Python Predictive Modeling Masterclass- Hands-On Guide

Python Predictive Modeling Masterclass: Hands-On Guide, available at $19.99, has an average rating of 4.45, with 68 lectures, based on 18 reviews, and has 5258 subscribers.

You will learn about 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. This course is ideal for individuals who are Beginners aspiring to enter the field of data science and predictive modeling. or Professionals looking to enhance their skills in predictive analytics and advance their careers. or Anyone interested in leveraging Python for predictive modeling and data-driven decision-making. or Students and researchers seeking practical knowledge and techniques for analyzing data and making predictions. or Business professionals who want to gain insights from data to drive strategic decision-making and improve business outcomes. It is particularly useful for Beginners aspiring to enter the field of data science and predictive modeling. or Professionals looking to enhance their skills in predictive analytics and advance their careers. or Anyone interested in leveraging Python for predictive modeling and data-driven decision-making. or Students and researchers seeking practical knowledge and techniques for analyzing data and making predictions. or Business professionals who want to gain insights from data to drive strategic decision-making and improve business outcomes.

Enroll now: Python Predictive Modeling Masterclass: Hands-On Guide

Summary

Title: Python Predictive Modeling Masterclass: Hands-On Guide

Price: $19.99

Average Rating: 4.45

Number of Lectures: 68

Number of Published Lectures: 68

Number of Curriculum Items: 68

Number of Published Curriculum Objects: 68

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

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.

Who Should Attend

Beginners aspiring to enter the field of data science and predictive modeling.

Professionals looking to enhance their skills in predictive analytics and advance their careers.

Anyone interested in leveraging Python for predictive modeling and data-driven decision-making.

Students and researchers seeking practical knowledge and techniques for analyzing data and making predictions.

Business professionals who want to gain insights from data to drive strategic decision-making and improve business outcomes.

Target Audiences

Beginners aspiring to enter the field of data science and predictive modeling.

Professionals looking to enhance their skills in predictive analytics and advance their careers.

Anyone interested in leveraging Python for predictive modeling and data-driven decision-making.

Students and researchers seeking practical knowledge and techniques for analyzing data and making predictions.

Business professionals who want to gain insights from data to drive strategic decision-making and improve business outcomes.

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.

Course Curriculum

Chapter 1: Introduction and Installation

Lecture 1: Introduction to Predictive Modelling with Python

Lecture 2: Installation

Chapter 2: Data Pre Processing

Lecture 1: Data Pre Proccessing

Lecture 2: Dataframe

Lecture 3: Imputer

Lecture 4: Create Dumies

Lecture 5: Splitting Dataset

Lecture 6: Features Scaling

Chapter 3: Linear Regression

Lecture 1: Introduction to Linear Regression

Lecture 2: Estimated Regression Model

Lecture 3: Import the Library

Lecture 4: Plot

Lecture 5: Tip Example

Lecture 6: Print Function

Chapter 4: Salary Prediction

Lecture 1: Introduction to Salary Dataset

Lecture 2: Fitting Linear Regression

Lecture 3: Fitting Linear Regression Continue

Lecture 4: Prediction from the Model

Lecture 5: Prediction from the Model Continue

Chapter 5: Profit Prediction

Lecture 1: Introduction to Multiple Linear Regression

Lecture 2: Creating Dummies

Lecture 3: Removing one Dummy and Splitting Dataset

Lecture 4: Training Set and Predictions

Lecture 5: Stats Models to Make Optimal Model

Lecture 6: Steps to Make Optimal Model

Lecture 7: Making Optimal Model by Backward Elimination

Lecture 8: Adjusted R Square

Lecture 9: Final Optimal Model Implementation

Chapter 6: Boston Housing

Lecture 1: Introduction to Jupyter Notebook

Lecture 2: Understanding Dataset and Problem Statement

Lecture 3: Working with Correlation Plots

Lecture 4: Working with Correlation Plots Continue

Lecture 5: Correlation Plot and Splitting Dataset

Lecture 6: MLR Model with Sklearn and Predictions

Lecture 7: MLR model with Statsmodels and Predictions

Lecture 8: Getting Optimal model with Backward Elimination Approach

Lecture 9: RMSE Calculation and Multicollinearity Theory

Lecture 10: VIF Calculation

Lecture 11: VIF and Correlation Plots

Chapter 7: Logistic Regression

Lecture 1: Introduction to Logistic Regression

Lecture 2: Understanding Problem Statement and Splitting

Lecture 3: Scaling and Fitting Logistic Regression Model

Lecture 4: Prediction and Introduction to Confusion Matrix

Lecture 5: Confusion Matrix Explanation

Lecture 6: Checking Model Performance using Confusion Matrix

Lecture 7: Plots Understanding

Lecture 8: Plots Understanding Continue

Chapter 8: Diabetes

Lecture 1: Introduction and data Preprocessing

Lecture 2: Fitting Model with Sklearn Library

Lecture 3: Fitting Model with Statmodel Library

Lecture 4: Using Statsmodel Package

Lecture 5: Backward Elimination Approach

Lecture 6: Backward Elimination Approach Continue

Lecture 7: More on Backward Elimination Approach

Lecture 8: Final Model

Lecture 9: ROC Curves

Lecture 10: Threshold Changing

Lecture 11: Final Predictions

Chapter 9: Credit Risk

Lecture 1: Intro to Credit Risk

Lecture 2: Label Encoding

Lecture 3: Gender Variable

Lecture 4: Dependents and Educationvariable

Lecture 5: Missing Values Treatment in Self Employed Variable

Lecture 6: Outliers Treatment in ApplicantIncome Variable

Lecture 7: Missing Values

Lecture 8: Property Area Variable

Lecture 9: Splitting Data

Lecture 10: Final Model and Area under ROC Curve

Instructors

EDUCBA Bridging the Gap
Learn real world skills online

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5 stars: 12 votes

Frequently Asked Questions

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