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Bowles_2.1_titanic: The Anatomy of a New Problem

Einlesen einer CSV-Datei und untersuchen, was 'drinn ist, incl. beschreibende Statistik.

Wir machen das für so verschiedene Datensätze wie MLPC-Titanic, Rock-vs-Mines u.a.

ipynb: $anaconda/ipynb-tasks/bowles/Bowles_2.1_titanic.ipynb
2019_ws_dsci » DSCI Taskbook » Notebooks Bowles
  • Notebooks Bowles
    • (ipynb)dsci_intro_1
    • (ipynb)dsci_intro_2
    • Bowles_2.1_titanic: The Anatomy of a New Problem
    • Bowles_2.2_rocks: Classification Problems: Detecting Unexploded Mines Using Sonar
    • Bowles_2.3_rocks: Visualizing Properties of the Rocks versus Mines Data Set
    • Bowles_2.4_abalone: Real‐Valued Predictions with Factor Variables: How Old Is Your Abalone?
    • Bowles_2.5_wine: Real‐Valued Predictions Using Real‐Valued Attributes: Calculate How Your Wine Tastes
    • Bowles_2.6_glass: Multiclass Classification Problem: What Type of Glass Is That?
    • Bowles_3.3_rocks: Measuring the Performance of Predictive Models
    • Bowles_3.4_wine_rocks: Achieving Harmony Between Model and Data
    • Bowles_4.3_wine: Solving the Penalized Linear Regression Problem
    • Bowles_4.4_rocks_wine_abalone: Extensions to Linear Regression with Numeric Input
    • Bowles_5.2_wine: Multivariable Regression: Predicting Wine Taste
    • Bowles_5.3_rocks: Binary Classification: Using Penalized Linear Regression to Detect Unexploded Mines
    • Bowles_5.4_rocks: Build a Rocks versus Mines Classifier for Deployment
    • Bowles_5.5_glass: Multiclass Classification: Classifying Crime Scene Glass Samples

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