From ef64c3bd7b354670cfd11543262af03f4a6d5311 Mon Sep 17 00:00:00 2001 From: Dustin_dusTir <du-wal@web.de> Date: Tue, 15 Feb 2022 16:14:38 +0100 Subject: [PATCH] =?UTF-8?q?=F0=9F=94=A5=20hotfix=20fix=20some=20google=20c?= =?UTF-8?q?olab=20links?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Forecast/Sales Forecast for retail store/README.md | 2 +- Insurance/Insurance Fraud detection/README.md | 7 +++---- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/Forecast/Sales Forecast for retail store/README.md b/Forecast/Sales Forecast for retail store/README.md index 0b6efb0..f4d42be 100644 --- a/Forecast/Sales Forecast for retail store/README.md +++ b/Forecast/Sales Forecast for retail store/README.md @@ -1,7 +1,7 @@ # Sales Forecast for retail store >see __German Version__ [below](#German_version) -<a href="https://colab.research.google.com/github/AlexRossmann/machine-learning-services/blob/main/Forecast/Sales%20Forecast%20for%20retail%20store/notebook.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Notebook In Google Colab"/></a> +<a href="https://colab.research.google.com/github/AlexRossmann/machine-learning-services/blob/main/Forecast/Sales%20Forecast%20for%20retail%20store/notebook_Multiple Linear Regression.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Notebook In Google Colab"/></a> and <a href="https://colab.research.google.com/github/AlexRossmann/machine-learning-services/blob/main/Forecast/Sales%20Forecast%20for%20retail%20store/notebook_Random Forrest Regressor.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Notebook In Google Colab"/></a> diff --git a/Insurance/Insurance Fraud detection/README.md b/Insurance/Insurance Fraud detection/README.md index bc7b0ba..1f35d99 100644 --- a/Insurance/Insurance Fraud detection/README.md +++ b/Insurance/Insurance Fraud detection/README.md @@ -2,11 +2,10 @@ >see __German Version__ [below](#German_version) -<a href="https://colab.research.google.com/github/AlexRossmann/machine-learning-services/blob/main/Insurance/Insurance%20Fraud%20detection/notebook.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Notebook In Google Colab"/></a> +<a href="https://colab.research.google.com/github/AlexRossmann/machine-learning-services/blob/main/Insurance/Insurance%20Fraud%20detection/notebook_1.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Notebook In Google Colab"/></a> - -## Business Understandig +## Business Understanding __Corporation:__ na __Industry:__ Insurance @@ -15,7 +14,7 @@ __Business Objective:__ Through this service, future insurance fraudsters can b __Description:__ The insurance industry has always generated a large amount of data, be it the very personal data of policyholders, statistics on the performance of insurance products, or quite normal business metrics such as revenue, profit, and costs. The insurance sector is therefore predestined for the application of machine learning. At the same time, insurance companies are very popular targets for fraudsters. This can happen in the form of hacker attacks or quite analogously as insurance fraud, for example, in the case of a supposed claim. Since the insurance principle applies to insurance, all insured parties pay in the event of fraud. It harms the entire community. It is therefore particularly important to recognize and prevent fraud. __Solution:__ na -## Data Unterstanding +## Data Understanding __Data Frame:__ Auto Insurance Claims Data __Source:__ Kaggle, „Auto Insurance Claims Data“, https://kaggle.com/buntyshah/auto-insurance-claims-data -- GitLab