Skip to content
Snippets Groups Projects
Commit a9458ed8 authored by Azmi Zahrani's avatar Azmi Zahrani
Browse files

Merge branch 'feature/data-processing' into 'develop' 

Feature/data processing

See merge request !2
parents 05b46d52 65bba8f2
Branches
2 merge requests!10chore: add model version control and finally can trigger the sparking flow...,!2Feature/data processing
Pipeline #66335 passed with stages
Stage:
    Stage:
      in 11 seconds
      Expand all Hide whitespace changes
      Inline Side-by-side
      Showing
      with 30075 additions and 0 deletions
      data/X_test.csv 0 → 100644
      + 529
      − 0
      View file @ a9458ed8
      This diff is collapsed.
      data/X_train.csv 0 → 100644
      + 5978
      − 0
      View file @ a9458ed8
      This diff is collapsed.
      data/X_val.csv 0 → 100644
      + 528
      − 0
      View file @ a9458ed8
      This diff is collapsed.
      data/cleaned_data.csv 0 → 100644
      + 7033
      − 0
      View file @ a9458ed8
      This diff is collapsed.
      data/telco_customer_churn.csv 0 → 100644
      + 7044
      − 0
      View file @ a9458ed8
      This diff is collapsed.
      data/y_test.csv 0 → 100644
      + 529
      − 0
      View file @ a9458ed8
      Churn
      0
      0
      1
      1
      0
      0
      1
      1
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      0
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      1
      0
      1
      0
      0
      0
      1
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      1
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      1
      1
      0
      0
      1
      1
      1
      1
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      0
      1
      0
      0
      1
      0
      0
      0
      0
      0
      1
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      1
      1
      0
      1
      1
      0
      1
      1
      0
      1
      0
      0
      0
      1
      1
      1
      0
      0
      1
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      0
      0
      0
      0
      1
      1
      0
      0
      1
      1
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      0
      1
      0
      1
      0
      1
      1
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      1
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      1
      1
      1
      0
      0
      0
      1
      1
      0
      0
      1
      0
      0
      1
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      0
      1
      0
      1
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      1
      0
      0
      0
      0
      1
      0
      1
      0
      0
      0
      1
      0
      0
      0
      1
      1
      1
      0
      1
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      0
      0
      1
      1
      1
      0
      1
      1
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      1
      1
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      1
      0
      0
      0
      0
      1
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      1
      0
      1
      1
      1
      0
      0
      0
      0
      0
      0
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      0
      0
      0
      0
      1
      0
      0
      0
      1
      1
      1
      0
      1
      0
      0
      0
      1
      1
      0
      0
      1
      1
      1
      1
      1
      0
      0
      1
      0
      1
      0
      1
      0
      0
      0
      0
      0
      1
      1
      0
      1
      0
      1
      0
      0
      0
      0
      1
      0
      0
      1
      0
      1
      0
      0
      data/y_train.csv 0 → 100644
      + 5978
      − 0
      View file @ a9458ed8
      This diff is collapsed.
