Top 5 Machine Learning Projects to Instantly Boost Your Portfolio in 2025

📙 Chapter 3: Customer Churn Prediction – Retain Clients Before They Leave

🎯 Objective

The objective of this project is to build a machine learning model that can predict whether a customer is likely to churn — i.e., stop doing business with a company. Churn prediction is crucial for customer retention in industries like telecom, banking, SaaS, and e-commerce. You’ll develop a predictive model using historical customer behavior and use it to proactively identify customers at risk.

🧠 What is Customer Churn?

Customer churn refers to the loss of clients or subscribers. If a customer stops using your product or service during a given time period, they are considered churned. Reducing churn is significantly more cost-effective than acquiring new customers, which makes churn prediction a high-priority business task.

🛠️ Tools and Libraries Required

• Python 3.8+
• Pandas
• NumPy
• Scikit-learn
• Seaborn / Matplotlib
• SHAP / LIME (for explainability)
• Streamlit or Flask (optional deployment)

📥 Step 1: Dataset Collection

You can use open-source churn datasets like:

Load the data:

python

import pandas as pd

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

🔍 Step 2: Exploratory Data Analysis (EDA)

• Check class imbalance (how many customers churned vs. stayed)
• Visualize churn rates by contract type, payment method, tenure, etc.
• Identify missing values and outliers

python

import seaborn as sns

import matplotlib.pyplot as plt

sns.countplot(x='Churn', data=df)

plt.title('Churn Distribution')

Sample features:

🧹 Step 3: Data Preprocessing

Steps include:

• Encode categorical variables using Label Encoding or One-Hot Encoding
• Convert Yes/No to binary
• Scale numerical features (e.g., MonthlyCharges, Tenure)
• Handle missing values

Example:

python

from sklearn.preprocessing import LabelEncoder

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

le = LabelEncoder()

df['Gender'] = le.fit_transform(df['gender'])

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

✨ Step 4: Feature Engineering

• Create interaction terms (e.g., Tenure * MonthlyCharges)
• Bin tenure into buckets (0–12, 13–24, etc.)
• Create flags for customers with multiple products or no internet service

Example of binning:

python

df['tenure_group'] = pd.cut(df['tenure'], bins=[0, 12, 24, 48, 72], labels=["0-12","13-24","25-48","49-72"])

🔧 Step 5: Model Building

Popular models for churn prediction include:

Example:

python

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import classification_report

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

y = df['Churn']

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

model = RandomForestClassifier()

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

print(classification_report(y_test, y_pred))

📊 Step 6: Evaluation Metrics

Plotting ROC curve:

python

from sklearn.metrics import roc_auc_score, roc_curve

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

fpr, tpr, thresholds = roc_curve(y_test, probs)

plt.plot(fpr, tpr)

plt.title('ROC Curve')

🧠 Step 7: Explainability – Why Did They Churn?

Use SHAP or LIME to explain predictions:

• Identify which features lead to a higher churn probability
• Help stakeholders understand and trust the model

python

import shap

explainer = shap.Explainer(model, X_train)

shap_values = explainer(X_test)

shap.plots.beeswarm(shap_values)

🎨 Step 8: Visual Insights and Dashboards

Create dashboards using:

• Streamlit
• Power BI
• Tableau

Include:

• Churn heatmaps by contract/payment method
• Tenure vs. churn distribution
• Monthly charges distribution

🚀 Step 9: Deployment (Optional)

Use Streamlit to deploy an interactive churn predictor:

python

import streamlit as st

st.title("Customer Churn Predictor")

gender = st.selectbox("Gender", ['Male', 'Female'])

tenure = st.slider("Tenure (in months)", 0, 72)

monthly_charge = st.number_input("Monthly Charges")

# preprocess and predict

Deploy on Streamlit Cloud or Heroku.

🧾 Step 10: GitHub Portfolio Structure

Organize your project:

• /data – CSVs or raw data
• /notebooks – EDA and experiments
• /src – model logic and preprocessing
• /streamlit_app.py – app file
• README.md – overview, screenshots, results

✅ Summary Table