Predictive Modeling with Python

Predictive Modeling with Python, available at $59.99, has an average rating of 3.85, with 68 lectures, based on 74 reviews, and has 16406 subscribers.

You will learn about 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. This course is ideal for individuals who are This Predictive Modeling with Python Course can be taken up by anyone who shares a decent amount of interest in this field. The earlier someone starts the further they can reach. In the case of students who are pursuing a course in statistics, or computer science graduates it is a very good opportunity to direct your career in that direction. As this is a much demand skill every IT professional is looking for a good switch and entering the domain of predictive analysis. or Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, Statistician It is particularly useful for This Predictive Modeling with Python Course can be taken up by anyone who shares a decent amount of interest in this field. The earlier someone starts the further they can reach. In the case of students who are pursuing a course in statistics, or computer science graduates it is a very good opportunity to direct your career in that direction. As this is a much demand skill every IT professional is looking for a good switch and entering the domain of predictive analysis. or Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, Statistician.

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Summary

Title: Predictive Modeling with Python

Price: $59.99

Average Rating: 3.85

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

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.

Who Should Attend

This Predictive Modeling with Python Course can be taken up by anyone who shares a decent amount of interest in this field. The earlier someone starts the further they can reach. In the case of students who are pursuing a course in statistics, or computer science graduates it is a very good opportunity to direct your career in that direction. As this is a much demand skill every IT professional is looking for a good switch and entering the domain of predictive analysis.

Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, Statistician

Target Audiences

This Predictive Modeling with Python Course can be taken up by anyone who shares a decent amount of interest in this field. The earlier someone starts the further they can reach. In the case of students who are pursuing a course in statistics, or computer science graduates it is a very good opportunity to direct your career in that direction. As this is a much demand skill every IT professional is looking for a good switch and entering the domain of predictive analysis.

Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, Statistician

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.

Course Curriculum

Chapter 1: Introduction and Installation

Lecture 1: Introduction to Predictive Modelling with Python

Lecture 2: Installation

Chapter 2: Data Preprocessing

Lecture 1: Data Preprocessing

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 Education Variable

Lecture 5: Missing Values Treatment in Self Employed Variable

Lecture 6: Outliers Treatment in Applicant Income Variable

Lecture 7: Missing Values

Lecture 8: Property Area Variable

Lecture 9: Splitting Data

Lecture 10: Final Model and Area under ROC Curve

Instructors

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Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 12 votes

4 stars: 24 votes

5 stars: 34 votes

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