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Predictive Analytics and Modeling with Python
Understand how to use predictive analytics tools to solve real time business problems

What you will learn

Understand how to use predictive analytics tools to solve real time business problems

Learn about predictive models like regression, clustering and others

Use predictive analytics techniques to interpret model outputs

Learn Data Analysis and Manipulation, Visualization, Statistics, Hypothesis Testing

Description

What is Predictive Modeling

Predictive modeling is the process of creating, testing and validating a model. It uses statistics to predict the outcomes. Predictive modeling has different methods like machine learning, artificial intelligence and others. This model is made up of number of predictors which are likely to affect the future results. Predictive modeling is most widely used in information technology.

Uses of Predictive Modeling

Predictive modeling is the most commonly used statistical technique to predict the future behaviour. Predictive modeling analyzes the past performance to predict the future behaviour.

Features in Predictive Modeling

  • Data Analysis and Manipulation
  • Visualization
  • Statistics
  • Hypothesis Testing

Pre requisites for taking this course

The pre requisites for this course includes a basic statistical knowledge and details on software like Python.

Target Audience for this course


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This course is more suitable for students or researchers who are interested in learning about predictive analytics.

Predictive Modeling Course Objectives

After the completion of this course you will be able to

  • Understand how to use predictive analytics tools to solve real time business problems
  • Learn about predictive models like regression, clustering and others
  • Use predictive analytics techniques to interpret model outputs

What is Predictive Modeling

Predictive analytics is an emerging strategy across many business sectors and they are used to improve the performance of the companies. Predictive modeling is a part of predictive analytics which is used to create a statistical model to predict the future behaviour. The predictive modeling can be used on any type of event regardless of its occurrence. The predictive model to be used for a particular situation is often selected on the basis of the detection theory. This chapter includes an overview of predictive analytics and predictive modeling. This chapter also includes examples of predictive modeling.

How to Build a Predictive Model

The predictive models are used to analyze the past performance to predict the future results. There are several steps involved in building a predictive model

  • Pre Processing
  • Data Mining
  • Results validation
  • Understand business and data
  • Prepare data
  • Model data
  • Evaluation
  • Deployment
  • Monitor and improve
English
language

Content

Introduction and Installation

Introduction to Predictive Modelling with Python
Installation

Data Pre Processing

Data Pre Proccessing
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 Educationvariable
Missing Values Treatment in Self Employed Variable
Outliers Treatment in ApplicantIncome Variable
Missing Values
Property Area Variable
Splitting Data
Final Model and Area under ROC Curve