4.4 out of 5
12 reviews on Udemy

Python Predictive Modeling Masterclass: Hands-On Guide

Learn predictive modeling techniques in Python from data preprocessing to advanced algorithms.
EDUCBA Bridging the Gap
4,690 students enrolled
English [Auto]
Data Preprocessing: Techniques for cleaning, formatting, and organizing data effectively.
Linear Regression: Understanding and implementing linear regression models for predictive analysis.
Logistic Regression: Applying logistic regression for classification tasks and understanding its nuances.
Multiple Linear Regression: Extending regression analysis to multiple predictors for more complex modeling.
Advanced Algorithms: Exploring advanced predictive modeling algorithms such as decision trees, random forests, and gradient boosting.
Model Evaluation: Techniques for evaluating model performance and selecting the most suitable algorithms for specific tasks.
Practical Projects: Hands-on projects and real-world examples to reinforce learning and develop practical skills.
Python Libraries: Utilizing popular Python libraries such as scikit-learn, pandas, and statsmodels for efficient predictive modeling.
Interpretation and Visualization: Interpreting model results and visualizing data insights to communicate findings effectively.
Best Practices: Understanding best practices in predictive modeling, including feature selection, cross-validation, and hyperparameter tuning.

Welcome to the comprehensive course on Predictive Modeling with Python! In this course, you will embark on an exciting journey to master the art of predictive modeling using one of the most powerful programming languages in data science – Python.

Predictive modeling is an indispensable tool in extracting valuable insights from data and making informed decisions. Whether you’re a beginner or an experienced data practitioner, this course is designed to equip you with the essential skills and knowledge to excel in the field of predictive analytics.

We’ll begin by laying down the groundwork in the Introduction and Installation section, where you’ll get acquainted with the core concepts of predictive modeling and set up your Python environment to kickstart your learning journey.

Moving forward, we’ll delve into the intricacies of Data Preprocessing, exploring techniques to clean, manipulate, and prepare data for modeling. You’ll learn how to handle missing values, encode categorical variables, and scale features for optimal performance.

The heart of this course lies in its exploration of various predictive modeling algorithms. You’ll dive into Linear Regression, Logistic Regression, and Multiple Linear Regression, gaining a deep understanding of how these algorithms work and when to apply them to different types of datasets.

Through hands-on projects like Salary Prediction, Profit Prediction, and Diabetes Prediction, you’ll learn to implement predictive models from scratch using Python libraries such as scikit-learn and statsmodels. These projects will not only sharpen your coding skills but also provide you with real-world experience in solving practical data science problems.

By the end of this course, you’ll emerge as a proficient predictive modeler, capable of building and evaluating accurate predictive models to tackle diverse business challenges. Whether you’re aspiring to start a career in data science or looking to enhance your analytical skills, this course will empower you to unlock the full potential of predictive modeling with Python.

Get ready to dive deep into the fascinating world of predictive analytics and embark on a transformative learning journey with us!

Section 1: Introduction and Installation

In this section, students are introduced to the fundamentals of predictive modeling with Python in Lecture 1. Lecture 2 covers the installation process, ensuring all participants have the necessary tools and environments set up for the course.

Section 2: Data Preprocessing

Students learn essential data preprocessing techniques in this section. Lecture 3 focuses on data preprocessing concepts, while Lecture 4 introduces the DataFrame, a fundamental data structure in Python. Lecture 5 covers imputation methods, and Lecture 6 demonstrates how to create dummy variables. Lecture 7 explains the process of splitting datasets, and Lecture 8 covers features scaling for data normalization.

Section 3: Linear Regression

This section delves into linear regression analysis. Lecture 9 introduces linear regression concepts, and Lecture 10 discusses estimating regression models. Lecture 11 focuses on importing libraries, and Lecture 12 demonstrates plotting techniques. Lecture 13 offers a tip example, and Lecture 14 covers printing functions.

Section 4: Salary Prediction

Students apply linear regression to predict salaries in this section. Lecture 15 introduces the salary dataset, followed by fitting linear regression models in Lectures 16 and 17. Lectures 18 and 19 cover predictions from the model.

Section 5: Profit Prediction

Multiple linear regression is explored in this section for profit prediction. Lecture 20 introduces the concept, followed by creating dummy variables in Lecture 21. Lecture 22 covers dataset splitting, and Lecture 23 discusses training sets and predictions. Lectures 24 to 28 focus on building an optimal model using stats models and backward elimination.

Section 6: Boston Housing

This section applies linear regression to predict housing prices. Lecture 29 introduces Jupyter Notebook, and Lecture 30 covers dataset understanding. Lectures 31 to 37 cover correlation plots, model fitting, optimal model creation, and multicollinearity theory.

Section 7: Logistic Regression

Logistic regression analysis is covered in this section. Lecture 40 introduces logistic regression, followed by problem statement understanding in Lecture 41. Lecture 42 covers model scaling and fitting, while Lectures 43 to 47 focus on confusion matrix, model performance, and plot understanding.

Section 8: Diabetes

This section applies predictive modeling to diabetes prediction. Lecture 48 covers dataset preprocessing, followed by model fitting with different libraries in Lectures 49 to 51. Lectures 52 to 58 cover backward elimination, ROC curves, and final predictions.

Section 9: Credit Risk

The final section focuses on credit risk prediction. Lectures 59 to 68 cover label encoding, variable treatments, missing values, outliers, dataset splitting, and final model creation.

Through practical examples and hands-on exercises, students gain proficiency in predictive modeling techniques using Python for various real-world scenarios.

You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
4.4 out of 5
12 Ratings

Detailed Rating

Stars 5
Stars 4
Stars 3
Stars 2
Stars 1


9 hours on-demand video
Certificate of Completion