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classification basic churn

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Telecom Customer Churn - Supervised Classification Guide

This notebook demonstrates a beginner-friendly machine learning workflow using the dataset:

TelecomCustomerChurn.csv

Goal:

Predict whether a customer will churn (leave the service).

Target column:

Churn

Steps covered:

Load dataset
Exploratory Data Analysis (EDA) with Seaborn
Data cleaning
Feature encoding
Train/test split
Train classification models one by one
Evaluate models
Compare results

Import Libraries

Codepython

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

sns.set_style("whitegrid")

Load Dataset

Codepython

churn = pd.read_csv("../datasets/TelecomCustomerChurn.csv")

churn.head()

Output

   customerID  Gender  SeniorCitizen Partner Dependents  Tenure PhoneService  \
0  7590-VHVEG  Female              0     Yes         No       1           No   
1  5575-GNVDE    Male              0      No         No      34          Yes   
2  3668-QPYBK    Male              0      No         No       2          Yes   
3  7795-CFOCW    Male              0      No         No      45           No   
4  9237-HQITU  Female              0      No         No       2          Yes   

  MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \
0            No             DSL             No  ...               No   
1            No             DSL            Yes  ...              Yes   
2            No             DSL            Yes  ...               No   
3            No             DSL            Yes  ...              Yes   
4            No     Fiber optic             No  ...               No   

  TechSupport StreamingTV StreamingMovies  Contract PaperlessBilling  \
0          No          No              No   Monthly              Yes   
1          No          No              No  One year               No   
2          No          No              No   Monthly              Yes   
3         Yes          No              No  One year               No   
4          No          No              No   Monthly              Yes   

               PaymentMethod MonthlyCharges  TotalCharges Churn  
0                     Manual          29.85         29.85    No  
1                     Manual          56.95        1889.5    No  
2                     Manual          53.85        108.15   Yes  
3  Bank transfer (automatic)          42.30       1840.75    No  
4                     Manual          70.70        151.65   Yes  

[5 rows x 21 columns]

Codepython

churn.info()

Output

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   Gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   Tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

Exploratory Data Analysis (EDA)

Churn Distribution

Codepython

plt.figure(figsize=(6,4))
sns.countplot(x="Churn", data=churn)
plt.title("Churn Distribution")
plt.show()

Output

Tenure Distribution

Codepython

sns.histplot(churn["Tenure"], bins=30, kde=True)
plt.title("Customer Tenure Distribution")
plt.show()

Output

Monthly Charges Distribution

Codepython

sns.histplot(churn["MonthlyCharges"], bins=30, kde=True)
plt.title("Monthly Charges Distribution")
plt.show()

Output

Monthly Charges vs Churn

Codepython

sns.boxplot(x="Churn", y="MonthlyCharges", data=churn)
plt.title("Monthly Charges vs Churn")
plt.show()

Output

Tenure vs Churn

Codepython

sns.boxplot(x="Churn", y="Tenure", data=churn)
plt.title("Tenure vs Churn")
plt.show()

Output

Correlation Heatmap

Codepython

plt.figure(figsize=(10,6))
sns.heatmap(churn.corr(numeric_only=True), cmap="coolwarm")
plt.title("Correlation Matrix")
plt.show()

Output

Data Cleaning

Codepython

churn["TotalCharges"] = pd.to_numeric(churn["TotalCharges"], errors="coerce")

churn["TotalCharges"] = churn["TotalCharges"].fillna(churn["TotalCharges"].median())

churn = churn.drop("customerID", axis=1)

Convert Target Variable

Codepython

churn["Churn"] = churn["Churn"].map({"Yes":1,"No":0})

Encode Categorical Variables

Codepython

churn_encoded = pd.get_dummies(churn, drop_first=True)

Train/Test Split

Codepython

X = churn_encoded.drop("Churn", axis=1)
y = churn_encoded["Churn"]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

Logistic Regression

Codepython

from sklearn.linear_model import LogisticRegression

log_model = LogisticRegression(max_iter=1000)
log_model.fit(X_train, y_train)

log_preds = log_model.predict(X_test)
log_acc = accuracy_score(y_test, log_preds)

log_acc

Output

/opt/envs/ds/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:473: ConvergenceWarning: lbfgs failed to converge after 1000 iteration(s) (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT

