diff --git a/Forecast/Sales Forecast for retail store/README.md b/Forecast/Sales Forecast for retail store/README.md index 0b6efb082a3ed72a123300d1c458a31acd1a30f9..f4d42be58f0c22ce472966202c60de007303c216 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 bc7b0ba6ad5e1c44a60d65b045031b80fc859f89..1f35d990e421351bcf08d4a3d5f13bb55104fb75 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