Given this folder structure : 

MLOPS-CUSTOMER-PREDICTION/
├── api
├── configs
│   └── model_config.yaml
├── data
│   ├── processed
│   │   └── processed_data.csv
│   └── raw
│       ├── test.csv
│       └── train.csv
├── mlruns
│   ├── 0
│   ├── models
│   └── trash
├── notebooks
│   └── app.ipynb
├── src
│   ├── data
│   │   ├── __pycache__
│   │   ├── __init__.py
│   │   ├── data_loader.py
│   │   └── data_preprocessor.py
│   ├── models
│   │   ├── __init__.py
│   │   └── logistic_regression.py
│   └── utils
│       ├── __init__.py
│       ├── config.py
│       └── evaluation_metrics.py
├── tests
│   ├── __init__.py
│   ├── test_data_loader.py
│   ├── test_data_preprocessor.py
│   └── test_logistic_regression.py
├── .gitignore
├── app_v1.ipynb
├── Dockerfile
├── load.py
├── main.py
├── README.md
├── setup.py
└── train_with_conda.py
Q- as you can see we have a data that already processed oky : 

Q- and the path i think is already clear : 

Q- but for know i just use for the model training only the app_v1.ipynb 

Notebook , but as i am a software engineering i neeed to make my code more production ready as possible , 
And this why i think build a separate python file (.py) in the models folder is a good practice 

Q- before diving into more technical details what your option so far , 

You're on the right track! Moving the model training logic from the notebook to a dedicated Python script within the src/models directory is definitely a good practice for productionizing your ML workflow. Here's a breakdown of my thoughts and suggestions:



Q- this why i need to create for you this 

Whay form me i will provide you only the ones with need it context to to help me create the right project oky : 

First : i will give your only the configs.yml file oky : 
# File: configs/model_config.yaml


data:
  raw:
    train: "data/raw/train.csv"
    test: "data/raw/test.csv"
  processed:
    output: "data/processed/processed_data.csv"


model:
  type: "logistic_regression"
  parameters:
    C: 1.0
    max_iter: 100


evaluation:
  metrics:
    - "accuracy"
    - "precision"
    - "recall"
    - "f1"
    - "roc_auc"


And then : the data_loader.py logic 

# File: src/data/data_loader.py


import pandas as pd
import yaml
from pathlib import Path
from typing import Dict, Tuple


def load_config(config_path: str = "configs/model_config.yaml") -> Dict:
    """
    Load the configuration from the YAML file.
   
    Args:
        config_path (str): Path to the configuration file.
   
    Returns:
        Dict: Configuration dictionary.
   
    Raises:
        FileNotFoundError: If the configuration file is not found.
        yaml.YAMLError: If there's an error parsing the YAML file.
    """
    try:
        with open(config_path, 'r') as config_file:
            return yaml.safe_load(config_file)
    except FileNotFoundError:
        raise FileNotFoundError(f"Configuration file not found at {config_path}")
    except yaml.YAMLError as e:
        raise yaml.YAMLError(f"Error parsing the YAML configuration file: {e}")


def load_data(config: Dict) -> Tuple[pd.DataFrame, pd.DataFrame]:
    """
    Load train and test data from CSV files specified in the configuration.
   
    Args:
        config (Dict): Configuration dictionary containing file paths.
   
    Returns:
        Tuple[pd.DataFrame, pd.DataFrame]: Train and test DataFrames.
   
    Raises:
        FileNotFoundError: If either train or test CSV file is not found.
        pd.errors.EmptyDataError: If either CSV file is empty.
        Exception: For any other unexpected errors during data loading.
    """
    try:
        train_path = Path(config['data']['raw']['train'])
        test_path = Path(config['data']['raw']['test'])
       
        if not train_path.exists():
            raise FileNotFoundError(f"Train file not found at {train_path}")
        if not test_path.exists():
            raise FileNotFoundError(f"Test file not found at {test_path}")
       
        train_df = pd.read_csv(train_path)
        test_df = pd.read_csv(test_path)
       
        if train_df.empty:
            raise pd.errors.EmptyDataError("Train CSV file is empty")
        if test_df.empty:
            raise pd.errors.EmptyDataError("Test CSV file is empty")
       
        return train_df, test_df
   
    except pd.errors.EmptyDataError as e:
        raise pd.errors.EmptyDataError(f"Error: {e}")
    except Exception as e:
        raise Exception(f"An unexpected error occurred while loading the data: {e}")


if __name__ == "__main__":
    try:
        config = load_config()
        train_data, test_data = load_data(config)
        print("Data loaded successfully.")
        print(f"Train data shape: {train_data.shape}")
        print(f"Test data shape: {test_data.shape}")
    except Exception as e:
        print(f"Error: {e}")


Q- and aslo i will give you the path of the processed data 

Note  the processed data is the original data (merged (test , train)) but after applying the preprecoessing logic oky : 

Data\processed\processed_data.csv

And this  a sample for it 
Age,Tenure,Usage Frequency,Support Calls,Payment Delay,Total Spend,Last Interaction,Churn,Gender_Female,Gender_Male,Subscription Type_Basic,Subscription Type_Premium,Subscription Type_Standard,Contract Length_Annual,Contract Length_Monthly,Contract Length_Quarterly
-0.7533262960633177,0.448759583602762,-0.21051068470063652,0.4545491648153995,0.6096204846913078,1.24742680334121,0.293051884630808,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0
2.0596459214409184,1.0282779730087588,-1.7245624994616156,2.083099929058951,-0.6013186818522942,-0.3098645243738569,-0.9865836106995087,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0
1.2559395735825651,-1.0000363899122298,-1.3751659268244667,0.7802593176641097,0.6096204846913078,-1.8546975214672032,-1.335575109425959,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0


Q- next now i want to focus on the logistic_regression.py
Logic 

Q- but i want to be inspired by my work in my notebook 
Q- which in  fact build and train the model like this 

Q- as you can see i use also some part of power of mlflow here so 

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import mlflow
import mlflow.sklearn


def train_and_validate_model(X_train, X_test, y_train, y_test, model=LogisticRegression()):
    with mlflow.start_run():
        # Log model parameters
        mlflow.log_param("model", "LogisticRegression")
        mlflow.log_param("test_size", 0.2)
        mlflow.log_param("random_state", 42)
       
        # Train the model
        model.fit(X_train, y_train)


        # Make predictions
        y_pred = model.predict(X_test)
        y_pred_proba = model.predict_proba(X_test)[:, 1]


        # Calculate performance metrics
        accuracy = accuracy_score(y_test, y_pred)
        precision = precision_score(y_test, y_pred)
        recall = recall_score(y_test, y_pred)
        f1 = f1_score(y_test, y_pred)
        roc_auc = roc_auc_score(y_test, y_pred_proba)


        # Log metrics
        metrics = ['accuracy', 'precision', 'recall', 'f1_score', 'roc_auc']
        for metric, value in zip(metrics, [accuracy, precision, recall, f1, roc_auc]):
            mlflow.log_metric(metric, value)
            print(f"{metric.capitalize()}: {value:.4f}")


        # Log the model
        mlflow.sklearn.log_model(model, "logistic_regression_model")


        return accuracy, precision, recall, f1, roc_auc


# Call the function
result = train_and_validate_model(X_train, X_test, y_train, y_test)



Q- now can you first create a virtual discussion between  you and me (your are a senior ds) 
About how can translate this data science notebook in production read python module like 

Q- do not give right now the code just and discussion and then a list of best practuces to follow oky