This page contains results and explanations of benchmarking metrics for my hardware:
Ml
2026-04-29
Source: https://www.w3resource.com/python-exercises/pandas/pandas-machine-learning-integration.php
Structure of data.csv:
ID Name Age Gender Salary Target
1,Sara,25,Female,50000,0
2,Ophrah,30,Male,60000,1
3,Torben,22,Male,70000,0
4,Masaharu,35,Male,80000,1
5,Kaya,NaN,Female,55000,0
6,Abaddon,29,Male,NaN,1
Column Description:
ID: A unique identifier for each record (integer). Name: The name of the individual (string). Age: Age of the individual (numerical, may have missing values). Gender: Gender of the individual (categorical: Male/Female). Salary: The individual’s salary (numerical, may have missing values). Target: The target variable for binary classification (binary: 0 or 1).
Data Types
| category | types |
|---|---|
| text | str |
| numeric | int, float, complex |
| sequence | list, tuple, range |
| mapping | dict |
| set | set, frozenset |
| boolean | bool |
| binary | bytes, bytearray, memoryview |
| none | NoneType |
Keywords
Python reserves 35 hard keywords (plus the soft keywords match and case for structural pattern matching since 3.10). Reserved keywords cannot be used as identifiers; soft keywords are only special in the relevant context.