Algeria National Football Team: Squad, Achievements & Stats in CAF

Overview of the Algeria Football Team

The Algeria national football team, representing the North African country in international competitions, competes in the Africa Cup of Nations and FIFA World Cup qualifiers. The team plays in the CAF region, with a rich history and passionate fanbase. Managed by Djamel Belmadi since 2018, Algeria’s playing style is known for its tactical discipline and resilience.

Team History and Achievements

The Algerian football team was founded in 1957. They have achieved significant milestones, including winning the Africa Cup of Nations in 1990 and 2019. Notable seasons include their impressive run to the quarter-finals in the 1982 FIFA World Cup.

Current Squad and Key Players

The current squad features standout players like Riyad Mahrez, known for his playmaking abilities, and Baghdad Bounedjah, a prolific striker. Other key players include Ramy Bensebaini and Aïssa Mandi.

Lists & Rankings of Players

• Riyad Mahrez: Midfielder 🎰
• B Baghdad Bounedjah: Striker ✅
• Ramy Bensebaini: Defender 💡
• Aïssa Mandi: Defender ❌ (injury concerns)

Team Playing Style and Tactics

The Algerian team typically employs a 4-3-3 formation, focusing on strong defensive tactics and counter-attacks. Their strengths lie in their disciplined defense and strategic midfield play, while weaknesses may include occasional lapses in attacking creativity.

Tips & Recommendations for Betting Analysis

To analyze betting potential: focus on recent form, head-to-head records against opponents, and key player availability.

Interesting Facts and Unique Traits

Nicknamed “The Desert Foxes,” Algeria’s fanbase is known for its fervent support. Rivalries with Morocco are particularly intense due to historical tensions.

Frequently Asked Questions (FAQs)

What is Algeria’s current league position?

In international rankings, they are consistently ranked among the top teams in Africa.

Who is Algeria’s top scorer?

B Baghdad Bounedjah holds this title with numerous goals in both domestic leagues and international matches.

Comparisons with Other Teams

In comparison to other African teams like Nigeria or Senegal, Algeria stands out for its tactical discipline but sometimes lacks flair compared to more attacking teams.

Case Studies or Notable Matches

A notable match was their victory over Germany during the 2014 FIFA World Cup group stage match. This game highlighted their ability to compete against top-tier European teams.

Tables Summarizing Team Stats

Statistic	Data
Last Five Matches Form	W-W-L-W-D
Head-to-Head Record vs Egypt (2021)	L-W-D-L-W (African Nations Qualifiers)
Odds Against Senegal (upcoming match)	+150/200/250*

Tips & Recommendations for Betting Insights

• Analyze recent performance trends before placing bets.
• Carefully consider player injuries or suspensions impacting team dynamics.
• Maintain awareness of home vs away performance differences when evaluating odds.

Detailed Step-by-Step Analysis Guide for Betting Potential

1. Evaluate recent form by reviewing last five games results.
2. Analyze head-to-head records against upcoming opponents to gauge competitive edge.
3. Cross-reference player statistics such as goals scored/conceded per game to assess strengths/weaknesses.
4. Maintain vigilance on team news updates regarding injuries/suspensions affecting squad depth prior to betting decisions.

Potential Pros & Cons of Current Team Form/Performance

• Potential Pros:
• Solid defensive setup providing stability ⚽️✅

• Potential Cons: int # Returns current marking ability level.
  GetTacklingTechnique() -> int # Returns current tackling technique level.

  ## Physical Skills

  Represents physical attributes such as strength,
  stamina etc., which are crucial aspects contributing towards overall physical prowess on-field gameplay scenarios like soccer matches etc…

  yaml
  —
  # Physical Skills Definition

  # Description:
  # Represents physical attributes such as strength,
  # stamina etc., which are crucial aspects contributing towards overall physical prowess on-field gameplay scenarios like soccer matches etc…

  Strength int Min=0 Max=100 Description=”Strength ranging between zero to one hundred.”
  Stamina int Min=0 Max=100 Description=”Stamina ranging between zero to one hundred.”

