1. Predicting Student Exam Scores (Supervised – Regression)
🎯 Objective:
Build a regression model to predict exam scores based on study hours, attendance, and socioeconomic factors. Learn how input features impact academic performance.
🧠 Prerequisites:
- Basic Python knowledge
- Pandas/Numpy for data handling
- Understanding of linear regression
💡 Skills Covered:
- Regression models in Scikit-learn
- Feature engineering & preprocessing
- Model evaluation (R², RMSE, MAE)
- Data visualization for insights
✅ Key Tasks:
- Prepare dataset and handle missing values
- Perform correlation analysis between features and scores
- Train Linear & Decision Tree Regression models
- Compare models with visualizations
2. Credit Card Fraud Detection (Supervised – Classification)
🎯 Objective:
Detect fraudulent transactions by training classification models on credit card spending behavior.
🧠 Prerequisites:
- Python basics
- Pandas for data wrangling
- Knowledge of classification algorithms
💡 Skills Covered:
- Classification models (Logistic, Random Forest, XGBoost)
- Handling imbalanced data (SMOTE, undersampling)
- Model evaluation (Precision, Recall, F1, ROC-AUC)
- Anomaly detection concepts
✅ Key Tasks:
- Explore transaction patterns
- Handle imbalanced data
- Train and compare ML models
- Build fraud detection pipeline
3. House Price Prediction (Supervised – Regression)
🎯 Objective:
Predict housing prices using location, size, and features. Learn feature engineering for better models.
🧠 Prerequisites:
- Python basics
- Pandas/NumPy
- Regression models understanding
💡 Skills Covered:
- Linear, Ridge, Lasso Regression
- Feature engineering (encoding, scaling)
- Model evaluation (MAE, RMSE)
- Hyperparameter tuning (GridSearchCV)
✅ Key Tasks:
- Clean & preprocess data
- Encode categorical variables
- Train models & compare performance
- Create price prediction dashboard (optional)
4. Customer Segmentation (Unsupervised – Clustering)
🎯 Objective:
Group customers based on purchasing behavior for targeted marketing.
🧠 Prerequisites:
- Basic Python knowledge
- Pandas for data handling
- Understanding of clustering (K-means)
💡 Skills Covered:
- K-means & Hierarchical Clustering
- Feature scaling & PCA
- Cluster validation (Elbow, Silhouette)
- Business insights from clusters
✅ Key Tasks:
- Preprocess customer data
- Apply clustering & determine clusters
- Visualize segments
- Interpret clusters
5. Movie Recommendation System (Unsupervised – Collaborative Filtering)
🎯 Objective:
Build a recommendation system that suggests movies based on user preferences.
🧠 Prerequisites:
- Python basics
- Pandas/Numpy
- Similarity measures knowledge
💡 Skills Covered:
- User & Item-based Collaborative Filtering
- Similarity measures (cosine, Pearson)
- Matrix factorization (SVD)
- Model evaluation (precision@k, recall@k)
✅ Key Tasks:
- Build user-item matrix
- Apply collaborative filtering
- Recommend top N movies
- Evaluate recommendations
🎓 Perks & Benefits
📄 Certificate of Completion
📝 Letter of Recommendation (Performance-Based)
🕐 1.5 Hours Live Trainer Guidance Daily
🧠 Half-Day Practice Sessions
💻 Online & Offline Batches
📆 Duration: 1 Month