Pandas

Machine Learning Example with Pandas

2026-01-09
mlpandas

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).

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Predicting Life Expectancy

2026-01-09
linear‑regressionpandasloocvlassoridgeedahyperparameter‑tuning

Intro

The focus here is on EDA (Exploratory Data Analysis) and investigating the best choice for the \(\lambda\) hyperparameter for LASSO and Ridge Regression.

We will be working on the Life Expectancy CSV data obtained from WHO.

Peeking at Data

We begin by viewing the columns of the Life Expectancy Dataframe:

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

pd.options.display.float_format = '{:.2f}'.format
le_df = pd.read_csv("life_expectancy.csv")
le_df.columns

We can then view the range of our life expectancy values with a box plot:

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