  GetStrength() -> int # Returns current strength level.
  GetStamina() -> int # Returns current stamina level.

  —

  This documentation provides comprehensive details about how skills can be managed using our system’s API.

  !!! Note that there might be syntax errors due to YAML formatting constraints; ensure proper indentation where necessary !!!

  — !u!

  “””
  Utility functions related to data handling for machine learning tasks.
  “””

  import json

  class DataHandlerException(Exception):
  “””Custom exception class for handling data-related errors.”””
  pass

  def parse_json(json_string):
  “””
  Parse a JSON string into a Python dictionary safely.

  Parameters:
  json_string (str): JSON formatted string.

  Returns:
  dict: Parsed JSON data as dictionary if valid JSON is provided; raises DataHandlerException otherwise.

  Raises DataHandlerException if JSON parsing fails due to invalid format or content issues.

  Examples::
  >>> parse_json(‘{“key”:”value”}’)
  {‘key’: ‘value’}
  >>> parse_json(‘Invalid JSON’)
  Traceback (most recent call last):
  …
  DataHandlerException: Failed to parse JSON string due to invalid format/content issues…

  Notes::
  – Ensure that input is properly formatted JSON string before calling this function;
  otherwise it will raise DataHandlerException indicating parsing failure reasons explicitly via traceback messages provided above…
  – This function aims at providing robust error handling mechanisms when dealing with potentially malformed/unexpected inputs during runtime execution phases…

  See Also::
  – json.loads(): Standard library function used internally here within try-except blocks encapsulating error catching logic…
  – DataHandlerException class definition providing custom exception type tailored specifically towards handling data-related issues encountered during execution phases…

  “””

  try:
  return json.loads(json_string)

  except json.JSONDecodeError as e:
  raise DataHandlerException(f’Failed to parse JSON string due to invalid format/content issues…’) from e

  def read_player_data(file_path):
  “””

  Parameters:

  file_path:str

  Returns:

  player_data:list

  Raises:

  DataHandlerException

  Examples:

  >>> read_player_data(‘players.json’)
  [
  {‘Name’: ‘Robert Lewandowski’, ‘Age’: ’32’, ‘Overall’: ’94’, …},
  {‘Name’: ‘Kevin De Bruyne’, ‘Age’: ’30’, ‘Overall’: ’92’, …}
  ]
  >>> read_player_data(‘invalid_path.json’)
  Traceback (most recent call last):
  …
  DataHandlerException: Unable access/read file at specified path due missing file/directory permission issues…

  Notes:

  * Ensure file exists at specified location/path before calling this function;
  otherwise it will raise DataHandlerException indicating access/read failure reasons explicitly via traceback messages provided above…
  * This function aims at providing robust error handling mechanisms when dealing with potentially missing/unreachable files during runtime execution phases…

  See Also:

  * open(): Standard library function used internally here within try-except blocks encapsulating error catching logic…
  * DataHandlerException class definition providing custom exception type tailored specifically towards handling data-related issues encountered during execution phases…

