Predictive Modeling with Python

Think with a predictive mindset and understand well the basics of the techniques used in prediction with this course

3.40 (69 reviews)
Udemy
platform
English
language
Data Science
category
instructor
Predictive Modeling with Python
16,382
students
9.5 hours
content
Jun 2021
last update
$59.99
regular price

What you will learn

Learn the predictive modeling in python, linear regression, logistic regression, the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, stats model package, etc.

You will be guided through the installation of the required software. Data Pre-processing, which includes Data frame, splitting dataset, feature scaling, etc. You will gain an edge on Linear Regression, Salary Prediction, Logistic Regression. You will get to work on various datasets dealing with Credit Risk and Diabetes.

Description

Predictive Modeling is the use of data and statistics to predict the outcome of the data models. This prediction finds its utility in almost all areas from sports, to TV ratings, corporate earnings, and technological advances. Predictive modeling is also called predictive analytics. With the help of predictive analytics, we can connect data to effective action about the current conditions and future events. Also, we can enable the business to exploit patterns and which are found in historical data to identify potential risks and opportunities before they occur. Python is used for predictive modeling because Python-based frameworks give us results faster and also help in the planning of the next steps based on the results.

Our course ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction. Critical thinking is very important to validate models and interpret the results. Hence, our course material emphasizes on hardwiring this similar kind of thinking ability. You will have good knowledge about the predictive modeling in python, linear regression, logistic regression, the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, stats model package, etc.

In this course, you will get an introduction to Predictive Modelling with Python. You will be guided through the installation of the required software. Data Pre-processing, which includes Data frame, splitting dataset, feature scaling, etc. You will gain an edge on Linear Regression, Salary Prediction, Logistic Regression. You will get to work on various datasets dealing with Credit Risk and Diabetes.

Content

Introduction and Installation

Introduction to Predictive Modelling with Python
Installation

Data Preprocessing

Data Preprocessing
Dataframe
Imputer
Create Dumies
Splitting Dataset
Features Scaling

Linear Regression

Introduction to Linear Regression
Estimated Regression Model
Import the Library
Plot
Tip Example
Print Function

Salary Prediction

Introduction to Salary Dataset
Fitting Linear Regression
Fitting Linear Regression Continue
Prediction from the Model
Prediction from the Model Continue

Profit Prediction

Introduction to Multiple Linear Regression
Creating Dummies
Removing one Dummy and Splitting Dataset
Training Set and Predictions
Stats Models to Make Optimal Model
Steps to Make Optimal Model
Making Optimal Model by Backward Elimination
Adjusted R Square
Final Optimal Model Implementation

Boston Housing

Introduction to Jupyter Notebook
Understanding Dataset and Problem Statement
Working with Correlation Plots
Working with Correlation Plots Continue
Correlation Plot and Splitting Dataset
MLR Model with Sklearn and Predictions
MLR model with Statsmodels and Predictions
Getting Optimal model with Backward Elimination Approach
RMSE Calculation and Multicollinearity Theory
VIF Calculation
VIF and Correlation Plots

Logistic Regression

Introduction to Logistic Regression
Understanding Problem Statement and Splitting
Scaling and Fitting Logistic Regression Model
Prediction and Introduction to Confusion Matrix
Confusion Matrix Explanation
Checking Model Performance using Confusion Matrix
Plots Understanding
Plots Understanding Continue

Diabetes

Introduction and data Preprocessing
Fitting Model with Sklearn Library
Fitting Model with Statmodel Library
Using Statsmodel Package
Backward Elimination Approach
Backward Elimination Approach Continue
More on Backward Elimination Approach
Final Model
ROC Curves
Threshold Changing
Final Predictions

Credit Risk

Intro to Credit Risk
Label Encoding
Gender Variable
Dependents and Education Variable
Missing Values Treatment in Self Employed Variable
Outliers Treatment in Applicant Income Variable
Missing Values
Property Area Variable
Splitting Data
Final Model and Area under ROC Curve

Reviews

Ulday
May 19, 2023
Very hard to understand the accent, and the subtitles often get it wrong. Hope I'll become familiar with it along the way.
Omar
April 6, 2022
Good class. Data not available , nor code. For the diabetes set, I tracked it to kaggle's diabetes.csv
Alokkumar
August 10, 2021
Excellent Teaching. This course really helps out for those who want to pursue their career in Predictive Analytics and Machine Learning
Alvin
July 10, 2021
It is good lesson, but my advice to provide the dataset that would be used in the course, so the audience can try it themselves in their own computer or laptop

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4136520
udemy ID
6/21/2021
course created date
7/7/2021
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