Description

1. Data Science and Machine Learning Algorithms Recap:

   • Regression Techniques: Linear regression, neural networks, CART.
   • Classification Methods: Logistic regression, nearest neighbor, boosting.
   • Segmentation Approaches: Hierarchical clustering, K-means, DBSCAN.
   • Rule/Sequence Mining: Apriori algorithm, FP-growth, PrefixSpan.

2. Business Use Cases:

   • Leasing Industry: Predicting resale prices and setting leasing rates.
   • Credit Scoring: Estimating default probabilities to inform lending decisions.

3. Principles of Prediction, Regression, and Classification:

   • Fundamentals of predictive modeling and the distinction between regression and classification.

4. Supervised Machine Learning:

   • Two-Stage Paradigm: Model training with historical data and model application on new data.
   • Example: Resale price forecasting with supervised learning techniques.

5. Algorithm Selection for Supervised Learning:

   • Guidance on choosing between tree-based, regression-type, and ensemble algorithms.

Learning Outcomes

1. Understand Key Concepts:

   • Grasp fundamental principles of predictive analytics, including regression, classification, and segmentation.

2. Familiarize with Various Algorithms:

   • Learn about different machine learning algorithms used in predictive analytics.

3. Apply Predictive Analytics in Business Contexts:

   • Explore real-world business use cases like credit scoring and the leasing industry.

4. Implement Supervised Machine Learning:

   • Comprehend the two-stage paradigm of supervised machine learning.
   • Gain insights into developing and applying predictive models, such as resale price forecasting.

5. Select Appropriate Algorithms:

   • Obtain guidance on choosing suitable supervised learning algorithms for different business needs.

Recommendations

There are no strict prerequisites for starting this section. However, continuous engagement and completing the videos in the recommended section along with the provided PDFs will significantly enhance your understanding of predictive analytics.Upon completion, the next recommended section is "Foundations of Predictive Analytics - Part 2". This will build on the knowledge gained in Part 1, delving deeper into advanced topics and further practical applications of predictive analytics.Actively participate and practice the concepts learned to apply predictive analytics effectively in real-world scenarios.