Predictive Analytics And Modeling With Python

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.



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