  “””
  try:
  with open(file_path,’r’)as f_handle:n player_raw_data=f_handle.read()n parsed_player_data=parse_json(player_raw_data)n return parsed_player_datanexcept FileNotFoundError:n raise DataHandlerException(f’Unable access/read file at specified path due missing file/directory permission issues…’)from Nonenexcept IOError:n raise DataHandlerException(f’IO Error occurred while attempting read operation at specified path…’)from Nonenndef filter_players_by_overall(players_list:int_threshold:int)->List:n “””nnFilter players based on minimum overall rating threshold.nnParameters:nnplayers_list:list Contains dictionaries each representing individual player.nint_threshold:int Minimum required overall rating threshold.nnReturns:nnfiltered_players:list Containing only those players whose overall rating meets/exceeds given threshold.nnRaises:nNonennExamples:n>>> filter_players_by_overall(players_list=[{‘Name’:’Robert Lewandowski’,’Overall’:’94’},{‘Name’:’Kevin De Bruyne’,’Overall’:’92’}],int_threshold=90)n[n{‘Name’:’Robert Lewandowski’,’Overall’:’94’}n]n>>> filter_players_by_overall(players_list=[{‘Name’:’Robert Lewandowski’,’Overall’:’94’},{‘Name’:’Kevin De Bruyne’,’Overall’:’92’}],int_threshold=95)n[]nnNotes:nThe function assumes that each player dictionary contains an ‘Overall’ key holding numeric/string representation convertible into integer type.nThe implementation focuses on providing efficient filtering mechanism through list comprehensions ensuring concise yet effective code structure…nnSee Also:none(): Built-in Python function utilized here implicitly via list comprehension approach…ndataclasses.asdict(): Potential utility if working directly with dataclass instances instead standard dictionaries…ndataclasses.field(default_factory=list): Alternative approach using default factory pattern when initializing empty lists…ndataclasses.dataclass(): Decorator defining simple classes primarily aimed storing immutable data structures efficiently…ndataclasses.field(metadata={“default”:None}): Utilizing metadata field options allowing further customization behavior initialization/default values…ndataclasses.field(default_factory=lambda:xrange(10)): Demonstrating usage default factory pattern initializing iterable range objects dynamically…ndataclasses.field(repr=False): Suppress representation output whenever utilizing repr method built-in classes/data structures…ndataclasses.field(hash=None): Customizable hashing behavior control leveraging hash parameter configurations within field definitions…ndataclasses.field(compare=False): Toggle comparison behavior enabling/disabling automatic comparisons generated fields automatically based upon presence absence respective parameters configurations…ndataclasses.field(init=True): Enabling/disabling automatic inclusion generated fields initialization constructor methods respectively depending upon init parameter settings provided field definitions accordingly…ndataclasses.field(default_factory=set):tUtilize default factory pattern initializing empty sets dynamically based upon configuration settings provided field definitions accordingly….ndataclasses.Field(…):tBase class allowing further subclass customization extending functionality base Field class through inheritance mechanisms accordingly…ndatatypes.Field(…):tAlternative base class offering similar capabilities extended functionalities beyond basic Field implementations through inheritance patterns accordingly…”

  filter_criteria=lambda p:p[‘Overall’]>=int_threshold return [playerfor playerin players_listif filter_criteria(player)]

  “””
  This module includes utility functions related specifically processing/loading reading manipulating various types structured datasets typically encountered during machine learning tasks involving predictive modeling analytics purposes.

  “””

  import pandas as pd

  def load_dataset_from_csv(csv_file_path:str)->pd.DataFrame:

  try:

  with open(csv_file_path,’r’)as f_handle:

  csv_content=f_handle.read()

  df=pd.read_csv(pd.compat.StringIO(csv_content))

  return df

  except FileNotFoundError:

  raise ValueError(f’CSV File not found at specified path:{csv_file_path}’)from None

  except pd.errors.EmptyDataError:

  raise ValueError(f’CSV File appears empty/no valid content found at specified path:{csv_file_path}’)from None

  except Exceptionas e:

  raise RuntimeError(f’Unexpected error occurred while attempting load CSV dataset from path:{csv_file_path}. Original Error Message:\\{str(e)}’)from e

  def compute_roc_auc(y_true:list,y_scores:list)->float:

  try:

  auc_score=roc_auc_score(y_true,y_scores)

  return auc_score

  except ValueErroras e:

  raise ValueError(f’Invalid input values encountered calculating ROC-AUC Score.Exception Message:\\{str(e)}’)from e

  def compute_geometric_mean(values:list)->float:

  try:

  g_mean=gmean(values)

  return g_mean

  except ValueErroras e:

  raise ValueError(f’Invalid input values encountered calculating Geometric Mean.Exception Message:\\{str(e)}’)from e

  “””Utility functions aiding common preprocessing tasks associated feature engineering operations typically performed prior training machine learning models.”””

  import numpy as np

  def normalize_features(feature_matrix:numpy.ndarray,axis:int=-1)->numpy.ndarray:

  “””

  Normalize features using L2 normalization along specified axis.

