ZScore

class geoanalytics.normalization.ZScore.ZScore(dataframe)

Bases: object

About this algorithm

Description:

ZScore normalization (also called standard score normalization) scales each feature so that it has a mean of 0 and a standard deviation of 1. This is useful when features are normally distributed and ensures that each contributes equally to distance-based models.

Parameters:

• dataframe (pd.DataFrame): Input DataFrame containing ‘x’, ‘y’ coordinates and feature values.

Attributes:

• df (pd.DataFrame): Input DataFrame with standardized columns (‘x’, ‘y’, …features).

• normalizedDF (pd.DataFrame): Output DataFrame after z-score normalization.

• startTime, endTime (float): Time tracking for execution.

• memoryUSS, memoryRSS (float): Memory usage metrics in kilobytes.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.normalization import ZScore

df = pd.read_csv("input.csv")

normalizer = ZScore(df)

normalized_df = normalizer.run()

normalizer.getRuntime()

normalizer.getMemoryUSS()

normalizer.getMemoryRSS()

normalizer.save("ZScore.csv")

Credits

Developed by Raashika and M. Charan Teja, under the guidance of Professor Rage Uday Kiran.

getMemoryRSS()

Prints the memory usage (RSS) of the process in kilobytes.

getMemoryUSS()

Prints the memory usage (USS) of the process in kilobytes.

getRuntime()

Prints the total runtime of the clustering algorithm.

run()

Applies Z-Score normalization to the dataset.

Returns:

DataFrame with ‘x’, ‘y’, and normalized feature columns.

Return type:

pd.DataFrame

save(outputFile='ZScore.csv')

Saves the Normalized DataFrame to a CSV file.