Increase the number of iterations to improve the convergence (max_iter=1000).
You might also want to scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

0.8190205819730305

Decision Tree

Codepython

from sklearn.tree import DecisionTreeClassifier

tree_model = DecisionTreeClassifier()
tree_model.fit(X_train, y_train)

tree_preds = tree_model.predict(X_test)
tree_acc = accuracy_score(y_test, tree_preds)

tree_acc

Output

0.7217885024840313

Random Forest

Codepython

from sklearn.ensemble import RandomForestClassifier

rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)

rf_preds = rf_model.predict(X_test)
rf_acc = accuracy_score(y_test, rf_preds)

rf_acc

Output

0.7863733144073811

Gradient Boosting

Codepython

from sklearn.ensemble import GradientBoostingClassifier

gb_model = GradientBoostingClassifier()
gb_model.fit(X_train, y_train)

gb_preds = gb_model.predict(X_test)
gb_acc = accuracy_score(y_test, gb_preds)

gb_acc

Output

0.8168914123491838

K-Nearest Neighbors

Codepython

from sklearn.neighbors import KNeighborsClassifier

knn_model = KNeighborsClassifier()
knn_model.fit(X_train, y_train)

knn_preds = knn_model.predict(X_test)
knn_acc = accuracy_score(y_test, knn_preds)

knn_acc

Output

0.7785663591199432

Support Vector Machine

Codepython

from sklearn.svm import SVC

svm_model = SVC()
svm_model.fit(X_train, y_train)

svm_preds = svm_model.predict(X_test)
svm_acc = accuracy_score(y_test, svm_preds)

svm_acc

Output

0.7352732434350603

Compare Model Performance

Codepython

results = pd.DataFrame({
    "Model":[
        "Logistic Regression",
        "Decision Tree",
        "Random Forest",
        "Gradient Boosting",
        "KNN",
        "SVM"
    ],
    "Accuracy":[
        log_acc,
        tree_acc,
        rf_acc,
        gb_acc,
        knn_acc,
        svm_acc
    ]
})

results.sort_values("Accuracy", ascending=False)

Output

                 Model  Accuracy
0  Logistic Regression  0.819021
3    Gradient Boosting  0.816891
2        Random Forest  0.786373
4                  KNN  0.778566
5                  SVM  0.735273
1        Decision Tree  0.721789

Conclusion

This notebook demonstrated:

Data exploration with Seaborn
Data cleaning and preprocessing
Training multiple classification models
Evaluating and comparing models

Common strong models for churn prediction:

Random Forest
Gradient Boosting
Logistic Regression

SMOTE

Codepython

# !pip install -U imbalanced-learn

Codepython

import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline as ImbPipeline

# Load dataset
churn = pd.read_csv("../datasets/TelecomCustomerChurn.csv")

# Drop ID column if present
if "customerID" in churn.columns:
    churn = churn.drop("customerID", axis=1)

# Convert TotalCharges if needed
if "TotalCharges" in churn.columns:
    churn["TotalCharges"] = pd.to_numeric(churn["TotalCharges"], errors="coerce")

# Target and features
X = churn.drop("Churn", axis=1)
y = churn["Churn"].map({"No": 0, "Yes": 1})

# Identify column types
categorical_cols = X.select_dtypes(include=["object"]).columns
numerical_cols = X.select_dtypes(exclude=["object"]).columns

# Numeric pipeline
num_pipeline = Pipeline([
    ("imputer", SimpleImputer(strategy="median"))
])

# Categorical pipeline
cat_pipeline = Pipeline([
    ("imputer", SimpleImputer(strategy="most_frequent")),
    ("encoder", OneHotEncoder(drop="first", handle_unknown="ignore"))
])

# Preprocessor
preprocessor = ColumnTransformer([
    ("num", num_pipeline, numerical_cols),
    ("cat", cat_pipeline, categorical_cols)
])

