{"id":26947415,"url":"https://github.com/coder5omkar/telecom-churn-case-study","last_synced_at":"2025-04-02T20:19:13.769Z","repository":{"id":281790702,"uuid":"946430115","full_name":"coder5omkar/telecom-churn-case-study","owner":"coder5omkar","description":null,"archived":false,"fork":false,"pushed_at":"2025-03-11T06:03:58.000Z","size":457,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-11T07:19:34.372Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/coder5omkar.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-11T06:00:49.000Z","updated_at":"2025-03-11T06:04:01.000Z","dependencies_parsed_at":"2025-03-11T07:19:41.144Z","dependency_job_id":"d96fc9da-ab71-4983-bf0c-cc3b97769df9","html_url":"https://github.com/coder5omkar/telecom-churn-case-study","commit_stats":null,"previous_names":["coder5omkar/telecom-churn-case-study"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder5omkar%2Ftelecom-churn-case-study","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder5omkar%2Ftelecom-churn-case-study/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder5omkar%2Ftelecom-churn-case-study/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder5omkar%2Ftelecom-churn-case-study/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/coder5omkar","download_url":"https://codeload.github.com/coder5omkar/telecom-churn-case-study/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246884740,"owners_count":20849554,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-04-02T20:19:13.245Z","updated_at":"2025-04-02T20:19:13.755Z","avatar_url":"https://github.com/coder5omkar.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📶📱 Telecom Churn Case Study 🎭🔮\n\n## 🤔 Problem Statement 💭\n\n## 📊 Business Buzz 🌍\n\n📡 In the **telecom battlefield**, users 🏃‍♂️ between providers! With **15-25% churn** 📉, it's **pricier** to gain than retain 🤯. Keeping high-value 📈 customers = **priority #1** 🚀!\n\n🔍 **Mission:** Predict \u0026 prevent **churn** 🔮 before it's too late ⏳! Let’s **decode** 📜 customer signals 🧐 \u0026 **forecast exits** ⏳!\n\n## 🔄 What is Churn? ❌📴\n\n📲 **Prepaid vs Postpaid** 💰: In **postpaid**, 📩 cancellation = clear **churn**. In **prepaid**, users **vanish** like ghosts 👻! Hard to tell—trip? 🤷‍♂️ Or **churned?** ❌\n\n🚀 **Prepaid is king** 👑 in **India \u0026 SEA** 🌏, making **churn prediction** a BIG deal! 🎯\n\n## 🔍 Spotting Churn 📡\n\n**💰 Revenue Churn:** Users **spending \u003c ₹4**? 🧐 Might be churn! But some folks **only receive calls** 📞—not true churn ❌\n\n**📵 Usage Churn:** No 📞, no 📡, no 📲? **Silent exit!** 😶🚪 But if we wait **too long**, they've **already left!** 🏃‍♂️💨\n\n✅ **We’ll use:** **Usage-based churn** ✅\n\n## 💎 High-Value Churn 🚨\n\n🤑 **Top 20% users = 80% of revenue** 💰! Losing them = 🚨 **major loss** 🚨\n\n🎯 **Target:** **High-value users!** We’ll **define, track, and protect** these 💎 customers!\n\n## 🔬 Data Dive 🕵️‍♂️\n\n📂 **4 months of customer footprints** 🗂️ (June-Sept = **6️⃣ 7️⃣ 8️⃣ 9️⃣**)\n\n🎯 **Goal:** Predict **Month 9 churn** from **Months 6-8**! 🧐 **Spot unhappy signs early!**\n\n## 🔥 The Churn Timeline ⏳\n\n💚 **Good Phase** 😊: All’s well! 🎵 No worries! 😎\n\n⚠️ **Action Phase** 🤨: Users start **thinking** about leaving 🚪 (bad service? competitor offer? 🤔)\n\n❌ **Churn Phase** 🚨: **Poof! They’re gone!** 👻 Data gets cut OFF 🔪 for predictions!\n\n✅ **Plan:** First 2 months = 📗 Happy, Month 3 = 🔴 Danger, Month 4 = ❌ Churn!\n\n## 📜 The Data Bible 📖\n\n📂 **Dataset:** [Get it here!](https://drive.google.com/file/d/1SWnADIda31mVFevFcfkGtcgBHTKKI94J/view) 🔗\n\n📖 **Dictionary Guide:** Decode 📚 terms like **loc, IC, OG, T2T, RECH** 🔤\n\n## 🛠️ Data Surgery 🏥\n\n🛠 **Feature Crafting** 🎭: Smart tweaks 🔄 = Better Predictions 🧠💡\n\n💰 **High-Value Filter** 🎯: **Users spending ≥ X (top 30%)** = VIP 🚀\n\n❌ **Tagging Churn** 🏷️: No 📞, no 📡, no 📲 in **Month 9**? 🚪 **Tag as churn!** ✅\n\n## 🤖 Predicting Churn 🔮\n\n🚀 **The ML Magic** 🧙‍♂️\n\n🛠️ **Steps:**\n1️⃣ Preprocess 🎨 (fix missing values, formats 🛠️)\n2️⃣ Explore 📊 (find juicy insights! 🍉)\n3️⃣ Engineer 🚀 (new power features! 💡)\n4️⃣ Shrink 🔍 (use **PCA** to clean clutter 📉)\n5️⃣ Train 🤖 (try models! 🏆 handle **class imbalance** 🎭)\n6️⃣ Evaluate 🧐 (**focus on churners!** 📍 precision matters!)\n7️⃣ Pick the **winning model** 🏅\n\n🎯 **Two Goals:**\n1️⃣ **Who will churn?** 📉 (Predict exits before they happen!)\n2️⃣ **Why do they churn?** 🧐 (Find the **red flags** 🚩 \u0026 fix!)\n\n🛠 **Extra Trick:** Use **Logistic Regression** 📊 or **Tree Models** 🌳 for **explainable churn reasons!**\n\n📊 **Show churn insights visually** 🎨: Plots 📉, Graphs 📊, \u0026 **actionable strategies!** 🚀\n\n## 🚀 Action Plan 💥\n\n✅ Predict churn before it happens! 🔮\n✅ Spot **why** customers leave \u0026 fix it! 🛠️\n✅ Take **smart actions** (custom offers 🎁, better plans 📜, etc.)\n\n🔥 **Goal = Save Customers!** 💪💡📡 **Let’s reduce churn \u0026 boost revenue!** 🚀📈\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoder5omkar%2Ftelecom-churn-case-study","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcoder5omkar%2Ftelecom-churn-case-study","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoder5omkar%2Ftelecom-churn-case-study/lists"}