Predictive Analytics with Orange

Multiday, 16/08/2025 - 27/08/2025

Venue

Tertiary Courses Malaysia G-3A-02, Corporate Office Suite, KL Gateway, No 2, Jalan kerinchi, Gerbang kernichi Lestari, 59200 Kuala Lumpur, Malaysia

Entrance Fee

MYR1,000.00

Category

Business & Professional

Event Type

Class, Course, Training or Workshop

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Schedule

DateTime
16/08/20259:30 AM - 5:30 PM
27/08/20259:30 AM - 5:30 PM
Predictive Analytics with Orange

Discover the realm of predictive analytics with our Predictive Analytics with Orange training at Tertiary Courses. Orange, renowned for its user-friendly interface and comprehensive modules, has emerged as a leading tool in the analytics community. This course ensures participants unravel the extensive capabilities of Orange, enabling them to anticipate trends, patterns, and behaviors from their data.

Begin with a comprehensive overview of Orange and its landscape. Trainees will delve deep into classification, predictive modeling, and regression analysis techniques that power data-driven decisions. Progressing further, participants will explore advanced modules like clustering and image analytics, ultimately culminating in the powerful realm of dimension reduction. Join us and supercharge your predictive analytics skills, turning data insights into actionable strategies.

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant

HRD Corp Claimable Course for Employers Registered with HRD Corp

HRDF claimable

Course Code: M547

Topic 1 Overview of Predictive Analytics and Orange

  • Data Mining Process

  • Introduction to Machine Learning

  • Supervised vs UnSupervised Learnings

  • Overview of Orange

Topic 2: Data Preparation

  • Load Data to Orange

  • Interactive Visualization

  • Filter Data

  • Merge and Concat Data

  • Preprocess Data

  • Feature Statistics

  • Save Data

Topic 3: Regression

  • What is Regression

  • Linear Regression

  • Model Evaluation Metrics for Regression

  • Regularization

Topic 4: Classification

  • What is Classification

  • Classification Algorithms

  • K-Fold Cross Validation

  • Model Evaluation Metrics for Classification

  • Confusion Matrix

  • ROC Analysis for Binary Classification

Topic 5: Clustering

  • What is Clustering

  • K-Means Clustering

  • Silhouette Analysis

  • Hierarchical Clustering

Topic 6: Dimension Reduction

  • What is Dimension Reduction

  • Principal Component Analysis (PCA)

  • Feature Ranking

  • t-SNE and MDS

Topic 7: Association Analysis

  • What is Association Analysis

  • Apriori Algorithm

  • Association Analysis with Orange