# Full pipeline with SMOTE
pipeline = ImbPipeline([
    ("preprocessing", preprocessor),
    ("smote", SMOTE(random_state=42)),
    ("model", RandomForestClassifier(random_state=42))
])

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Hyperparameter space
param_dist = {
    "model__n_estimators": [100, 200, 300, 500],
    "model__max_depth": [None, 10, 20, 30],
    "model__min_samples_split": [2, 5, 10],
    "model__min_samples_leaf": [1, 2, 4],
    "model__max_features": ["sqrt", "log2", None]
}

# Randomized search
random_search = RandomizedSearchCV(
    pipeline,
    param_distributions=param_dist,
    n_iter=5,
    cv=2,
    scoring="accuracy",
    n_jobs=1,
    verbose=2,
    random_state=42
)

# Train model
random_search.fit(X_train, y_train)

# Best model
best_model = random_search.best_estimator_

# Predictions
y_pred = best_model.predict(X_test)

# Evaluation
print("Best Parameters:", random_search.best_params_)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))

Output

Fitting 2 folds for each of 5 candidates, totalling 10 fits

[CV] END model__max_depth=30, model__max_features=None, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100; total time=   1.1s
[CV] END model__max_depth=30, model__max_features=None, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100; total time=   1.1s
[CV] END model__max_depth=None, model__max_features=None, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=500; total time=   5.5s
[CV] END model__max_depth=None, model__max_features=None, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=500; total time=   5.9s
[CV] END model__max_depth=10, model__max_features=None, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.0s
[CV] END model__max_depth=10, model__max_features=None, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.0s
[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=300; total time=   0.9s
[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=300; total time=   0.9s
[CV] END model__max_depth=30, model__max_features=log2, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100; total time=   0.3s
[CV] END model__max_depth=30, model__max_features=log2, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100; total time=   0.3s
Best Parameters: {'model__n_estimators': 300, 'model__min_samples_split': 5, 'model__min_samples_leaf': 4, 'model__max_features': 'sqrt', 'model__max_depth': None}
Accuracy: 0.7934705464868701

Classification Report:
               precision    recall  f1-score   support

           0       0.85      0.87      0.86      1035
           1       0.62      0.58      0.60       374

    accuracy                           0.79      1409
   macro avg       0.73      0.73      0.73      1409
weighted avg       0.79      0.79      0.79      1409

Codepython

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

print("Accuracy:", accuracy_score(y_test, y_pred))
print("Precision:", precision_score(y_test, y_pred))
print("Recall:", recall_score(y_test, y_pred))
print("F1 Score:", f1_score(y_test, y_pred))

Output

Accuracy: 0.7934705464868701
Precision: 0.6182336182336182
Recall: 0.5802139037433155
F1 Score: 0.5986206896551725

Codepython

import seaborn as sns

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

# Confusion matrix
cm = confusion_matrix(y_test, y_pred)

plt.figure(figsize=(6,4))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues",
            xticklabels=["No Churn","Churn"],
            yticklabels=["No Churn","Churn"])

plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix")
plt.show()

Output

Codepython

target_data = [0,0,0,0,0,0,0,0,0,1]

predictions = [0,0,0,0,0,0,0,0,0,0]

print(classification_report(target_data, predictions))

Output

              precision    recall  f1-score   support

           0       0.90      1.00      0.95         9
           1       0.00      0.00      0.00         1

    accuracy                           0.90        10
   macro avg       0.45      0.50      0.47        10
weighted avg       0.81      0.90      0.85        10

/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])
/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])
/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])

Codepython

target_data = [0,0,0,0,0,1,1,1,1,1]

predictions = [0,0,0,0,0,0,0,0,0,0]

print(classification_report(target_data, predictions))

Output

              precision    recall  f1-score   support

           0       0.50      1.00      0.67         5
           1       0.00      0.00      0.00         5

    accuracy                           0.50        10
   macro avg       0.25      0.50      0.33        10
weighted avg       0.25      0.50      0.33        10

/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])
/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])
/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])

Codepython

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Teaching Computers to Learn•Interactive notebook lesson•Free

classification basic churn

No account is required. Learn the material, try the examples, and mark it complete when you are ready to move on. Sign in with email only if you want to save progress across devices.

Telecom Customer Churn - Supervised Classification Guide

This notebook demonstrates a beginner-friendly machine learning workflow using the dataset:

TelecomCustomerChurn.csv

Goal:

Predict whether a customer will churn (leave the service).

Target column:

Churn

Steps covered:

Load dataset
Exploratory Data Analysis (EDA) with Seaborn
Data cleaning
Feature encoding
Train/test split
Train classification models one by one
Evaluate models
Compare results

Import Libraries

Codepython

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

sns.set_style("whitegrid")

Load Dataset

Codepython

churn = pd.read_csv("../datasets/TelecomCustomerChurn.csv")

churn.head()

Output

   customerID  Gender  SeniorCitizen Partner Dependents  Tenure PhoneService  \
0  7590-VHVEG  Female              0     Yes         No       1           No   
1  5575-GNVDE    Male              0      No         No      34          Yes   
2  3668-QPYBK    Male              0      No         No       2          Yes   
3  7795-CFOCW    Male              0      No         No      45           No   
4  9237-HQITU  Female              0      No         No       2          Yes   

  MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \
0            No             DSL             No  ...               No   
1            No             DSL            Yes  ...              Yes   
2            No             DSL            Yes  ...               No   
3            No             DSL            Yes  ...              Yes   
4            No     Fiber optic             No  ...               No   

  TechSupport StreamingTV StreamingMovies  Contract PaperlessBilling  \
0          No          No              No   Monthly              Yes   
1          No          No              No  One year               No   
2          No          No              No   Monthly              Yes   
3         Yes          No              No  One year               No   
4          No          No              No   Monthly              Yes   

               PaymentMethod MonthlyCharges  TotalCharges Churn  
0                     Manual          29.85         29.85    No  
1                     Manual          56.95        1889.5    No  
2                     Manual          53.85        108.15   Yes  
3  Bank transfer (automatic)          42.30       1840.75    No  
4                     Manual          70.70        151.65   Yes  

[5 rows x 21 columns]

Codepython

churn.info()

Output

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   Gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   Tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

Exploratory Data Analysis (EDA)

Churn Distribution

Codepython

plt.figure(figsize=(6,4))
sns.countplot(x="Churn", data=churn)
plt.title("Churn Distribution")
plt.show()

Output

Tenure Distribution

Codepython

sns.histplot(churn["Tenure"], bins=30, kde=True)
plt.title("Customer Tenure Distribution")
plt.show()

Output

Monthly Charges Distribution

Codepython

sns.histplot(churn["MonthlyCharges"], bins=30, kde=True)
plt.title("Monthly Charges Distribution")
plt.show()

Output

Monthly Charges vs Churn

Codepython

sns.boxplot(x="Churn", y="MonthlyCharges", data=churn)
plt.title("Monthly Charges vs Churn")
plt.show()

Output

Tenure vs Churn

Codepython

sns.boxplot(x="Churn", y="Tenure", data=churn)
plt.title("Tenure vs Churn")
plt.show()

Output

Correlation Heatmap

Codepython

plt.figure(figsize=(10,6))
sns.heatmap(churn.corr(numeric_only=True), cmap="coolwarm")
plt.title("Correlation Matrix")
plt.show()

Output

Data Cleaning

Codepython

churn["TotalCharges"] = pd.to_numeric(churn["TotalCharges"], errors="coerce")

churn["TotalCharges"] = churn["TotalCharges"].fillna(churn["TotalCharges"].median())

churn = churn.drop("customerID", axis=1)

Convert Target Variable

Codepython

churn["Churn"] = churn["Churn"].map({"Yes":1,"No":0})

Encode Categorical Variables

Codepython

churn_encoded = pd.get_dummies(churn, drop_first=True)

Train/Test Split

Codepython

X = churn_encoded.drop("Churn", axis=1)
y = churn_encoded["Churn"]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