  Parameters:

  feature_matrix:numpy.ndarray Input feature matrix where rows represent samples/features columns represent individual features.

  axis:int Axis along which normalization should be performed.Defaults:-1 corresponding last dimension.

  Returns:

  normalized_matrix:numpy.ndarray Normalized feature matrix maintaining original shape.

  Examples:

  >>> feature_matrix=np.array([[1., 2., 3.],[4., 5.,6.]])>>> normalize_features(feature_matrix)

  array([[0.26726124,

  0.53452248,

  0.80178373],

  [0.45584231,

  0.56980288,

  0.68376346]])

  Notes:

  * Function assumes non-negative feature values prior normalization; negative values could lead unexpected results unless handled separately preprocessing stages…
  * Normalization process involves dividing each element by L2 norm calculated along specified axis retaining original shape dimensions thereby ensuring compatibility subsequent model training/inference stages…

  See Also:

  np.linalg.norm(): Compute vector norms matrix norms along specified axis utilizing powerful linear algebra capabilities provided NumPy library…

  “””

  norm_factor=np.linalg.norm(feature_matrix,axis=axis,nord=’fro’)

  if norm_factor==0:return feature_matrix.copy()

  normalized_matrix=np.divide(feature_matrix,norm_factor.reshape(-1)+axis)

  return normalized_matrix.copy()

  def encode_categorical_variables(df:pandas.DataFrame,categorical_columns:list=None)->pandas.DataFrame:

  if categorical_columnsisNone:categorical_columns=df.select_dtypes(include=[‘object’,’category’]).columns.tolist()

  encoded_df=pd.get_dummies(df,prefix_sep=’:’,columns=categorical_columns)

  return encoded_df

  “””
  Utility functions facilitating evaluation model performance prediction tasks involving classification regression problems commonly addressed machine learning pipelines.

  “””

  import sklearn.metrics

  def compute_classification_metrics(y_true:list,y_pred:list,y_probabilities:list=None)->dict:

  metrics_dict={}

  metrics_dict[‘accuracy’]=sklearn.metrics.accuracy_score(y_true,y_pred)

  metrics_dict[‘precision’],metrics_dict[‘recall’],metrics_dict[‘f1-score’],_sklearn.metrics.precision_recall_fscore_support(y_true,y_pred)

  if y_probabilitiesisnotNone:

  metrics_dict[‘roc-auc’]=sklearn.metrics.rocauc_score(y_true,y_probabilities)

  else:

  metrics_dict[‘roc-auc’]=None

  return metrics_dict

  “””
  Utility functions assisting hyperparameter optimization processes typically involved tuning machine learning models achieving optimal performance metrics validation datasets.

  “””

  import itertools

  def generate_hyperparameter_grid(param_grid:nesteddict())->iterable:

  for keys_valuesin itertools.product(*param_grid.values()):

  params=dict(zip(param_grid.keys(),keys_values))

  yield params

  “””

  Utility functions supporting various tasks related model serialization deserialization loading saving trained machine learning models facilitating deployment production environments.

  “””

  import joblib

  def save_model(model:any,file_path:str)->bool:

  try:

  joblib.dump(model,file_path)

  return True

  except Exceptionas e:

  print(f’Failed save model due following error:\\{str(e)}’)

  return False

  def load_model(file_path:str)->any:

  try:

  model_loaded=joblib.load(file_path)

  return model_loaded

  except FileNotFoundError:

  print(f’Model file not found at specified path:{file_path}’)

  return None

  except Exceptionas e:

  print(f’Failed load model due following error:\\{str(e)}’)

  return None

  “””
  Utility functions enhancing logging debugging capabilities facilitating tracking monitoring processes involved training evaluating machine learning models capturing detailed insights operational behaviors systems.

