Introduction

Predictive modeling is at the core of data-driven decision-making, enabling businesses to forecast trends, detect anomalies, and optimize operations. This Advanced Predictive Modeling Techniques Training Course equips participants with cutting-edge methodologies, including advanced regression, ensemble learning, deep learning models, and probabilistic forecasting.

Using Python, Scikit-Learn, TensorFlow, and cloud-based ML frameworks, participants will develop and optimize predictive models applicable to finance, healthcare, marketing, supply chain, and cybersecurity.

Objectives

By the end of this course, participants will:

1. Master advanced regression, classification, and time-series forecasting techniques.
2. Implement ensemble methods, boosting techniques, and deep learning models.
3. Optimize predictive models using hyperparameter tuning and feature engineering.
4. Leverage probabilistic models and Bayesian methods for uncertainty estimation.
5. Deploy models in real-world production environments.
6. Utilize AutoML and cloud-based machine learning services.
7. Apply predictive modeling techniques to real-world business challenges.

Who Should Attend?

• Data Scientists & Machine Learning Engineers
• Business Analysts & Decision Scientists
• Financial Analysts & Risk Management Professionals
• Healthcare & Marketing Analytics Specialists
• Software Engineers & AI Enthusiasts
• Anyone looking to enhance predictive modeling expertise

Course Outline (5 Days)

Day 1: Fundamentals of Predictive Modeling & Feature Engineering

Morning Session

• Overview of Predictive Modeling

  • Supervised vs. Unsupervised Learning
  • Business Applications in finance, healthcare, marketing, and cybersecurity
  • Data pipeline for predictive modeling

• Feature Engineering & Selection

  • Handling missing values, outliers, and skewed data
  • Dimensionality reduction: PCA, LDA, t-SNE
  • Hands-on: Feature engineering on real-world datasets

Afternoon Session

• Advanced Feature Selection Techniques

  • Recursive Feature Elimination (RFE)
  • Mutual information & SHAP values for feature importance
  • Hands-on: Building robust features for predictive models

• Hands-on Exercise

  • Data preprocessing and feature engineering for customer churn prediction

Day 2: Advanced Regression & Classification Models

Morning Session

• Advanced Regression Techniques

  • Generalized Linear Models (GLM), Lasso & Ridge Regression
  • Polynomial Regression & Splines
  • Hands-on: Predicting housing prices using advanced regression

• Handling Imbalanced Datasets

  • Oversampling, undersampling, and SMOTE
  • Cost-sensitive learning & anomaly detection
  • Hands-on: Improving fraud detection using resampling techniques

Afternoon Session

• Advanced Classification Models

  • Support Vector Machines (SVM) with Kernel Tricks
  • Neural Networks for structured data
  • Hands-on: Building a classification model for credit risk analysis

• Hands-on Exercise

  • Training and tuning classification models for healthcare diagnostics

Day 3: Ensemble Learning & Boosting Techniques

Morning Session

• Introduction to Ensemble Learning

  • Bagging, Boosting, and Stacking
  • Random Forest & Extra Trees
  • Hands-on: Improving model accuracy with ensemble techniques

• Gradient Boosting & XGBoost

  • How boosting improves weak learners
  • Fine-tuning XGBoost with hyperparameter optimization
  • Hands-on: Boosting techniques for stock price prediction

Afternoon Session

• Advanced Boosting Models

  • LightGBM & CatBoost for high-dimensional datasets
  • Hands-on: Fine-tuning boosting algorithms for NLP applications

• Hands-on Exercise

  • Applying stacking techniques to customer segmentation

Day 4: Time-Series Forecasting & Probabilistic Models

Morning Session

• Time-Series Forecasting Fundamentals

  • Moving Averages, Exponential Smoothing
  • ARIMA, SARIMA, and Prophet models
  • Hands-on: Forecasting demand using Prophet

• Feature Engineering for Time-Series Data

  • Rolling windows, seasonal decomposition
  • Fourier transformations for periodicity detection
  • Hands-on: Building time-series features for sales forecasting

Afternoon Session

• Deep Learning for Time-Series Forecasting

  • LSTMs, GRUs, and Transformers for time-series
  • Sequence-to-sequence models for predictive analytics
  • Hands-on: Building an LSTM-based time-series prediction model

• Hands-on Exercise

  • Forecasting energy consumption using deep learning models

Day 5: Model Deployment & Real-World Applications

Morning Session

• Probabilistic & Bayesian Predictive Models

  • Bayesian Regression & Uncertainty Estimation
  • Gaussian Processes for predictive analytics
  • Hands-on: Building Bayesian models for risk forecasting

• AutoML & Cloud-Based Predictive Modeling

  • Google AutoML, AWS SageMaker, and Azure ML
  • Scaling predictive models in cloud environments
  • Hands-on: Deploying a predictive model using Google AutoML

Afternoon Session

• Capstone Project & Final Presentations

  • Choose from:
    1. Customer Churn Prediction for a Telecom Company
    2. Stock Market Price Prediction using Ensemble Learning
    3. Real-Time Fraud Detection using Machine Learning
  • Participants present their models & receive expert feedback

• Certification & Networking Session

Post-Course Benefits

• Hands-on experience with real-world predictive modeling projects
• Expertise in advanced regression, ensemble methods, and deep learning
• Ability to deploy predictive models in production environments
• Portfolio-ready projects for career growth
• Access to exclusive resources (datasets, notebooks, and cheat sheets)