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AI/ML Use Cases in Telecom Industries
AI/ML Use Cases in Telecom Industries
Curriculum
6 Sections
44 Lessons
35 Hours
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Module 1: Environment Setup & AI/ML Overview in Telecom
Learn how to set up a reproducible AI/ML environment and understand telecom-specific data challenges.
10
1.1
1.1: Introduction: Why AI/ML matters in 4G/5G networks – Basics
1.2
1.2:Typical telecom datasets: CDRs, QoS metrics, session logs
1.3
1.3: Installing Python & Anaconda
1.4
1.4: Creating and activating a virtual environment
1.5
1.5: Conda create -n Telecom AI python=3.10
1.6
1.6:conda activate Telecom AI
1.7
1.7: Installing key libraries (pandas, numpy, scikit-learn, matplotlib, seaborn, xgboost)
1.8
1.8:Launching and working with Jupyter Notebooks
1.9
1.9: Intro to Git & GitHub: initializing repos, committing, pushing code
1.10
LAB: Create virtual environment, Run a test notebook, Push your notebook to GitHub
Module 2: Use Case 1 — Customer Churn Prediction
Predict which customers are likely to leave the telecom service using ML models.
7
2.1
2.1: Load dataset: customer billing, usage, complaints
2.2
2.2: Data Cleaning (Handle nulls, outliers, Feature engineering (tenure_bins, charges_to_float))
2.3
2.3: Model Building: Logistic Regression, Random Forest
2.4
2.4: Model Evaluation(Accuracy, F1 Score, ROC Curve)
2.5
2.5: Visualization: Feature importance & churn distribution
2.6
2.6: Export results & notebook to GitHub
2.7
LAB: End-to-end churn prediction pipeline in Jupyter + GitHub upload.
Module 3: Use Case 2 — Network Fault Prediction
Predict 4G/5G network equipment failures using signal and log data.
7
3.1
3.1: Load and pre-process device logs (signal strength, temperature, packet drops)
3.2
3.2: Correlation analysis and feature selection
3.3
3.3: Model Building: SVM, Gradient Boosting
3.4
3.4: Hyper parameter tuning with GridSearchCV
3.5
3.5: Visualization: Fault trends by region/device
3.6
3.6:Export trained model (.pkl) to GitHub
3.7
LAB: Create and test fault prediction notebook with Git version control.
Module 4: Use Case 3 — Call Drop Prediction
Predict if a 5G call will drop based on real-time metrics.
7
4.1
4.1: Synthetic dataset: Signal_Strength, Tower_Distance, User_Speed, IsDropped
4.2
4.2: Data Cleaning: remove anomalies, normalize input features
4.3
4.3: Modeling: Decision Tree, XGBoost
4.4
4.4: Evaluation: Confusion matrix, classification report
4.5
4.5: Visualization: Dropped calls by time/location
4.6
4.6: Export notebook + visuals to GitHub
4.7
LAB: Simulate call drop prediction E2E with visual dashboards.
Module 5: Use Case 4 — Customer Segmentation for Marketing
Group telecom customers into clusters for targeted offers.
6
5.1
5.1: Data pre-processing: scaling, missing value handling
5.2
5.2: Model Building: K-Means, Hierarchical Clustering
5.3
5.3: Dimensionality Reduction: PCA/t-SNE visualization
5.4
5.4: Visualization: Cluster heatmaps & insights
5.5
5.5: Export clustering results to GitHub
5.6
LAB: Perform customer segmentation and publish notebook to repo.
Module 6: Use Case 5 — Fraud Detection in Telecom
Detect anomalous behavior in telecom usage data.
7
6.1
6.1: Data: CDRs, SIM change frequency, call location
6.2
6.2: Handling class imbalance: SMOTE
6.3
6.3: Modeling: Isolation Forest, Auto encoders
6.4
6.4: Evaluation: Precision, Recall, PR Curve
6.5
6.5: Visualization: Fraud heat map, anomaly score chart
6.6
6.6: GitHub workflow (Push final code, model, and README.md,Version tracking with commits)
6.7
LAB: Build and publish a telecom fraud detection model on GitHub.
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