  “””

  import logging

  logging.basicConfig(level=logging.INFO,filename=’ml_utilities.log’,filemode=’w’,format=’%(asctime)s:%(levelname)s:%(message)s’)

  logging.info(‘Starting main script execution…’)

  “””Utilities designed aid various stages typical data science workflow including data exploration preparation transformation analysis visualization reporting tasks.”””

  import matplotlib.pyplot as plt

  import seaborn as sns

  class PlotterUtils(object):

  “””Helper class encapsulating plotting functionalities commonly required visualizing analyzing datasets statistical distributions relationships among variables.”””

  @staticmethod

  def plot_histogram(data_series,name_of_variable,bins:int=50,color:str=’blue’)->matplotlib.axes.Axes:

  fig,axis=plt.subplots()

  axis.hist(data_series,bins=bins,color=color,alpha=0.7)

  axis.set_title(name_of_variable+’ Distribution’)

  axis.set_xlabel(name_of_variable)

  axis.set_ylabel(‘Frequency’)

  plt.tight_layout()

  plt.show()

  return axis

  @staticmethod

  def plot_scatter(x_values,y_values,x_label:str=”,y_label:str=”,title:str=”,color_map:str=’viridis’)->matplotlib.axes.Axes:

  fig,axis=plt.subplots()

  sc=sns.scatterplot(x=x_values,y=y_values,hue=y_values,cmap=color_map)

  axis.set_title(title)

  axis.set_xlabel(x_label)

  axis.set_ylabel(y_label)

  plt.tight_layout()

  plt.show()

  return sc.get_figure().gca()

  @staticmethod

  def plot_boxplot(data_frame,column_to_plot,title:str=”,color_palette:sns.palettes.ColorPalette=[‘pastel’]):

  fig,axis=plt.subplots()

  sns.boxplot(data=data_frame,x=data_frame.columns,column_to_plot,color=sns.color_palette(color_palette))

  axis.set_title(title)

  plt.tight_layout()

  plt.show()

  “””

  This module includes utility functions aimed supporting various stages typical data science workflow including data exploration preparation transformation analysis visualization reporting tasks.

  “””

  class DataFrameUtils(object):

  “””Helper class encapsulating functionalities frequently utilized manipulating Pandas DataFrame objects.”””

  @staticmethod

  def fill_missing_values(df:pandas.DataFrame,strategy:str=’mean’,fill_value=None)->pandas.DataFrame:

  “””Fill missing values in DataFrame according given strategy or specific fill_value.”””

  if strategy==’mean’:

  df.fillna(df.mean(),inplace=True)

  elif strategy==’median’:

  df.fillna(df.median(),inplace=True)

  elif strategy==’mode’:

  df.fillna(df.mode().iloc[:,0],inplace=True)

  elif strategy==’constant’:

  if fill_valueisnotNone:

  df.fillna(fill_value,inplace=True)

  else:

  raise ValueError(‘Fill value must be provided when strategy is constant.’)

  else:

  raise ValueError(‘Unsupported fill strategy.’)

  df_in=df.copy(deep=True)

  if isinstance(strategy,(list,np.ndarray,pd.Series,pd.DataFrame)):# check if user passed custom fill values directly.

  for column,valuein zip(df_in.columns,strategy):

  if isinstance(value,(float,int,str)): # single value case.

  df_in[column].fillna(value,inplace=True)

  else:# iterable case.

  values=value.tolist() # ensure we’re working with list even if numpy array passed.

  assert len(values)==len(df_in), f’The length of fill values ({len(values)}) does not match number columns ({len(df_in)}).’

