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dev_documentations.md

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File dev_generate_docs.py (.py)

  • extract_info_from_file : Extrait les informations pertinentes (docstrings, fonctions, classes) d'un fichier Python, et le contenu pour les fichiers CSS et JavaScript, tout en ignorant les types de fichiers spécifiés.

Args: file_path (str): Le chemin complet vers le fichier.

Returns: dict: Un dictionnaire contenant les informations extraites.

  • generate_documentation_md : Génère un fichier Markdown contenant la documentation de tous les fichiers dans un répertoire, en ignorant les répertoires spécifiés et les types de fichiers spécifiques.

Args: directory_path (str): Le chemin du répertoire contenant les fichiers. output_file (str): Le chemin du fichier de sortie Markdown.

File LICENSE ()

File Scrypt_projet with docs tring.py (.py)

  • load_data : Load the dataset and set the working directory.

Returns: pd.DataFrame: The loaded dataset.

  • rename_variables : Rename the columns of the dataframe.

Args: dfp (pd.DataFrame): The original dataframe.

Returns: pd.DataFrame: The dataframe with renamed columns.

  • data_overview : Display basic information and descriptive statistics of the dataframe.

Args: dfp (pd.DataFrame): The dataframe to be described.

  • plot_qualitative_distributions : Plot pie charts for qualitative variables.

Args: dfp (pd.DataFrame): The dataframe with qualitative variables.

  • plot_countplots : Plot countplots for specified variables.

Args: dfp (pd.DataFrame): The dataframe to plot.

  • missing_values_analysis : Analyze missing values in the dataframe.

Args: dfp (pd.DataFrame): The dataframe to analyze.

Returns: pd.DataFrame: A dataframe with the percentage of missing values.

  • plot_missing_values_histogram : Plot histogram of missing values for specified variables.
  • plot_outlier_distributions : Plot histograms and boxplots for continuous variables.

Args: dfp (pd.DataFrame): The dataframe with continuous variables.

  • impute_missing_values : Impute missing values for categorical and continuous variables.

Args: dfp (pd.DataFrame): The dataframe with missing values.

Returns: pd.DataFrame: The dataframe with imputed values.

  • transform_categorical_to_dummies : Transform categorical variables into dummy variables.

Args: dfp (pd.DataFrame): The dataframe with categorical variables.

Returns: pd.DataFrame: The dataframe with dummy variables.

  • correlation_matrix : Compute and display the correlation matrix.

Args: dfp2 (pd.DataFrame): The dataframe for which to compute the correlation matrix.

  • drop_column : Drop a specified column from the dataframe.

Args: dfp2 (pd.DataFrame): The dataframe from which to drop the column. column (str): The column to drop.

Returns: pd.DataFrame: The dataframe with the column dropped.

  • prepare_modeling_data : Prepare the data for modeling by separating the dependent and explanatory variables.

Args: dfp2 (pd.DataFrame): The dataframe to prepare.

Returns: tuple: Tuple containing the matrices X (explanatory) and Y (dependent).

  • split_data : Split the data into training and testing sets.

Args: X (pd.DataFrame): The explanatory variables. Y (pd.Series): The dependent variable.

Returns: tuple: Tuple containing the training and testing sets (X_app, X_test, Y_app, Y_test).

  • logistic_regression_model : Train and evaluate a logistic regression model.

Args: X_app (pd.DataFrame): Training set of explanatory variables. Y_app (pd.Series): Training set of the dependent variable. X_test (pd.DataFrame): Testing set of explanatory variables. Y_test (pd.Series): Testing set of the dependent variable.

Returns: float: The accuracy of the model.

  • knn_model : Train and evaluate a k-NN model.

Args: X_app (pd.DataFrame): Training set of explanatory variables. Y_app (pd.Series): Training set of the dependent variable. X_test (pd.DataFrame): Testing set of explanatory variables. Y_test (pd.Series): Testing set of the dependent variable.

Returns: float: The accuracy of the model.

  • decision_tree_model : Train and evaluate a decision tree model.

Args: X_app (pd.DataFrame): Training set of explanatory variables. Y_app (pd.Series): Training set of the dependent variable. X_test (pd.DataFrame): Testing set of explanatory variables. Y_test (pd.Series): Testing set of the dependent variable.

Returns: float: The accuracy of the model.

  • random_forest_model : Train and evaluate a random forest model.

Args: X_app (pd.DataFrame): Training set of explanatory variables. Y_app (pd.Series): Training set of the dependent variable. X_test (pd.DataFrame): Testing set of explanatory variables. Y_test (pd.Series): Testing set of the dependent variable.

Returns: float: The accuracy of the model.

  • main : Main function to run the data analysis and modeling pipeline.

File Scrypt_projet.ipynb (.ipynb)

File Scrypt_projet.py (.py)

  • optimize_k : No docstring found.
  • my_custom_loss_func : No docstring found.