      data/y_val.csv 0 → 100644
      + 528
      − 0
      View file @ a9458ed8
      Churn
      0
      1
      1
      0
      0
      0
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      1
      0
      0
      0
      1
      0
      0
      1
      0
      0
      0
      0
      1
      1
      0
      0
      1
      1
      0
      0
      1
      0
      1
      1
      1
      1
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      1
      0
      1
      1
      0
      1
      1
      0
      0
      1
      1
      0
      1
      1
      1
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      1
      0
      1
      1
      1
      0
      0
      0
      0
      0
      0
      1
      0
      0
      1
      0
      0
      0
      0
      1
      1
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      1
      0
      1
      1
      1
      0
      0
      1
      0
      0
      1
      0
      1
      1
      1
      0
      1
      0
      0
      0
      0
      0
      0
      1
      1
      0
      1
      0
      0
      0
      0
      1
      0
      0
      1
      1
      0
      0
      1
      1
      0
      0
      0
      0
      0
      0
      1
      1
      1
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      1
      0
      0
      1
      0
      1
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      1
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      0
      0
      1
      0
      0
      1
      0
      0
      0
      0
      1
      0
      1
      0
      0
      0
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      1
      1
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      1
      0
      0
      1
      0
      0
      0
      0
      0
      0
      1
      0
      1
      0
      1
      1
      0
      0
      0
      1
      1
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      1
      0
      0
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      1
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      0
      1
      0
      1
      1
      0
      0
      0
      0
      1
      0
      1
      0
      0
      1
      1
      1
      0
      1
      0
      0
      0
      0
      0
      0
      1
      1
      0
      1
      0
      0
      0
      0
      1
      0
      0
      0
      0
      1
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      1
      0
      1
      1
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      models/Gradient_Boosting.joblib 0 → 100644
      + 0
      − 0
      View file @ a9458ed8
      File added
      models/K-Nearest_Neighbors.joblib 0 → 100644
      + 0
      − 0
      View file @ a9458ed8
      File added
      models/Logistic_Regression.joblib 0 → 100644
      + 0
      − 0
      View file @ a9458ed8
      File added
      models/Random_Forest.joblib 0 → 100644
      + 0
      − 0
      View file @ a9458ed8
      File added
      models/Support_Vector_Machine.joblib 0 → 100644
      + 0
      − 0
      View file @ a9458ed8
      File added
      models/XGBoost.joblib 0 → 100644
      + 0
      − 0
      View file @ a9458ed8
      File added
      notebooks/customer_churn_analysis.ipynb 0 → 100644
      + 851
      − 0
      View file @ a9458ed8
      This diff is collapsed.
      notebooks/customer_churn_data_preprocessing.ipynb 0 → 100644
      + 784
      − 0
      View file @ a9458ed8
      This diff is collapsed.
      notebooks/customer_churn_machine_learning_models.ipynb 0 → 100644
      + 293
      − 0
      View file @ a9458ed8
      %% Cell type:markdown id: tags:
      # Customer Churn Prediction: Machine Learning Models
      This notebook will load the training, validation, and test datasets, train multiple machine learning models, evaluate their performance, and save the models for future use.
      %% Cell type:code id: tags:
      ``` python
      # Import Libraries
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score, classification_report
      from sklearn.preprocessing import StandardScaler
      import joblib
      from sklearn.svm import SVC
      from sklearn.ensemble import GradientBoostingClassifier
      from sklearn.neighbors import KNeighborsClassifier
      from xgboost import XGBClassifier
      # Set display options for better readability
      pd.set_option('display.max_columns', None)
      ```
      %% Cell type:markdown id: tags:
      ## 1. Load the Data
      Load the training, validation, and test datasets that we saved in the preprocessing step.
      %% Cell type:code id: tags:
      ``` python
      # Load the datasets
      X_train = pd.read_csv('../data/X_train.csv')
      y_train = pd.read_csv('../data/y_train.csv')
      X_val = pd.read_csv('../data/X_val.csv')
      y_val = pd.read_csv('../data/y_val.csv')
      X_test = pd.read_csv('../data/X_test.csv')
      y_test = pd.read_csv('../data/y_test.csv')
      # Display the shapes of the datasets
      print(f"Training set size: {X_train.shape[0]}")
      print(f"Validation set size: {X_val.shape[0]}")
      print(f"Test set size: {X_test.shape[0]}")
      ```
      %% Output
      Training set size: 5977
      Validation set size: 527
      Test set size: 528
      %% Cell type:markdown id: tags:
      ## 2. Scale the Data
      Scale the features using StandardScaler.
