feat: new article

Author: DiogoRibeiro7

_posts/Economics/2024-12-01-forecasting_commodity_prices_using_machine_learning_techniques_and_applications.md

@@ -4,8 +4,7 @@ categories:
 - Economics
 classes: wide
 date: '2024-12-01'
-excerpt: Explore how machine learning can be leveraged to forecast commodity prices,
-  such as oil and gold, using advanced predictive models and economic indicators.
+excerpt: Explore how machine learning can be leveraged to forecast commodity prices, such as oil and gold, using advanced predictive models and economic indicators.
 header:
   image: /assets/images/data_science_13.jpg
   og_image: /assets/images/data_science_13.jpg
@@ -22,15 +21,11 @@ keywords:
 - Markdown
 - Data Science
 - Machine Learning
-seo_description: Learn how machine learning techniques are revolutionizing the forecasting
-  of commodity prices like oil and gold, using advanced predictive models and economic
-  indicators.
+- markdown
+seo_description: Learn how machine learning techniques are revolutionizing the forecasting of commodity prices like oil and gold, using advanced predictive models and economic indicators.
 seo_title: Forecasting Commodity Prices with Machine Learning | Data Science Applications
 seo_type: article
-summary: This article delves into the application of machine learning techniques to
-  forecast commodity prices, such as oil and gold. It discusses the methods, economic
-  indicators used, and the challenges in building predictive models in this complex
-  domain.
+summary: This article delves into the application of machine learning techniques to forecast commodity prices, such as oil and gold. It discusses the methods, economic indicators used, and the challenges in building predictive models in this complex domain.
 tags:
 - Commodity prices
 - Machine learning
@@ -39,6 +34,7 @@ tags:
 - Data science in economics
 - Markdown
 - Data Science
+- markdown
 title: 'Forecasting Commodity Prices Using Machine Learning: Techniques and Applications'
 ---
 

_posts/data science/2019-12-29-understanding_splines_what_they_how_they_used_data_analysis.md

@@ -4,9 +4,7 @@ categories:
 - Data Science
 classes: wide
 date: '2019-12-29'
-excerpt: Splines are powerful tools for modeling complex, nonlinear relationships
-  in data. In this article, we'll explore what splines are, how they work, and how
-  they are used in data analysis, statistics, and machine learning.
+excerpt: Splines are powerful tools for modeling complex, nonlinear relationships in data. In this article, we'll explore what splines are, how they work, and how they are used in data analysis, statistics, and machine learning.
 header:
   image: /assets/images/data_science_19.jpg
   og_image: /assets/images/data_science_19.jpg
@@ -25,15 +23,10 @@ keywords:
 - Go
 - Statistics
 - Machine Learning
-seo_description: Splines are flexible mathematical tools used for smoothing and modeling
-  complex data patterns. Learn what they are, how they work, and their practical applications
-  in regression, data smoothing, and machine learning.
+seo_description: Splines are flexible mathematical tools used for smoothing and modeling complex data patterns. Learn what they are, how they work, and their practical applications in regression, data smoothing, and machine learning.
 seo_title: What Are Splines? A Deep Dive into Their Uses in Data Analysis
 seo_type: article
-summary: Splines are flexible mathematical functions used to approximate complex patterns
-  in data. They help smooth data, model non-linear relationships, and fit curves in
-  regression analysis. This article covers the basics of splines, their various types,
-  and their practical applications in statistics, data science, and machine learning.
+summary: Splines are flexible mathematical functions used to approximate complex patterns in data. They help smooth data, model non-linear relationships, and fit curves in regression analysis. This article covers the basics of splines, their various types, and their practical applications in statistics, data science, and machine learning.
 tags:
 - Splines
 - Regression

_posts/data science/2019-12-30-evaluating_binary_classifiers_imbalanced_datasets.md

