Premier League stats & predictions Premier League Kuwait 4

Explore the Thrills of the Kuwait Premier League with Expert Betting Insights

As a passionate football fan, keeping up with the latest developments in the Kuwait Premier League is essential. This guide provides you with comprehensive insights into each match, ensuring you never miss a beat. From detailed analysis to expert betting predictions, we've got you covered every step of the way. Discover how to stay ahead of the game and make informed decisions for every match day.

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• Instagram# -*- coding: utf-8 -*- # Copyright (c) Jupyter Development Team. # Distributed under the terms of the Modified BSD License. import os from nbconvert.preprocessors import ExecutePreprocessor from traitlets.config import Config class MyExecutePreprocessor(ExecutePreprocessor): def __init__(self): super(MyExecutePreprocessor,self).__init__() self.allow_errors = True self.timeout=60000 self.kernel_name = 'python' self.capturing = False self.disable_caching = True def preprocess(self,nb,**kwargs): if 'code' not in kwargs: kwargs['code'] = [] # Add cell magics magic_code = """ # %load_ext autoreload # %autoreload # %matplotlib inline""" kwargs['code'].insert(0,magic_code) return super(MyExecutePreprocessor,self).preprocess(nb,**kwargs) class NotebookManager(object): def __init__(self): self.preprocessor = MyExecutePreprocessor() def execute_notebook(self,nb_path,output_path=None): # Load notebook object self.nb = nbformat.read(nb_path,'json') # Execute notebook object self.preprocessor.preprocess(self.nb) # Save notebook object if output_path == None: nbformat.write(self.nb,nb_path) return None else: nbformat.write(self.nb,output_path) return output_path if __name__ == '__main__': NB_PATH = os.path.abspath(os.path.join(os.getcwd(),'test.ipynb')) OUTPUT_PATH = os.path.abspath(os.path.join(os.getcwd(),'test_out.ipynb')) NB_MANAGER = NotebookManager() NB_MANAGER.execute_notebook(NB_PATH,output_path=OUTPUT_PATH)<|repo_name|>kristianmonrad/ML_project<|file_sep|>/scripts/generate_embeddings.py import os import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from joblib import Parallel,delayed def process_file(i): print('Processing file {}...'.format(i)) # Get file name without extension file_name_no_ext = i.replace('.csv','') # Read csv file into dataframe df = pd.read_csv(i) # Extract text column from dataframe text_data = df['text'].values # Fit vectorizer model tfidf_vectorizer = TfidfVectorizer() tfidf_vectorizer.fit(text_data) # Transform text data using fitted vectorizer model tfidf_data = tfidf_vectorizer.transform(text_data) # Write transformed data as sparse matrix file (.npz) sparse_matrix_filename = file_name_no_ext + '.npz' sparse_matrix_filepath = os.path.join('sparse_matrices',sparse_matrix_filename) print('Writing sparse matrix file {}...'.format(sparse_matrix_filepath)) sparse_matrix_filepath = tfidf_data.save(sparse_matrix_filepath) return sparse_matrix_filepath if __name__ == '__main__': # Create directory for storing sparse matrices if not os.path.exists('sparse_matrices'): os.mkdir('sparse_matrices') # Get list of csv files located under data/raw/csv_files/ directory file_list = [] for root_dir,folders,file_names in os.walk('data/raw/csv_files'): for i in file_names: file_list.append(os.path.join(root_dir,i)) # Parallelize processing files sparse_matrices_filepath_list = Parallel(n_jobs=-1)(delayed(process_file)(i) for i in file_list)<|repo_name|>kristianmonrad/ML_project<|file_sep|>/scripts/plot_cluster_distributions.py import matplotlib.pyplot as plt import pandas as pd from scipy.stats import chi2_contingency def plot_cluster_distributions(cluster_label,data_column,title='Cluster distribution',xlabel='Cluster label',ylabel='Count',output_file=None): """ Plots cluster distributions Input: cluster_label (pd.Series): Series containing