Statistics For Data Science with Python — Statistic Distribution (6/10)
Normal Distribution
Scipy — Python for Data Science
Numpy— Python for Data Science
Type of Distributions
Statistics Skew
Distribuição Normal Padronizada
Z-Score
Teorema Central do Limite
SCIPY.Stats
Naïve Bayes e distribuições
Conversão Atributos Categóricos => Numéricos Discreto
[Google Colaboratory
Edit descriptioncolab.research.google.com](https://colab.research.google.com/drive/11ls3eL-LkHWOytWPvPC4Gv_xBFFNM8Ww#scrollTo=rkz8kpdZnRym&line=1&uniqifier=1 "https://colab.research.google.com/drive/11ls3eL-LkHWOytWPvPC4Gv_xBFFNM8Ww#scrollTo=rkz8kpdZnRym&line=1&uniqifier=1")
Aprendizagem Baseada em Distâncias — KNN
Linear Regression
Skewed Data with Machine Learning
[Transforming Skewed Data
towardsdatascience.com](https://towardsdatascience.com/transforming-skewed-data-73da4c2d0d16 "https://towardsdatascience.com/transforming-skewed-data-73da4c2d0d16")
Neural Networks Initiators
Initializers
Normality Tests
- Parametric statistics: the data is in some distribution, usually the normal distribution.
- Non-parametric statistics: data is in another (or unknown) distribution
- If the data is “normal”, we use parametric statistics. Otherwise, we use non-parametric statistics.
Shapiro-Wilk Test
Shapiro-Wilk Test
p-value is used to interpret the statistical test.
p <= alpha: rejects hypothesis, not normal
p > alpha: don’t reject the hypothesis, it’s normal
