https://github.com/coder5omkar/telecom-churn-case-study
https://github.com/coder5omkar/telecom-churn-case-study
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/coder5omkar/telecom-churn-case-study
- Owner: coder5omkar
- Created: 2025-03-11T06:00:49.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-03-11T06:03:58.000Z (11 months ago)
- Last Synced: 2025-03-11T07:19:34.372Z (11 months ago)
- Language: Jupyter Notebook
- Size: 446 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 📶📱 Telecom Churn Case Study 🎭🔮
## 🤔 Problem Statement 💭
## 📊 Business Buzz 🌍
📡 In the **telecom battlefield**, users 🏃♂️ between providers! With **15-25% churn** 📉, it's **pricier** to gain than retain 🤯. Keeping high-value 📈 customers = **priority #1** 🚀!
🔍 **Mission:** Predict & prevent **churn** 🔮 before it's too late ⏳! Let’s **decode** 📜 customer signals 🧐 & **forecast exits** ⏳!
## 🔄 What is Churn? ❌📴
📲 **Prepaid vs Postpaid** 💰: In **postpaid**, 📩 cancellation = clear **churn**. In **prepaid**, users **vanish** like ghosts 👻! Hard to tell—trip? 🤷♂️ Or **churned?** ❌
🚀 **Prepaid is king** 👑 in **India & SEA** 🌏, making **churn prediction** a BIG deal! 🎯
## 🔍 Spotting Churn 📡
**💰 Revenue Churn:** Users **spending < ₹4**? 🧐 Might be churn! But some folks **only receive calls** 📞—not true churn ❌
**📵 Usage Churn:** No 📞, no 📡, no 📲? **Silent exit!** 😶🚪 But if we wait **too long**, they've **already left!** 🏃♂️💨
✅ **We’ll use:** **Usage-based churn** ✅
## 💎 High-Value Churn 🚨
🤑 **Top 20% users = 80% of revenue** 💰! Losing them = 🚨 **major loss** 🚨
🎯 **Target:** **High-value users!** We’ll **define, track, and protect** these 💎 customers!
## 🔬 Data Dive 🕵️♂️
📂 **4 months of customer footprints** 🗂️ (June-Sept = **6️⃣ 7️⃣ 8️⃣ 9️⃣**)
🎯 **Goal:** Predict **Month 9 churn** from **Months 6-8**! 🧐 **Spot unhappy signs early!**
## 🔥 The Churn Timeline ⏳
💚 **Good Phase** 😊: All’s well! 🎵 No worries! 😎
⚠️ **Action Phase** 🤨: Users start **thinking** about leaving 🚪 (bad service? competitor offer? 🤔)
❌ **Churn Phase** 🚨: **Poof! They’re gone!** 👻 Data gets cut OFF 🔪 for predictions!
✅ **Plan:** First 2 months = 📗 Happy, Month 3 = 🔴 Danger, Month 4 = ❌ Churn!
## 📜 The Data Bible 📖
📂 **Dataset:** [Get it here!](https://drive.google.com/file/d/1SWnADIda31mVFevFcfkGtcgBHTKKI94J/view) 🔗
📖 **Dictionary Guide:** Decode 📚 terms like **loc, IC, OG, T2T, RECH** 🔤
## 🛠️ Data Surgery 🏥
🛠 **Feature Crafting** 🎭: Smart tweaks 🔄 = Better Predictions 🧠💡
💰 **High-Value Filter** 🎯: **Users spending ≥ X (top 30%)** = VIP 🚀
❌ **Tagging Churn** 🏷️: No 📞, no 📡, no 📲 in **Month 9**? 🚪 **Tag as churn!** ✅
## 🤖 Predicting Churn 🔮
🚀 **The ML Magic** 🧙♂️
🛠️ **Steps:**
1️⃣ Preprocess 🎨 (fix missing values, formats 🛠️)
2️⃣ Explore 📊 (find juicy insights! 🍉)
3️⃣ Engineer 🚀 (new power features! 💡)
4️⃣ Shrink 🔍 (use **PCA** to clean clutter 📉)
5️⃣ Train 🤖 (try models! 🏆 handle **class imbalance** 🎭)
6️⃣ Evaluate 🧐 (**focus on churners!** 📍 precision matters!)
7️⃣ Pick the **winning model** 🏅
🎯 **Two Goals:**
1️⃣ **Who will churn?** 📉 (Predict exits before they happen!)
2️⃣ **Why do they churn?** 🧐 (Find the **red flags** 🚩 & fix!)
🛠 **Extra Trick:** Use **Logistic Regression** 📊 or **Tree Models** 🌳 for **explainable churn reasons!**
📊 **Show churn insights visually** 🎨: Plots 📉, Graphs 📊, & **actionable strategies!** 🚀
## 🚀 Action Plan 💥
✅ Predict churn before it happens! 🔮
✅ Spot **why** customers leave & fix it! 🛠️
✅ Take **smart actions** (custom offers 🎁, better plans 📜, etc.)
🔥 **Goal = Save Customers!** 💪💡📡 **Let’s reduce churn & boost revenue!** 🚀📈