By the end of this course, learners will be able to identify, apply, analyze, and evaluate predictive analytics techniques using Python. They will gain hands-on skills in data preprocessing, regression modeling, logistic regression, and credit risk analysis, equipping them to solve real-world data challenges with confidence.
Predictive Modeling with Python: Apply & Evaluate
Gain insight into a topic and learn the fundamentals.
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Build and evaluate regression and classification models in Python.
Apply preprocessing, scaling, and feature selection for prediction.
Perform credit risk analysis using logistic regression techniques.
Skills you'll gain
- Pandas (Python Package)
- Scikit Learn (Machine Learning Library)
- Data Processing
- Risk Modeling
- Predictive Analytics
- Feature Engineering
- Correlation Analysis
- Statistical Modeling
- Supervised Learning
- Predictive Modeling
- Regression Analysis
- Statistical Analysis
- Data Transformation
- Data Cleansing
- Machine Learning Methods
- NumPy
Details to know
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Recently updated!
October 2025
Assessments
19 assignments
Taught in English
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There are 5 modules in this course
This module introduces learners to predictive modeling with Python, covering essential installations, preprocessing techniques, and fundamental regression concepts. Learners build a strong foundation in data preparation, feature scaling, and understanding regression basics.
What's included
15 videos4 assignments
This module explores simple and multiple linear regression models, focusing on fitting techniques, dummy variables, and model refinement using backward elimination and adjusted R². Learners gain the ability to build and optimize regression models for accurate predictions.
What's included
15 videos4 assignments
This module deepens regression knowledge with correlation analysis, multicollinearity detection, and performance evaluation using RMSE and VIF. Learners also transition into logistic regression and confusion matrix interpretation.
What's included
15 videos4 assignments
This module provides advanced insights into logistic regression, including model building with Sklearn and Statsmodels, optimization through backward elimination, and performance evaluation using ROC curves and threshold analysis.
What's included
15 videos4 assignments
This capstone module applies predictive modeling techniques to credit risk analysis. Learners preprocess categorical variables, handle missing values and outliers, and build models to assess borrower default probability using ROC and AUC.
What's included
8 videos3 assignments
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