cluster labels data_column (pd.Series): Series containing data column used for clustering title (str): Title for plot xlabel (str): Label for x-axis ylabel (str): Label for y-axis output_file (str): Path to output file Output: pd.DataFrame: Contingency table containing cluster distributions """ cluster_label_series = pd.Series(cluster_label,name='Cluster label') data_column_series = pd.Series(data_column,name='Data column') df = pd.concat([cluster_label_series,data_column_series],axis=1) contingency_table = df.groupby(['Cluster label','Data column']).size().unstack().fillna(0) contingency_table.columns.name=None crosstab_result_df = pd.DataFrame(columns=['Chi-square','P-value']) crosstab_result_df.loc['Result']=[chi2_contingency(contingency_table)[0],chi2_contingency(contingency_table)[1]] plt.figure(figsize=(8.27*1.5/2.54*1.5*(contingency_table.shape[0]+1),11.69/2.54*1.5)) plt.subplot(211) contingency_table.plot(kind='bar',rot=0,title=title) plt.xlabel(xlabel) plt.ylabel(ylabel) plt.subplot(212) crosstab_result_df.plot(kind='bar',rot=0,title='Chi-square test result') plt.xlabel('Result') plt.ylabel('Value') if output_file != None: plt.savefig(output_file,dpi=300,bbox_inches='tight') plt.show() return contingency_table<|file_sep|># Machine Learning Project The aim of this project was to implement machine learning methods capable of extracting relevant information from a large amount of text data. This was done by implementing various natural language processing methods such as topic modelling. The purpose was then use this extracted information as features for supervised machine learning methods. ## Structure The structure is based on [cookiecutter-data-science](https://drivendata.github.io/cookiecutter-data-science/) by [DrivenData](https://www.drivendata.org/) with some modifications. ## Setup Install [Anaconda](https://www.continuum.io/downloads). Clone repository: bash git clone https://github.com/kristianmonrad/ML_project.git ML_project && cd ML_project/ Create Anaconda environment: bash conda env create -f environment.yml Activate Anaconda environment: bash source activate ML_project_env ## Usage ### Run scripts Run script `generate_embeddings.py` which generates TF-IDF sparse matrices: bash python generate_embeddings.py --help usage: generate_embeddings.py [-h] [--n_jobs N_JOBS] optional arguments: -h, --help show this help message and exit --n_jobs N_JOBS number of jobs used when parallelizing execution (default: -1) Run script `process_notebooks.py` which executes Jupyter notebooks: bash python process_notebooks.py --help usage: process_notebooks.py [-h] [--notebook NB] optional arguments: -h, --help show this help message and exit --notebook NB path to notebook file (default: None) ### Run tests Run tests using pytest: bash pytest -v tests/ ## Requirements * [Anaconda](https://www.continuum.io/downloads) * [Python](https://www.python.org/) * [Jupyter](http://jupyter.org/) <|repo_name|>kristianmonrad/ML_project<|file_sep|>/environment.yml name: ML_project_env dependencies: - python=3.6 - ipython - jupyter - pandas - numpy - scikit-learn - scipy - nltk - seaborn - matplotlib - pytest - tqdm - joblib - nbconvert <|file_sep|># -*- coding: utf-8 -*- """ Created on Tue Mar @author: Kristian Monrad Johansen Description: Script containing functions related to topic modelling. """ import numpy as np import gensim from gensim.corpora import Dictionary def preprocess_corpus(corpus,min_word_count=10): print('Preprocessing corpus...') preprocessed_corpus=[] for document in corpus: preprocessed_document=[] for word_token in document: if word_token.count > min_word_count: preprocessed_document.append(word_token) preprocessed_corpus.append(preprocessed_document) return preprocessed_corpus def create_lda_model(corpus,num_topics): print('Creating LDA model...') dictionary=gensim.corpora.Dictionary(corpus) dictionary.filter_extremes(no_below=20