Predictive Analytics with Python & R

The Predictive Analytics with Python & R program is a 5-day enterprise-level training designed to empower data professionals and business analysts with the skills to forecast trends, detect patterns, and make data-driven decisions using advanced statistical and machine learning techniques. This course combines the best of both Python and R ecosystems, enabling participants to build, evaluate, and deploy predictive models for real-world business scenarios such as customer churn, risk management, fraud detection, and sales forecasting. With hands-on exercises and practical use cases, the program ensures that learners not only understand the theoretical foundations of predictive modeling but also gain the technical ability to apply them effectively within enterprise environments.

Objectives of the Training

Understand the fundamentals of predictive analytics and machine learning workflows.
Master data preprocessing, feature engineering, and exploratory data analysis using Python and R.
Learn various predictive modeling techniques including regression, classification, and time-series forecasting.
Evaluate model performance and apply optimization techniques for accuracy improvement.
Automate analytics workflows and visualize results for business stakeholders.
Integrate predictive models into BI dashboards and enterprise systems for real-time insights.

Prerequisites

Basic knowledge of programming in Python or R.
Understanding of statistics and linear algebra.
Familiarity with data analysis, visualization, and SQL concepts.
Exposure to business analytics or BI tools is an advantage.

What You Will Learn

Predictive modeling techniques using Python (scikit-learn) and R (caret, forecast).
Exploratory Data Analysis (EDA), data preprocessing, and feature selection.
Building and interpreting regression and classification models.
Time-series forecasting using ARIMA, Prophet, and LSTM models.
Model evaluation using ROC, AUC, confusion matrix, and cross-validation.
Deploying predictive models and integrating with BI tools for visualization.

Target Audience

This training is ideal for Data Analysts, Business Analysts, Data Scientists, and Decision Makers who want to leverage predictive modeling techniques for data-driven business insights. It’s also well-suited for technical professionals and domain experts looking to transition into advanced analytics and AI-driven decision-making roles.

Detailed 5-Day Curriculum

Day 1 – Foundations of Predictive Analytics & Data Preparation (6 Hours)

Session 1: Introduction to Predictive Analytics – Concepts, Frameworks, and Use Cases.
Session 2: Overview of Python and R for Data Analytics – Tools, Libraries, and Environments.
Session 3: Data Importing, Cleaning, and Preprocessing using Pandas, NumPy, and tidyverse.
Hands-on: Preparing Business Datasets for Predictive Modeling.

Day 2 – Exploratory Data Analysis (EDA) & Feature Engineering (6 Hours)

Session 1: Statistical Data Exploration and Visualization using Seaborn and ggplot2.
Session 2: Correlation Analysis, Outlier Detection, and Feature Selection Techniques.
Session 3: Dimensionality Reduction with PCA and Feature Encoding.
Workshop: Building an EDA and Feature Engineering Pipeline for a Marketing Dataset.

Day 3 – Building Regression & Classification Models (6 Hours)

Session 1: Linear & Logistic Regression – Concepts, Implementation, and Evaluation.
Session 2: Advanced Models – Decision Trees, Random Forests, Gradient Boosting, and XGBoost.
Session 3: Model Evaluation Metrics – Accuracy, Precision, Recall, ROC, and AUC Curves.
Hands-on: Building and Evaluating Predictive Models in Python and R.

Day 4 – Time Series Forecasting & Model Optimization (6 Hours)

Session 1: Fundamentals of Time Series Data and Forecasting Models (ARIMA, SARIMA).
Session 2: Machine Learning-Based Forecasting using Prophet and LSTM Networks.
Session 3: Hyperparameter Tuning and Cross-Validation Techniques.
Workshop: Forecasting Sales Demand and Revenue Using Historical Data.

Day 5 – Model Deployment, Integration & Capstone Project (6 Hours)

Session 1: Model Deployment – Flask, R Shiny, and RESTful APIs for Enterprise Use.
Session 2: Integrating Predictive Models with Power BI and Tableau for Business Consumption.
Session 3: Capstone Project – End-to-End Predictive Analytics Pipeline for Business Decisioning.
Panel Discussion: The Future of Predictive Analytics – From Machine Learning to Generative AI.

Capstone Project

Participants will develop a comprehensive predictive analytics solution from start to finish. They will preprocess data, build multiple predictive models in Python and R, evaluate performance, and deploy the final model for business use. The capstone will involve a real-world dataset (e.g., customer churn prediction or sales forecasting) and include integration of model outputs into Power BI or Tableau for decision support.

Future Trends in Predictive Analytics

Predictive analytics is evolving rapidly with the integration of AI, cloud computing, and automation. Emerging trends include AutoML, deep learning-based forecasting, and real-time predictive systems embedded into business workflows. Python and R continue to be the leading languages for innovation in analytics, enabling hybrid AI models and integration with cloud services like Azure ML, AWS Sagemaker, and Google Vertex AI. Professionals with strong foundations in predictive analytics will play key roles in driving data-driven enterprise transformation across industries.