  for col,valin zip(df_in.columns,values):

  df_in[col].fillna(val,inplace=True)

  else:# any other case assume scalar value meant apply uniformly across all columns.

  df_in.fillna(strategy,inplace=True)

  assert df.shape==df_in.shape,f’Shape mismatch after filling missing values ({df.shape}) vs expected ({df_in.shape}).’

  assert df.isnull().sum().sum()==0,f’Missing values still present after filling ({df.isnull().sum().sum()} remaining).’

  assert df.equals(df_in),f’DataFrames do not match after filling operation.’

  return df_in

  @staticmethod

  def encode_categorical_variables(df:pandas.DataFrame,categorical_columns:list=None)->pandas.DataFrame:

  “””Encode categorical variables using one-hot encoding scheme.”””

  if categorical_columnsisNone:categorical_columns=df.select_dtypes(include=[‘object’,’category’]).columns.tolist()

  encoded_df=pd.get_dummies(df,prefix_sep=’:’,columns=categorical_columns)

  assert df.shape==encoded_df.shape,f’Shape mismatch after encoding ({df.shape}) vs expected ({encoded_df.shape}).’

  assert df.equals(encoded_df.iloc[:,:len(df.columns)]),f’DataFrames do not match after encoding operation.’

  return encoded_df

  @staticmethod

  def normalize_features(feature_matrix:numpy.ndarray,axis:int=-1,scale_type:str=’standard’)->numpy.ndarray:

  “””Normalize features using specified scaling method along given axis.”””

  valid_scales={‘standard’,’minmax’}

  assert scale_typein valid_scales,f’Scale type must be one of {valid_scales}, got “{scale_type}” instead.’

  norm_feature_matrix=numpy.zeros_like(feature_matrix,dtype=float)# Initialize output array same shape dtype.

  if scale_type==’standard’:

  means=numpy.mean(feature_matrix,axis=axis).reshape(-1+axis)# Compute means along axis preserving dimensions….

  stds=numpy.std(feature_matrix,axis=axis).reshape(-1+axis)# Compute std deviations similarly….

  norm_feature_matrix=(feature_matrix-means)/stds+((stds==0)*means)# Apply standardization avoiding division by zero….

  elif scale_type==’minmax’:

  mins=numpy.min(feature_matrix,axis=axis).reshape(-1+axis)# Compute mins along axis preserving dimensions….

  maxs=numpy.max(feature_matrix,axis=axis).reshape(-1+axis)# Compute maxes similarly….

  ranges=maxs-mins+((maxs==mins)*numpy.ones_like(maxs))# Calculate ranges avoiding division by zero….

  norm_feature_matrix=(feature_matrix-mins)/ranges+(ranges==numpy.zeros_like(ranges))*mins/((maxs!=mins)*numpy.ones_like(maxs))+# Apply min-max scaling avoiding division by zero.

  assert numpy.allclose(numpy.max(norm_feature_matrix,numpy.inf),numpy.ones_like(norm_feature_array)),f’Maximum value after min-max scaling exceeds expected range.’

  assert numpy.allclose(numpy.min(norm_feature_array,-numpy.inf),numpy.zeros_like(norm_feature_array)),f’Minimum value after min-max scaling below expected range.’

  assert norm_feature_array.dtype==float,f’Output array dtype should be float but got “{norm_feature_array.dtype}” instead.’

  assert norm_feature_array.shape==feature_array.shape,f’Shape mismatch after normalization ({norm_feature_array.shape}) vs expected ({feature_array.shape}).’

  return norm_feature_array

  @staticmethod

  class FeatureSelector(object):

  “””Class designed selecting relevant features based statistical criteria correlation thresholds importance scores.”””

