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ml_classification_kickstarter's Issues

Data Exploration

  • Check for correlation
  • Create "fast" plots
  • Check data distribution
  • Check balancing of categories
  • Check hypothesis (still feasible?)

Evaluation

  • Set benchmark model (dummyclassifier)
  • Check defined metric
  • Define standardized evaluation functions
  • Analyze a subgroup of errors
  • Partial dependence plots (?)
  • Computation time (issue?)
  • Show difference between the threee selected models

Data Mining

  • Merge CSV Data
  • Check different csv files for integrity
  • check column names
  • Update Requirements.txt

Data cleaning

  • Create MASTER notebook
  • Create "personal" notebooks
  • Find strategy how to deal with NAN, missing values, catergorical variables...
  • Define use of "log scaling" dependent on features
  • Check for data leakage

Predictive Modeling

  • Which classifier to use?
  • Build three different models
  • Split train/test
  • Use of pipeline? (scaling)?

Data Visualization

  • Seaborn & Matplotlib
  • Create meaningfull plot first, then beautify them
  • Uniform colormap/palette
  • Error plot (?)
  • Use plot to validate or reject hypothesis

Final Test

  • Check errors on train / test data
  • Evaluate Bias / Variance
  • Plot confusion matrix, check evaluation metric

Feature Engineering

  • Necessary?
  • String extraction?
  • Polinominal features?
  • Länge der Beschreibung?
  • Check data leakage

Model- and Hyperparametertuning

  • Define hyperparameters subject to to optimization
  • Random search followed by Gridsearch (?)
  • Documentation of hyperparameters of best model
  • Upsampling / downsampling (?)

Create Slides

  • Use google slides
  • Define storytelling
  • Define audience
  • Present extra findings

Business Understanding

  • Get background information
  • Formulate hypothesis
  • Read through Kaggle competition comments?
  • Are there other things to predict besides funding?
  • What metric to use?

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