Course Overview
This course is designed to provide participants with a comprehensive understanding of predictive analytics and its application using Python. Through hands-on exercises and real-world case studies, participants will learn how to harness the power of data to make informed predictions, improve decision-making processes, and drive business value. The course covers essential techniques such as regression, classification, time series forecasting, and model evaluation, leveraging Python's robust libraries like Pandas, Scikit-learn, and Stats models. By the end of the course, participants will be proficient in building, tuning, and deploying predictive models to solve complex problems in various industries.
Course Duration
10 Days
Who Should Attend
- Data Analysts and Scientists who want to expand their skill set in predictive modeling.
- IT professionals interested in data-driven decision-making.
- Business Analysts seeking to apply predictive analytics in their organizations.
- Statisticians and researchers who wish to leverage Python for advanced analytics.
- Anyone with a foundational knowledge of Python looking to delve into predictive analytics.
Course Objectives
By the end of this course, participants will be able to:
- Understand the key concepts and techniques of predictive analytics.
- Apply Python libraries to manipulate and analyze data for predictive modeling.
- Develop and evaluate predictive models using various algorithms.
- Perform regression, classification, and time series forecasting.
- Implement data preprocessing and feature engineering techniques.
- Interpret model outputs and improve model performance.
- Use cross-validation and hyperparameter tuning to optimize models.
- Deploy predictive models in real-world scenarios.
- Work with large datasets and handle data-related challenges.
- Communicate predictive insights effectively to stakeholders.
Course Outline:
Module 1: Introduction to Predictive Analytics and Python
- Understanding predictive analytics and its applications
- Python environment setup and essential libraries
- Data types, variables, and operators
- Control flow statements and functions
Module 2: Data Acquisition and Exploration
- Data sources and formats (CSV, Excel, databases, APIs)
- Data loading and handling with Pandas
- Exploratory data analysis (EDA) techniques
- Data visualization with Matplotlib and Seaborn
Module 3: Data Preprocessing and Feature Engineering
- Handling missing values, outliers, and inconsistencies
- Data cleaning and transformation
- Feature selection and engineering
- Data normalization and scaling
Module 4: Machine Learning Fundamentals
- Supervised vs. unsupervised learning
- Regression and classification problems
- Model evaluation metrics (accuracy, precision, recall, F1-score)
- Model selection techniques (cross-validation, hyperparameter tuning)
Module 5: Linear Regression
- Simple and multiple linear regression
- Model assumptions and diagnostics
- Feature selection for linear regression
- Model interpretation and evaluation
Module 6: Logistic Regression
- Logistic regression for classification
- Model interpretation and evaluation
- Overcoming limitations (regularization, feature engineering)
Module 7: Decision Trees and Random Forests
- Decision tree algorithms (ID3, C4.5, CART)
- Random forest ensemble method
- Hyperparameter tuning for decision trees and random forests
- Model interpretation and visualization
Module 8: Support Vector Machines (SVM)
- SVM for classification and regression
- Kernel trick and its applications
- Model selection and hyperparameter tuning
- SVM implementation with Python
Module 9: Ensemble Methods
- Bagging and boosting techniques
- Gradient boosting and XGBoost
- Model stacking and blending
- Ensemble model evaluation
Module 10: Model Deployment and Evaluation
- Model deployment options (API, web application, batch scoring)
- Model monitoring and retraining
- Model explainability and interpretability
- Ethical considerations in predictive analytics
