Predictive Modeling with Python: Apply & Evaluate

位教师：EDUCBA

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5个模块

深入了解一个主题并学习基础知识。

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

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.

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要了解的详细信息

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作业

19 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有5个模块

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.

This comprehensive program begins with the foundations of predictive modeling, guiding learners through data preparation, dummy variables, feature scaling, and basic regression concepts. It progresses to mastering linear regression, covering model fitting, handling multicollinearity, and optimizing models through backward elimination and adjusted R². Learners then enhance their models by analyzing correlations, calculating RMSE, and applying advanced validation techniques. The course deepens into logistic regression, focusing on classification, confusion matrices, ROC curves, and threshold adjustments to evaluate performance effectively. Finally, learners apply their knowledge in a credit risk case study, gaining practical experience with encoding, missing value treatment, outlier handling, and AUC-based evaluation. What makes this course unique is its step-by-step blend of theory and hands-on practice using real datasets, ensuring learners not only understand the concepts but can apply and evaluate predictive models in professional contexts.

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

15个视频总计119分钟

Introduction to Predictive Modelling with Python 6分钟
Installation 9分钟
Data Preproccessing 12分钟
Dataframe 8分钟
Imputer 10分钟
Create Dumies 6分钟
Splitting Dataset 8分钟
Features Scaling 6分钟
Introduction to Linear Regression 9分钟
Estimated Regression Model 8分钟
Import the Library 7分钟
Plot 7分钟
Tip Example 9分钟
Print Function 6分钟
Introduction to Salary Dataset 8分钟

4个作业总计60分钟

Graded-Foundations of Predictive Modeling 30分钟
Getting Started with Python for Prediction 10分钟
Preparing Your Data for Models 10分钟
First Steps with Regression 10分钟

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

15个视频总计125分钟

Fitting Linear Regression 7分钟
Fitting Linear Regression Continue 6分钟
Prediction from the Model 6分钟
Prediction from the Model Continue 7分钟
Introduction to Multiple Linear Regression 7分钟
Creating Dummies 12分钟
Removing one Dummy and Splitting Dataset 7分钟
Training Set and Predictions 7分钟
Stats Models to Make Optimal Model 9分钟
Steps to Make Optimal Model 10分钟
Making Optimal Model by Backward Elimination 9分钟
Adjusted R Square 9分钟
Final Optimal Model Implementation 10分钟
Introduction to Jupyter Notebook 11分钟
Understanding Dataset and Problem Statement 9分钟

4个作业总计60分钟

Graded-Mastering Linear Regression 30分钟
Building Simple Regression Models 10分钟
Handling Dummies and Dataset Splits 10分钟
Towards the Optimal Model 10分钟

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

15个视频总计131分钟

Working with Correlation Plots 7分钟
Working with Correlation Plots Continue 6分钟
Correlation Plot and Splitting Dataset 13分钟
MLR Model with Sklearn and Predictions 6分钟
MLR model with Statsmodels and Predictions 9分钟
Getting Optimal model with Backward Elimination Approach 10分钟
RMSE Calculation and Multicollinearity Theory 9分钟
VIF Calculation 7分钟
VIF and Correlation Plots 9分钟
Introduction to Logistic Regression 9分钟
Understanding Problem Statement and Splitting 11分钟
Scaling and Fitting Logistic Regression Model 5分钟
Prediction and Introduction to Confusion Matrix 11分钟
Confusion Matrix Explanation 6分钟
Checking Model Performance using Confusion Matrix 13分钟

4个作业总计60分钟

Graded-Enhancing Regression Models 30分钟
Exploring Correlations and Predictions 10分钟
Model Refinement and Validation 10分钟
Logistic Regression Essentials 10分钟

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

15个视频总计118分钟

Plots Understanding 6分钟
Plots Understanding Continue 7分钟
Introduction and data Preprocessing 8分钟
Fitting Model with Sklearn Library 6分钟
Fitting Model with Statmodel Library 11分钟
Using Statsmodel Package 6分钟
Backward Elimination Approach 9分钟
Backward Elimination Approach Continue 7分钟
More on Backward Elimination Approach 9分钟
Final Model 10分钟
ROC Curves 9分钟
Threshold Changing 9分钟
Final Predictions 7分钟
Intro to Credit Risk 8分钟
Label Encoding 6分钟

4个作业总计60分钟

Graded-Logistic Regression in Depth 30分钟
Visualizing and Building Logistic Models 10分钟
Model Optimization Techniques 10分钟
Evaluating Logistic Models 10分钟

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.