Machine Learning

Recall that a Neural Network follows the following construction:

1. Pass data (forward) through model to get predicted values
2. Calculate loss with predicted values against labels
3. Perform backpropagation w.r.t each weight / bias to get the direction in which to move that weight such that it moves closer to the global minima
4. Update parameters with gradients using an optimiser.

ball's pace slows down this makes total fkn sense! if the gradient signs are the same, increasing your confidence in that direction and move further. you want to take less steps over all

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.

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: