Project goal
Predict which customers will leave. Act before revenue drops.
Business value
• Retention costs less than acquisition
• Clear actions for sales and support
• High interview relevance
Dataset
Telco customer churn style dataset.
Target: Churn (Yes left, No stayed)
Key features
• tenure
• MonthlyCharges
• TotalCharges
• Contract
• PaymentMethod
• InternetService
Tech stack
• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
Step 1. Import libraries
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.preprocessing import LabelEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score
Step 2. Load data
df = pd.read_csv("customer_churn.csv")
df.head()Step 3. Basic checks
df.shape
df.info()
df.isnull().sum()
Step 4. Data cleaning
Convert TotalCharges to numeric.
df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')
df['TotalCharges'].fillna(df['TotalCharges'].median(), inplace=True)
Drop customer ID.
df.drop('customerID', axis=1, inplace=True)Step 5. Exploratory Data Analysis
Churn distribution.
sns.countplot(x='Churn', data=df)
plt.show()
Tenure vs churn.
sns.boxplot(x='Churn', y='tenure', data=df)
plt.show()
Common insights:
• Month-to-month contracts churn more
• Low tenure users churn early
• High monthly charges increase churn
Step 6. Encode categorical variables
le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
df[col] = le.fit_transform(df[col])
Step 7. Feature scaling
scaler = StandardScaler()
num_cols = ['tenure', 'MonthlyCharges', 'TotalCharges']
df[num_cols] = scaler.fit_transform(df[num_cols])
Step 8. Split data
X = df.drop('Churn', axis=1)
y = df['Churn']
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=42, stratify=y
)Step 9. Build model
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)
Step 10. Predictions
y_pred = model.predict(X_test)
y_prob = model.predict_proba(X_test)[:,1]
Step 11. Evaluation
confusion_matrix(y_test, y_pred)
print(classification_report(y_test, y_pred))
roc_auc_score(y_test, y_prob)
Typical results:
• Accuracy around 78 to 83 percent
• ROC AUC around 0.84
• Recall for churn is key metric
Step 12. Business actions from model
• Target high-risk users
• Offer discounts to month-to-month users
• Push yearly contracts
• Improve onboarding for first 90 days
Resume bullet example:
• Built churn prediction model using Logistic Regression
• Identified contract type and tenure as top churn drivers
• Improved churn recall using class-aware split
Interview explanation flow:
• Revenue loss problem
• Why recall matters more than accuracy
• How features map to actions
Mini task for you:
• Train Random Forest
• Compare ROC AUC
• Tune threshold for higher recall
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