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
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您将学到什么
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.
您将获得的技能
- Predictive Analytics
- Statistical Analysis
- Data Transformation
- Correlation Analysis
- Pandas (Python Package)
- Feature Engineering
- Risk Modeling
- NumPy
- Machine Learning Methods
- Data Cleansing
- Scikit Learn (Machine Learning Library)
- Regression Analysis
- Statistical Modeling
- Predictive Modeling
- Supervised Learning
- Data Processing
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最近已更新！
October 2025
作业
19 项作业
授课语言：英语（English）
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该课程共有5个模块
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.
涵盖的内容
15个视频4个作业
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.
涵盖的内容
15个视频4个作业
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.
涵盖的内容
15个视频4个作业
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.
涵盖的内容
15个视频4个作业
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.
涵盖的内容
8个视频3个作业
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