8. Supervised Learning

Supervised learning

• Uses labelled data
• Predictor variables / features or a Target variable
• Classification: Target variable consists of categories (Decision Trees, Random Forests, Naive Bayes, Neural Networks, and Support Vector etc.)
• Regression: Target variable is continuous (Linear vs Logistic)

Exploratory data analysis (EDA)

• Exploratory data analysis (EDA) is an approach to analyzing dataset to summarize the main characteristics with visual methods
• Exploring data frame
  • df.head() # Return the top 5 rows of a data frame
  • df.info() # Return information about the dataframe including the data types of each column
  • df.describe() # Return statistical details like percentile or mean of the data frame
  • df.shape() # Return the number of dimensions in the array
• scikit-learn

from sklearn import datasets

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

plt.style.use(‘ggplot)

Visual Exploratory data analysis (EDA)

plt.figure()

sns.countplot(x='missile', hue='party', data=df, palette='RdBu')

plt.xticks([0,1], ['No', 'Yes'])

plt.show()