Supervised-Learning

Perceptron

classificationneuronslinearbinary

Origins

The perceptron learning algorithm is the most simple algorithm we have for Binary Classification.

It was introduced by Frank Rosenblatt in his seminal paper: "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain" in 1958. The history however dates back further to the theoretical foundations of Warren McCulloch and Walter Pitts in 1943 and their paper "A Logical Calculus of the Ideas Immanent in Nervous Activity". The interested reader may visit these links for annotations and the original pdfs.

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Predicting Life Expectancy

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
Index(['Country', 'Year', 'Status', 'Life expectancy ', 'Adult Mortality',
       'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B',
       'Measles ', ' BMI ', 'under-five deaths ', 'Polio', 'Total expenditure',
       'Diphtheria ', ' HIV/AIDS', 'GDP', 'Population',
       ' thinness  1-19 years', ' thinness 5-9 years',
       'Income composition of resources', 'Schooling'],
      dtype='object')

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

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