Training on Predictive Analytics using Python

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

Customized Training

This training can be tailored to your institution needs and delivered at a location of your choice upon request.

Requirements

Participants need to be proficient in English.

Training Fee

The fee covers tuition, training materials, refreshments, lunch, and study visits. Participants are responsible for their own travel, visa, insurance, and personal expenses.

Certification

A certificate from Ideal Workplace Solutions is awarded upon successful completion.

Accommodation

Accommodation can be arranged upon request. Contact via email for reservations.

Payment

Payment should be made before the training starts, with proof of payment sent to [email protected].
For further inquiries, please contact us on details below:

Email: [email protected]

Mobile: +254759708394