Logistic Regression

Codepython

from sklearn.linear_model import LogisticRegression

log_model = LogisticRegression(max_iter=1000)
log_model.fit(X_train, y_train)

log_preds = log_model.predict(X_test)
log_acc = accuracy_score(y_test, log_preds)

log_acc

Output

/opt/envs/ds/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:473: ConvergenceWarning: lbfgs failed to converge after 1000 iteration(s) (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT

Increase the number of iterations to improve the convergence (max_iter=1000).
You might also want to scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

0.8190205819730305

Decision Tree

Codepython

from sklearn.tree import DecisionTreeClassifier

tree_model = DecisionTreeClassifier()
tree_model.fit(X_train, y_train)

tree_preds = tree_model.predict(X_test)
tree_acc = accuracy_score(y_test, tree_preds)

tree_acc

Output

0.7217885024840313

Random Forest

Codepython

from sklearn.ensemble import RandomForestClassifier

rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)

rf_preds = rf_model.predict(X_test)
rf_acc = accuracy_score(y_test, rf_preds)

rf_acc

Output

0.7863733144073811

Gradient Boosting

Codepython

from sklearn.ensemble import GradientBoostingClassifier

gb_model = GradientBoostingClassifier()
gb_model.fit(X_train, y_train)

gb_preds = gb_model.predict(X_test)
gb_acc = accuracy_score(y_test, gb_preds)

gb_acc

Output

0.8168914123491838

K-Nearest Neighbors

Codepython

from sklearn.neighbors import KNeighborsClassifier

knn_model = KNeighborsClassifier()
knn_model.fit(X_train, y_train)

knn_preds = knn_model.predict(X_test)
knn_acc = accuracy_score(y_test, knn_preds)

knn_acc

Output

0.7785663591199432

Support Vector Machine

Codepython

from sklearn.svm import SVC

svm_model = SVC()
svm_model.fit(X_train, y_train)

svm_preds = svm_model.predict(X_test)
svm_acc = accuracy_score(y_test, svm_preds)

svm_acc

Output

0.7352732434350603

Compare Model Performance

Codepython

results = pd.DataFrame({
    "Model":[
        "Logistic Regression",
        "Decision Tree",
        "Random Forest",
        "Gradient Boosting",
        "KNN",
        "SVM"
    ],
    "Accuracy":[
        log_acc,
        tree_acc,
        rf_acc,
        gb_acc,
        knn_acc,
        svm_acc
    ]
})

results.sort_values("Accuracy", ascending=False)

Output

                 Model  Accuracy
0  Logistic Regression  0.819021
3    Gradient Boosting  0.816891
2        Random Forest  0.786373
4                  KNN  0.778566
5                  SVM  0.735273
1        Decision Tree  0.721789

Conclusion

This notebook demonstrated:

Data exploration with Seaborn
Data cleaning and preprocessing
Training multiple classification models
Evaluating and comparing models

Common strong models for churn prediction:

Random Forest
Gradient Boosting
Logistic Regression

SMOTE

Codepython

# !pip install -U imbalanced-learn

Codepython

import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline as ImbPipeline

# Load dataset
churn = pd.read_csv("../datasets/TelecomCustomerChurn.csv")

# Drop ID column if present
if "customerID" in churn.columns:
    churn = churn.drop("customerID", axis=1)

# Convert TotalCharges if needed
if "TotalCharges" in churn.columns:
    churn["TotalCharges"] = pd.to_numeric(churn["TotalCharges"], errors="coerce")

# Target and features
X = churn.drop("Churn", axis=1)
y = churn["Churn"].map({"No": 0, "Yes": 1})

# Identify column types
categorical_cols = X.select_dtypes(include=["object"]).columns
numerical_cols = X.select_dtypes(exclude=["object"]).columns

# Numeric pipeline
num_pipeline = Pipeline([
    ("imputer", SimpleImputer(strategy="median"))
])

# Categorical pipeline
cat_pipeline = Pipeline([
    ("imputer", SimpleImputer(strategy="most_frequent")),
    ("encoder", OneHotEncoder(drop="first", handle_unknown="ignore"))
])

# Preprocessor
preprocessor = ColumnTransformer([
    ("num", num_pipeline, numerical_cols),
    ("cat", cat_pipeline, categorical_cols)
])

