Credit Risk Prediction Project From Scratch in Python

Credit Risk Prediction Project From Scratch in Python, available at $59.99, has an average rating of 4.05, with 6 lectures, based on 25 reviews, and has 3104 subscribers.

You will learn about Dataset Setup Data Cleaning Plotting Model Creation Testing the Data This course is ideal for individuals who are Beginner Machine learning developers curious about Data science It is particularly useful for Beginner Machine learning developers curious about Data science.

Enroll now: Credit Risk Prediction Project From Scratch in Python

Summary

Title: Credit Risk Prediction Project From Scratch in Python

Price: $59.99

Average Rating: 4.05

Number of Lectures: 6

Number of Published Lectures: 6

Number of Curriculum Items: 6

Number of Published Curriculum Objects: 6

Original Price: ?799

Quality Status: approved

Status: Live

What You Will Learn

Dataset Setup

Data Cleaning

Plotting

Model Creation

Testing the Data

Who Should Attend

Beginner Machine learning developers curious about Data science

Target Audiences

Beginner Machine learning developers curious about Data science

This course consist of two parts: Problem statement explanation and Solution explanation with source code.

Part 1: This is the introduction part of the CREDIT RISK PREDICTION Project where we provide the details and procedures of the coming project that we will build in Part2 of this Project. This is based on prediction of defaulters in bank credit based on the data provided by the bank using past analysis. The result of this project will be that we will be able to forecast what are the chances of a person with certain credentials that will be a defaulter or a successful player.

Part 2: This is the second part of the CREDIT RISK PREDICTION Project where we create a complete project on Kaggle Community Platform regarding prediction of Credit Failure of customers based on their credentials. We use data cleaning, data plotting and utilised Random Forest Classifier, Support Vector Machine and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.

Whom is This Course for?

Aspiring machine learning students want to learn on machine learning projects but struggle hard to find interesting ideas and how to build the project. How should students build Machine learning projects, find data science or machine learning project ideas that motivate you, when deciding on a machine project to get started. You can decide the domain and dataset based on your interest. Size of the dataset and complexity of the dataset. If you are a fresher or a beginner, We recommend you get started with ML projects that focus on data cleaning and then move on to analytics, machine learning, and deep learning

Thanks & Regard

Jitendra

Course Curriculum

Chapter 1: Introduction

Lecture 1: Problem Statement Explanation – Credit Risk Prediction in Python

Lecture 2: [Solution] – Getting started and Import important libraries

Lecture 3: [Solution] – Visualization of Data

Lecture 4: [Solution] – Training , Testing and Create Machine Learning model

Lecture 5: [Solution] – SVM and Logistic Regression Model

Lecture 6: Source Code and Dataset

Instructors

Jitendra Singh
Edtech company

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 4 votes

4 stars: 11 votes

5 stars: 8 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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