Hyperparameter-Tuning

Predicting Life Expectancy

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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