# Full pipeline with SMOTE
pipeline = ImbPipeline([
    ("preprocessing", preprocessor),
    ("smote", SMOTE(random_state=42)),
    ("model", RandomForestClassifier(random_state=42))
])

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Hyperparameter space
param_dist = {
    "model__n_estimators": [100, 200, 300, 500],
    "model__max_depth": [None, 10, 20, 30],
    "model__min_samples_split": [2, 5, 10],
    "model__min_samples_leaf": [1, 2, 4],
    "model__max_features": ["sqrt", "log2", None]
}

# Randomized search
random_search = RandomizedSearchCV(
    pipeline,
    param_distributions=param_dist,
    n_iter=5,
    cv=2,
    scoring="accuracy",
    n_jobs=1,
    verbose=2,
    random_state=42
)

# Train model
random_search.fit(X_train, y_train)

# Best model
best_model = random_search.best_estimator_

# Predictions
y_pred = best_model.predict(X_test)

# Evaluation
print("Best Parameters:", random_search.best_params_)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))

Output

Fitting 2 folds for each of 5 candidates, totalling 10 fits

[CV] END model__max_depth=30, model__max_features=None, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100; total time=   1.1s
[CV] END model__max_depth=30, model__max_features=None, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100; total time=   1.1s
[CV] END model__max_depth=None, model__max_features=None, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=500; total time=   5.5s
[CV] END model__max_depth=None, model__max_features=None, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=500; total time=   5.9s
[CV] END model__max_depth=10, model__max_features=None, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.0s
[CV] END model__max_depth=10, model__max_features=None, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.0s
[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=300; total time=   0.9s
[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=300; total time=   0.9s
[CV] END model__max_depth=30, model__max_features=log2, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100; total time=   0.3s
[CV] END model__max_depth=30, model__max_features=log2, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100; total time=   0.3s
Best Parameters: {'model__n_estimators': 300, 'model__min_samples_split': 5, 'model__min_samples_leaf': 4, 'model__max_features': 'sqrt', 'model__max_depth': None}
Accuracy: 0.7934705464868701

Classification Report:
               precision    recall  f1-score   support

           0       0.85      0.87      0.86      1035
           1       0.62      0.58      0.60       374

    accuracy                           0.79      1409
   macro avg       0.73      0.73      0.73      1409
weighted avg       0.79      0.79      0.79      1409

Codepython

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

print("Accuracy:", accuracy_score(y_test, y_pred))
print("Precision:", precision_score(y_test, y_pred))
print("Recall:", recall_score(y_test, y_pred))
print("F1 Score:", f1_score(y_test, y_pred))

Output

Accuracy: 0.7934705464868701
Precision: 0.6182336182336182
Recall: 0.5802139037433155
F1 Score: 0.5986206896551725

Codepython

import seaborn as sns

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

# Confusion matrix
cm = confusion_matrix(y_test, y_pred)

plt.figure(figsize=(6,4))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues",
            xticklabels=["No Churn","Churn"],
            yticklabels=["No Churn","Churn"])

plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix")
plt.show()

Output

Codepython

target_data = [0,0,0,0,0,0,0,0,0,1]

predictions = [0,0,0,0,0,0,0,0,0,0]

print(classification_report(target_data, predictions))

Output

              precision    recall  f1-score   support

           0       0.90      1.00      0.95         9
           1       0.00      0.00      0.00         1

    accuracy                           0.90        10
   macro avg       0.45      0.50      0.47        10
weighted avg       0.81      0.90      0.85        10

/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])
/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])
/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])

Codepython

target_data = [0,0,0,0,0,1,1,1,1,1]

predictions = [0,0,0,0,0,0,0,0,0,0]

print(classification_report(target_data, predictions))

Output

              precision    recall  f1-score   support

           0       0.50      1.00      0.67         5
           1       0.00      0.00      0.00         5

    accuracy                           0.50        10
   macro avg       0.25      0.50      0.33        10
weighted avg       0.25      0.50      0.33        10

/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])
/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])
/opt/envs/ds/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])

Codepython

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