      %% Cell type:code id: tags:
      ``` python
      # Initialize the scaler
      scaler = StandardScaler()
      # Fit the scaler on the training data and transform the training, validation, and test sets
      X_train_scaled = scaler.fit_transform(X_train)
      X_val_scaled = scaler.transform(X_val)
      X_test_scaled = scaler.transform(X_test)
      ```
      %% Cell type:markdown id: tags:
      ## 3. Model Training
      Train multiple machine learning models
      %% Cell type:code id: tags:
      ``` python
      # Initialize models
      models = {
      'Logistic Regression': LogisticRegression(max_iter=2000),
      'Random Forest': RandomForestClassifier(n_estimators=100, random_state=127),
      'Support Vector Machine': SVC(probability=True),
      'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=127),
      'K-Nearest Neighbors': KNeighborsClassifier(n_neighbors=5),
      'XGBoost': XGBClassifier(eval_metric='logloss')
      }
      # Train models and evaluate on the validation set
      for model_name, model in models.items():
      model.fit(X_train_scaled, y_train.values.ravel())
      y_val_pred = model.predict(X_val_scaled)
      accuracy = accuracy_score(y_val, y_val_pred)
      print(f"{model_name} Validation Accuracy: {accuracy:.4f}")
      ```
      %% Output
      Logistic Regression Validation Accuracy: 0.8027
      Random Forest Validation Accuracy: 0.7742
      Support Vector Machine Validation Accuracy: 0.7989
      Gradient Boosting Validation Accuracy: 0.7951
      K-Nearest Neighbors Validation Accuracy: 0.7666
      XGBoost Validation Accuracy: 0.7837
      %% Cell type:markdown id: tags:
      ## 4. Save the Models
      Save the trained models for future use.
      %% Cell type:code id: tags:
      ``` python
      # Save each model
      for model_name, model in models.items():
      joblib.dump(model, f'../models/{model_name.replace(" ", "_")}.joblib')
      print(f"{model_name} saved successfully.")
      ```
      %% Output
      Logistic Regression saved successfully.
      Random Forest saved successfully.
      Support Vector Machine saved successfully.
      Gradient Boosting saved successfully.
      K-Nearest Neighbors saved successfully.
      XGBoost saved successfully.
      %% Cell type:markdown id: tags:
      ## 5. Load and Evaluate the Models
      Load the saved models and evaluate them on the test set.
      %% Cell type:code id: tags:
      ``` python
      # Load and evaluate each model on the test set
      for model_name in models.keys():
      loaded_model = joblib.load(f'../models/{model_name.replace(" ", "_")}.joblib')
      y_test_pred = loaded_model.predict(X_test_scaled)
      accuracy = accuracy_score(y_test, y_test_pred)
      print(f"{model_name} Test Accuracy: {accuracy:.4f}")
      print(classification_report(y_test, y_test_pred))
      ```
      %% Output
      Logistic Regression Test Accuracy: 0.8030
      precision recall f1-score support
      0 0.84 0.90 0.87 388
      1 0.66 0.54 0.59 140
      accuracy 0.80 528
      macro avg 0.75 0.72 0.73 528
      weighted avg 0.79 0.80 0.80 528
      Random Forest Test Accuracy: 0.7841
      precision recall f1-score support
      0 0.82 0.91 0.86 388
      1 0.63 0.44 0.52 140
      accuracy 0.78 528
      macro avg 0.73 0.68 0.69 528
      weighted avg 0.77 0.78 0.77 528
      Support Vector Machine Test Accuracy: 0.8087
      precision recall f1-score support
      0 0.84 0.92 0.88 388
      1 0.69 0.51 0.58 140
      accuracy 0.81 528
      macro avg 0.76 0.71 0.73 528
      weighted avg 0.80 0.81 0.80 528
      Gradient Boosting Test Accuracy: 0.8030
      precision recall f1-score support
      0 0.84 0.91 0.87 388
      1 0.67 0.51 0.58 140
      accuracy 0.80 528
      macro avg 0.75 0.71 0.72 528
      weighted avg 0.79 0.80 0.79 528
      K-Nearest Neighbors Test Accuracy: 0.7746
      precision recall f1-score support
      0 0.83 0.87 0.85 388
      1 0.58 0.52 0.55 140
      accuracy 0.77 528
      macro avg 0.71 0.69 0.70 528
      weighted avg 0.77 0.77 0.77 528
      XGBoost Test Accuracy: 0.7765
      precision recall f1-score support
      0 0.82 0.88 0.85 388
      1 0.60 0.48 0.53 140
      accuracy 0.78 528
      macro avg 0.71 0.68 0.69 528
      weighted avg 0.76 0.78 0.77 528
      0% or .
      You are about to add 0 people to the discussion. Proceed with caution.
      Finish editing this message first!
      Please register or to comment