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Statistics on customs
Início
Track01
The Problem
Designing the solution
Steps to solve the problem
Starting code journey
Getting Pairs
Read WITS data
Mirror of a pair
AllpairsMirror
Python Codes - Book Sample
Track02
Create your first Choropleth map
Create dictionaries: JSON and GeoJSON
Read and clean immigration data
Fusion of GeoJson and immigration data
Create your first map with pinpoints
Using WITS DATA to build a bar graph
Insert a bar graph in a pinpoint map
Obtaining the risk of each country
Fusion of risk and geometry data
Drawing route between two ports
Track03
Descriptive and Inference Statistics
Variables Types: Qualitative x Quantitative
Types of descriptive statistics
Central tendency of data and skewness
Main measures of variability
What is an outlier
Read and clean UK data
Build Bar graph
Numerical measures
Box-plot graph for all data
Box-plot graph for a selection
Track04
Experiment observation and sample
Venn and Tree Diagram
Simple and Composite Events
Law of large numbers
Three probability definitions
Frequency and empirical probability
Coin simulator
Six-face dice simulator
Frequencies from data
Frequency of categories from tables
Simple and marginal probabilties
Conditional probabilities
Probability Independence
Multiplication rule
Bayes Theorem
Combinations and permutations
Track05
Example of random variables
Probability of events to random variables
PMF versus CDF
Discrete uniform distribution
Bernoulli distribution
Binomial distribution
Hypergeometric distribution
Poisson distribution
Find fraud as a sum of Bernoulli
Maps and probabilities
Hypergeometric inspections
Track06
Continous random distribution of probability
Normal distribution of probability
Standard Normal distribution of probability
Inverse standard normal distribution
Student T distribution
Inverse student T distribution
Weight dimension and value per HS6
How to fit a distribution
Employing standard deviation
Total time spent in a system
Application of Gaussian Mixture
Gaussian Mixture on OCDB database
Track07
Types of inspection
Central Limit Theorem
Building a CLT Simulator
More results on CLT
Confidence interval and normal distribution
Applying normal confidence interval
Normal versus Student's T distributions
Confidence interval and Student T distribution
Applying Student T confidence interval
Estimating sample size using normal distribution
Estimating sample size using Student T distribution
Estimating proportion using samples
Confidence interval for weight of HS6 code
Sample size for weight of HS6 code
Inspection and proportion estimate
Track08
Defining Statistical Test of Hypothesis
Numerical example of test of Hypothesis: mean
Code for test of hypothesis to mean
Code for Right tailed mean test
Code for Left tailed mean test
Code for small sample of hypothesis to mean
P-Value and Test of Hypothesis
Statistical power and power analysis
Shapiro Wilk for normality test
Shapiro Wilk to verify CLT Simulator
Shapiro Wilk for HS6 code weight samples
Test of hypothesis for weight of HS6 code
Track09
Linear regression: concepts and equations
Linear regression: numerical example
Correlation is not causation
Dummy and categorical variables
Multiple linear regression
Dummy Multiple linear regression
Predicting Exportation & Importation Volume
Predictions on Cumulative Probability
Multiple Linear Regression Philippine Revenue
Trading indicators to predict EDBS
Track10
Regression versus Classification
Train-Test Split
Parameter versus Hyperparameter
Training, Validation and Test
Neural Networks
Garson's Approach
Clustering Algorithms
ROC curve
Ensembles
Gaussian Mixture x K-means on HS6 Weight
Evaluation of Classification Methods
Comparing Logistic, Neural, and Ensemble
Fruits or not, split or encode and scale first?
Fruits, Ensemble, and ROC Curve: Mix up all!
Possible themes for future discussions
Track11
A review on Parametric Statistics
Parametric tests for Hypothesis Testing
Parametric vs. Non-Parametric Tests
One sample z-test and their relation with two-sample z-test
One sample t-test and their relation with two-sample t-test
Welch's two-sample t-test: two populations with different variances
Non-Parametric test for Hypothesis Testing: Mann-Whitney U Test
Non-Parametric test for Hypothesis Testing: Wilcoxon Sign-Rank Test
Non-Parametric test for Hypothesis Testing: Wilcoxon Sign Test
Non-Parametric test for Hypothesis Testing: Chi-Square Goodness-of-fit
Non-Parametric test for Hypothesis Testing: Kolmogorov-Smirnov
Non-Parametric for comparing machine learning methods
Using Wilcoxon Sign Test to compare clustering methods
Using Wilcoxon Sign-Rank Test to compare clustering methods
What is A/B testing and how to combine with hypothesis testing?
Using Chi-Square fit to check if Benford-Law holds or not
Using Kolmogorov-Smirnov fit to check if Pareto holds or not
Discount vs. No Discount: non-parametric tests
Track12
Building Knowledge Chatbots
Ecommerce free collection databases
Synthetic data generation using Faker
Synthetic data generation using Bayesian
Capstone Project 1
Capstone Project 2
Capstone Project 3
Customs Revenues Prediction
Track13
Track14
Track15
Track16
Track17
Map of Tracks Topics
Week1
W1-1.1
W1-1.2
W1-1.3
W1-1.4
W1-2.1
W1-2.2
W1-2.3
W1-2.4
W1-3.1
W1-4.1
W1-4.2
W1-4.3
W1-4.4
Week2
W2-1.1
W2-1.2
W2-1.3
W2-1.4
W2-1.5
W2-1.6
W2-1.7
W2-2.1
W2-2.2
W2-2.3
W2-2.4
W2-2.5
W2-3.1
W2-3.2
Week3
W3-1.1
W3-1.2
W3-1.3
W3-1.4
W3-2.1
W3-2.2
W3-2.3
W3-2.4
W3-2.5
Week4
W4-1.1
W4-1.2
W4-1.3
W4-1.4
W4-2.1
W4-2.2
W4-2.3
W4-2.4
W4-3.1
W4-3.2
Week5
W5-1.1
W5-1.2
W5-1.3
W5-1.4
W5-2.1
W5-2.2
W5-2.3
W5-2.4
W5-3.1
Notebooks of the Book
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18
Chapter 19
Chapter 20
Python Online
Mind Maps
Statistics on customs
Track 17
LLMs, RAG and Agents
Apresentação com conceitos sobre uso de LLMs
NotebookLM explicativo sobre os conceitos
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