: "10Gi"<|file_sep|># realtime-monitoring-metrics-docker-kubernetes ## Install Docker: curl https://get.docker.com | bash - ## Start Docker: sudo systemctl start docker && sudo systemctl enable docker ## Create directory structure: mkdir realtime-monitoring-metrics-docker-kubernetes && cd realtime-monitoring-metrics-docker-kubernetes && mkdir docker && cd docker && mkdir metrics && mkdir metrics/prometheus && mkdir metrics/grafana && mkdir metrics/cadvisor && cd ../.. ## Install