Predictive Analytics And Modeling With Python

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

What you'll 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

Requirements

The pre requisites for this course includes a basic statistical knowledge and details on software like SPSS or SAS or STATA.

Description

What is Predictive ModelingPredictive 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 ModelingPredictive 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 ModelingData Analysis and ManipulationVisualizationStatisticsHypothesis TestingPre requisites for taking this courseThe pre requisites for this course includes a basic statistical knowledge and details on software like Python.Target Audience for this courseThis course is more suitable for students or researchers who are interested in learning about predictive analytics.Predictive Modeling Course ObjectivesAfter the completion of this course you will be able toUnderstand how to use predictive analytics tools to solve real time business problemsLearn about predictive models like regression, clustering and othersUse predictive analytics techniques to interpret model outputsWhat is Predictive ModelingPredictive 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 ModelThe predictive models are used to analyze the past performance to predict the future results. There are several steps involved in building a predictive modelPre ProcessingData MiningResults validationUnderstand business and dataPrepare dataModel dataEvaluationDeploymentMonitor and improve

Overview

Section 1: Introduction and Installation

Lecture 1 Introduction to Predictive Modelling with Python

Lecture 2 Installation

Section 2: Data Pre Processing

Lecture 3 Data Pre Proccessing

Lecture 4 Dataframe

Lecture 5 Imputer

Lecture 6 Create Dumies

Lecture 7 Splitting Dataset

Lecture 8 Features Scaling

Section 3: Linear Regression

Lecture 9 Introduction to Linear Regression

Lecture 10 Estimated Regression Model

Lecture 11 Import the Library

Lecture 12 Plot

Lecture 13 Tip Example

Lecture 14 Print Function

Section 4: Salary Prediction

Lecture 15 Introduction to Salary Dataset

Lecture 16 Fitting Linear Regression

Lecture 17 Fitting Linear Regression Continue

Lecture 18 Prediction from the Model

Lecture 19 Prediction from the Model Continue

Section 5: Profit Prediction

Lecture 20 Introduction to Multiple Linear Regression

Lecture 21 Creating Dummies

Lecture 22 Removing one Dummy and Splitting Dataset

Lecture 23 Training Set and Predictions

Lecture 24 Stats Models to Make Optimal Model

Lecture 25 Steps to Make Optimal Model

Lecture 26 Making Optimal Model by Backward Elimination

Lecture 27 Adjusted R Square

Lecture 28 Final Optimal Model Implementation

Section 6: Boston Housing

Lecture 29 Introduction to Jupyter Notebook

Lecture 30 Understanding Dataset and Problem Statement

Lecture 31 Working with Correlation Plots

Lecture 32 Working with Correlation Plots Continue

Lecture 33 Correlation Plot and Splitting Dataset

Lecture 34 MLR Model with Sklearn and Predictions

Lecture 35 MLR model with Statsmodels and Predictions

Lecture 36 Getting Optimal model with Backward Elimination Approach

Lecture 37 RMSE Calculation and Multicollinearity Theory

Lecture 38 VIF Calculation

Lecture 39 VIF and Correlation Plots

Section 7: Logistic Regression

Lecture 40 Introduction to Logistic Regression

Lecture 41 Understanding Problem Statement and Splitting

Lecture 42 Scaling and Fitting Logistic Regression Model

Lecture 43 Prediction and Introduction to Confusion Matrix

Lecture 44 Confusion Matrix Explanation

Lecture 45 Checking Model Performance using Confusion Matrix

Lecture 46 Plots Understanding

Lecture 47 Plots Understanding Continue

Section 8: Diabetes

Lecture 48 Introduction and data Preprocessing

Lecture 49 Fitting Model with Sklearn Library

Lecture 50 Fitting Model with Statmodel Library

Lecture 51 Using Statsmodel Package

Lecture 52 Backward Elimination Approach

Lecture 53 Backward Elimination Approach Continue

Lecture 54 More on Backward Elimination Approach

Lecture 55 Final Model

Lecture 56 ROC Curves

Lecture 57 Threshold Changing

Lecture 58 Final Predictions

Section 9: Credit Risk

Lecture 59 Intro to Credit Risk

Lecture 60 Label Encoding

Lecture 61 Gender Variable

Lecture 62 Dependents and Educationvariable

Lecture 63 Missing Values Treatment in Self Employed Variable

Lecture 64 Outliers Treatment in ApplicantIncome Variable

Lecture 65 Missing Values

Lecture 66 Property Area Variable

Lecture 67 Splitting Data

Lecture 68 Final Model and Area under ROC Curve

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