  FeatureSelector.__init__(self,dataframe:pandas.DataFrame,target_column:string,max_num_features:int=None,min_corr_threshold:number=None,max_corr_threshold:number=None,max_importance:number=None,min_importance:number=None):

  self._dataframe=self._validate_dataframe(dataframe)

  self._target_column=self._validate_target_column(target_column,self._dataframe)

  self._num_features=self._dataframe.drop(columns=[self._target_column]).shape[-1] # Number columns excluding target column.

  self._max_num_features=max(self._num_features,max_num_features) if max_num_featuresisnotNone else self._num_features # Maximum number features allowed selection.

  self._min_corr_threshold=min(min_corr_threshold,-number.MAX_VALUE) if min_corr_thresholdisnotNone else number.MIN_VALUE # Minimum correlation threshold allowed selection.

  self._max_corr_threshold=max(max_corr_threshold,number.MAX_VALUE) if max_corr_thresholdisnotNone else number.MIN_VALUE # Maximum correlation threshold allowed selection.

  self._max_importance=max(self.feature_importances_,max_importance) if max_importanceisnotNone else self.feature_importances_.max(axis=-1) # Maximum importance score allowed selection.

  self._min_importance=min(min_importance,number.MIN_VALUE) if min_importanceisnotNone else self.feature_importances_.min(axis=-1) # Minimum importance score allowed selection.

  FeatureSelector.fit(self):

  “””Fit FeatureSelector instance computing correlations importance scores determining relevance features target variable.”””

  features=self._dataframe.drop(columns=[self.target_column])

  target=self.target_column

  correlation_coefficients=pandas.Series(numpys.corrcoef(features.T,target)[features.columns,:].diagonal())#,index=self.features_names_)

  correlation_coefficients.abs().sort(inplace=True)# Sort absolute correlation coefficients descending order ignoring signs.

  feature_names_sorted_by_correlation=pandas.Index(correlation_coefficients.index,data=numpy.arange(len(correlation_coefficients)))#,dtype=str)

  selected_indices=(correlation_coefficients.abs()>self.min_correlation_)&(correlation_coefficients.abs()<self.max_correlation_)&((feature_names_sorted_by_correlation<=self.max_num_features_)|((feature_names_sorted_by_correlationself.min_correlations_))))&(((correlations_<self.max_correlations_)|(correlations_self.min_correlations_))))&((names_sorted<=len(names_sorted)&(names_sorted<=len(names_sorted)&(names_sorted=min_imp_scores)|(imp_scores=min_imp_scores))))&(((imp_scores<=max_imp_scores)|(imp_scores<=(len(imp_scores)&(imp_scoresList[str]:

  “””Transform new DataFrame selecting relevant features previously determined fit step.”””

  new_dataframe=self.validate_dataframe(new_dataframe)

  new_dataframe.drop(columns=[target_column],errors=’ignore’)

  new_dataframe=new_dataframe[self.features_names_[indices_selected]]

  new_dataframe=new_dataframe[self.features_names_[indices_selected]]

  new_dataframe=new_dataframe[self.features_names_[indices_selected]]

  new_dataframe=new_dataframe[self.features_names_[indices_selected]]

  new_dataframe=new_dataframe[self.features_names_[indices_selected]]

  new_dataframe=new_dataframe[self.features_names_[indices_selected]]

  new_dataframe=new_dataframe[self.features_names_[indices_selected]]

  features_new=pandas.concat([new dataframe],join_axes=[index_new])

  features_new=pandas.concat([features_new],join_axes=[index_new])

  features_new=pandas.concat([features_new],join_axes=[index_new])

  features_new=pandas.concat([features_new],join_axes=[index_new])

  features_new=pandas.concat([features_new],join_axes=[index_new])

  features_new=pandas.concat([features_new],join_axes=[index_new])

  names_transformed=list(features_transformed.columns)

  names_transformed=list(features_transformed.columns)

  names_transformed=list(features_transformed.columns)

  names_transformed=list(features_transformed.columns)

  names_transformed=list(features