Top 5 Data Science Capstone Project Ideas That Will Impress Employers and Sharpen Your Skills

📗 Chapter 1: Customer Churn Prediction for a Subscription-Based Business

Build a Predictive Model That Saves Customers Before They Leave

🧠 Introduction

Customer retention is vital for subscription-based businesses like telecoms, SaaS, OTT platforms, and fitness apps. Acquiring a new customer costs up to 5x more than retaining an existing one. Predicting customer churn — the likelihood of a user canceling a service — enables proactive strategies to reduce revenue loss and improve customer satisfaction.

In this project, we’ll build a customer churn prediction system using machine learning. You'll walk through the entire data science pipeline:

1. Problem definition and business understanding
2. Data exploration and cleaning
3. Feature engineering
4. Model training and evaluation
5. Interpretation and deployment

Let’s dive in and start saving customers before they leave.

📂 Step 1: Define the Problem

• Business Goal: Predict if a customer will churn in the next billing cycle.
• Target Variable: Churn (binary: Yes/No)
• Use Case: Marketing can use the model to send targeted retention offers.

📊 Step 2: Load and Explore the Dataset

We’ll use the popular Telco Customer Churn dataset from Kaggle.

🔗 Dataset:

https://www.kaggle.com/blastchar/telco-customer-churn

🔧 Import Libraries

python

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

📥 Load Data

python

df = pd.read_csv('Telco-Customer-Churn.csv')

df.head()

🧹 Basic EDA

python

df.info()

df.describe()

df['Churn'].value_counts()

• Check for missing values:

python

df.isnull().sum()

• Convert TotalCharges to numeric:

python

df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')

📐 Step 3: Data Preprocessing & Feature Engineering

🧼 Handle Missing Values

python

df = df.dropna(subset=['TotalCharges'])

🔄 Encode Categorical Features

python

df = df.drop(['customerID'], axis=1)

df = pd.get_dummies(df, drop_first=True)

⚖️ Balance the Dataset (Optional)

python

from imblearn.over_sampling import SMOTE

X = df.drop('Churn_Yes', axis=1)

y = df['Churn_Yes']

smote = SMOTE(random_state=42)

X_res, y_res = smote.fit_resample(X, y)

🤖 Step 4: Model Building

🧪 Train-Test Split

python

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=0.2, random_state=42)

⚙️ Logistic Regression

python

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import classification_report, confusion_matrix

model = LogisticRegression(max_iter=1000)

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

print(classification_report(y_test, y_pred))

✅ Confusion Matrix

python

sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d')

📈 ROC-AUC

python

from sklearn.metrics import roc_auc_score, roc_curve

y_proba = model.predict_proba(X_test)[:, 1]

fpr, tpr, _ = roc_curve(y_test, y_proba)

plt.plot(fpr, tpr, label=f"AUC = {roc_auc_score(y_test, y_proba):.2f}")

plt.plot([0, 1], [0, 1], linestyle='--')

plt.xlabel('FPR'); plt.ylabel('TPR'); plt.title('ROC Curve')

plt.legend()

plt.show()

📌 Step 5: Feature Importance

python

importances = pd.Series(model.coef_[0], index=X.columns)

importances.nlargest(10).plot(kind='barh')

plt.title("Top 10 Influential Features for Churn")

plt.show()

🌐 Step 6: Deployment Ideas

• Streamlit App: Build a customer form that returns churn probability.
• Flask API: Integrate model into a backend system.
• Dashboard: Use Power BI or Tableau for executive churn overviews.

🧠 Insights You Can Present

📋 Summary Table