data_science_module

It focused on easy and fast use by modularization from data preprocessing to modeling.

Module Information

• LabelEncoder

  • input parameter : org_df
  • org_df (type: DataFrame) >> Target of LabelEncoding
  • output : Return DataFrame after LabelEncoding

• MinMaxScaler

  • input parameter : org_df, target, showPlot
  • org_df (type: DataFrame) >> Target of MinMaxScaling
  • target (type: String) >> Feature name of target value
  • showPlot (type: bool, default=False) >> Whether to plot the result
  • output : Return DataFrame that after MinMaxScaling. Drawing graph based on whether or not plotting

• RobustScaler

  • input parameter : org_df, target, showPlot
  • org_df (type: DataFrame) >> Target of RobustScaling
  • target (type: String) >> Feature name of target value
  • showPlot (type: bool, default=False) >> Whether to plot the result
  • output : Return DataFrame that after RobustScaling. Drawing graph based on whether or not plotting

• StandardScaler

  • input parameter : org_df, target, showPlot
  • org_df (type: DataFrame) >> Target of StandardScaling
  • target (type: String) >> Feature name of target value
  • showPlot (type: Bool, default=False) >> Whether to plot the result
  • output : Return DataFrame that after StandardScaling. Drawing graph based on whether or not plotting

• DecisionTree

  • input parameter : scaled_df, target, test_size, shuffle, criterion, showPlot
  • scaled_df (type: DataFrame) >> Target of DecisionTree that after Scaling
  • target (type: String) >> Feature name of target value
  • test_size (type: Float, default: 0.25) >> Specify testset ratio when training_test_split
  • shuffle (type: Bool, default: False) >> Specify whether shuffle when training_test_split
  • criterion (type: String, default: 'gini') >> Determine the type of criterion used in Decision Tree
  • showPlot (type: bool, default=False) >> Whether to plot the result
  • output : Show DecisionTree score and confision matrix. Drawing tree based on whether or not plotting

• KNN

  • input parameter : scaled_df, target, test_size, shuffle, k
  • scaled_df (type: DataFrame) >> Target of DecisionTree that after Scaling
  • target (type: String) >> Feature name of target value
  • test_size (type: Float, default: 0.25) >> Specify testset ratio when training_test_split
  • shuffle (type: Bool, default: False) >> Specify whether shuffle when training_test_split
  • k (type: Int, default: 3) >> Specify a value for n_neighbors in KNN
  • output : Show KNN score evaluated by cross_validation=5 and confision matrix

• LinearReg

  • input parameter : scaled_df, target, test_size, shuffle
  • scaled_df (type: DataFrame) >> Target of DecisionTree that after Scaling
  • target (type: String) >> Feature name of target value
  • test_size (type: Float, default: 0.25) >> Specify testset ratio when training_test_split
  • shuffle (type: Bool, default: False) >> Specify whether shuffle when training_test_split
  • output : Show regression score