LE1

1. Business Understanding

   1. Problem erkennen und analysieren

      1. Descriptive (what is happening), Predictive (what will happen), Prescriptive (what should i do)

2. Data Understanding

   1. Data collection

   2. Data Probleme identifizieren

   3. understanding Data, patterns, principles -> wisdom

   4. Steps of Data Understanding

      1. Collect initial data

      2. explore the data -> Visualisieren

      3. verify qualitiy

      4. find outliers -> bild normal profile, detect anomalies, statistical model

3. Data Preparation

   1. Data Consolidation -> Collect, select, integrate

   2. Data Cleaning -> impute missing values, reduce noise, eliminate

   3. Data Transformation -> normalize, aggregate, construct new attributes

   4. Data Reduction -> reduce variables, cases and balance skewed data

4. Modeling

   1. select modelling technique

   2. generate test design

   3. build model

   4. assess model (rank the models)

5. Evaluation

   1. Evaluate Results

   2. Review Process

   3. Determince next steps -> move on or new data

6. Deployment

   1. Plan Deployment

   2. Plan Monitoring and Maintenance

   3. Produce Final Report

   4. Review Project

      1. Repeatable data mining process implementation

Supervised learning

• ML task of learning a function that maps an input to an output based on example -> ML braucht Beispiele um daraus zu lernen

• Prediction

  • Classification

  • Regression

Unsupervised learning

• Algorithm that learns patterns fron unlabled data -> ML bildet sich eigene Rückschlüsse

• Association

  • Link analysis

  • Sequence Analysis

• Clustering

  • Outlier Analysis

• Interpret the models according to domain knowledge, the data mining success criteria and the desired test design

• Judge the success of the application of modeling and discovery techniques more technically

• Only consider models whereas the evaluation phase also takes into account all other results that were produced in the course of the project