@@ -4,9 +4,7 @@ categories:
 - Data Science
 classes: wide
 date: '2019-12-30'
-excerpt: AUC-ROC and Gini are popular metrics for evaluating binary classifiers, but
-  they can be misleading on imbalanced datasets. Discover why AUC-PR, with its focus
-  on Precision and Recall, offers a better evaluation for handling rare events.
+excerpt: AUC-ROC and Gini are popular metrics for evaluating binary classifiers, but they can be misleading on imbalanced datasets. Discover why AUC-PR, with its focus on Precision and Recall, offers a better evaluation for handling rare events.
 header:
   image: /assets/images/data_science_8.jpg
   og_image: /assets/images/data_science_8.jpg
@@ -20,23 +18,16 @@ keywords:
 - Binary classifiers
 - Imbalanced data
 - Machine learning metrics
-seo_description: When evaluating binary classifiers on imbalanced datasets, AUC-PR
-  is a more informative metric than AUC-ROC or Gini. Learn why Precision-Recall curves
-  provide a clearer picture of model performance on rare events.
+seo_description: When evaluating binary classifiers on imbalanced datasets, AUC-PR is a more informative metric than AUC-ROC or Gini. Learn why Precision-Recall curves provide a clearer picture of model performance on rare events.
 seo_title: 'AUC-PR vs. AUC-ROC: Evaluating Classifiers on Imbalanced Data'
 seo_type: article
-summary: In this article, we explore why AUC-PR (Area Under Precision-Recall Curve)
-  is a superior metric for evaluating binary classifiers on imbalanced datasets compared
-  to AUC-ROC and Gini. We discuss how class imbalance distorts performance metrics
-  and provide real-world examples of why Precision-Recall curves give a clearer understanding
-  of model performance on rare events.
+summary: In this article, we explore why AUC-PR (Area Under Precision-Recall Curve) is a superior metric for evaluating binary classifiers on imbalanced datasets compared to AUC-ROC and Gini. We discuss how class imbalance distorts performance metrics and provide real-world examples of why Precision-Recall curves give a clearer understanding of model performance on rare events.
 tags:
 - Binary classifiers
 - Imbalanced data
 - Auc-pr
 - Precision-recall
-title: 'Evaluating Binary Classifiers on Imbalanced Datasets: Why AUC-PR Beats AUC-ROC
-  and Gini'
+title: 'Evaluating Binary Classifiers on Imbalanced Datasets: Why AUC-PR Beats AUC-ROC and Gini'
 ---
 
 When working with binary classifiers, metrics like **AUC-ROC** and **Gini** have long been the default for evaluating model performance. These metrics offer a quick way to assess how well a model discriminates between two classes, typically a **positive class** (e.g., detecting fraud or predicting defaults) and a **negative class** (e.g., non-fraudulent or non-default cases). 

_posts/data science/2020-01-06-role_data_science_predictive_maintenance.md

@@ -4,9 +4,7 @@ categories:
 - Data Science
 classes: wide
 date: '2020-01-06'
-excerpt: Explore the role of data science in predictive maintenance, from forecasting
-  equipment failure to optimizing maintenance schedules using techniques like regression
-  and anomaly detection.
+excerpt: Explore the role of data science in predictive maintenance, from forecasting equipment failure to optimizing maintenance schedules using techniques like regression and anomaly detection.
 header:
   image: /assets/images/data_science_7.jpg
   og_image: /assets/images/data_science_7.jpg
@@ -21,14 +19,10 @@ keywords:
 - Machine learning
 - Predictive analytics
 - Industrial analytics
-seo_description: Discover how data science techniques such as regression, clustering,
-  and anomaly detection optimize predictive maintenance, helping organizations forecast
-  failures and enhance operational efficiency.
+seo_description: Discover how data science techniques such as regression, clustering, and anomaly detection optimize predictive maintenance, helping organizations forecast failures and enhance operational efficiency.
 seo_title: How Data Science Powers Predictive Maintenance
 seo_type: article
-summary: An in-depth look at how data science techniques such as regression, clustering,
-  anomaly detection, and machine learning are transforming predictive maintenance
-  across various industries.
+summary: An in-depth look at how data science techniques such as regression, clustering, anomaly detection, and machine learning are transforming predictive maintenance across various industries.
 tags:
 - Predictive maintenance
 - Machine learning

_posts/statistics/2019-12-28-shapirowilk_test_vs_andersondarling_checking_normality_small_large_samples.md

@@ -4,9 +4,7 @@ categories:
 - Statistics
 classes: wide
 date: '2019-12-28'
-excerpt: Explore the differences between the Shapiro-Wilk and Anderson-Darling tests,
-  two common methods for testing normality, and how sample size and distribution affect
-  their performance.
+excerpt: Explore the differences between the Shapiro-Wilk and Anderson-Darling tests, two common methods for testing normality, and how sample size and distribution affect their performance.
 header:
   image: /assets/images/data_science_20.jpg
   og_image: /assets/images/data_science_20.jpg
@@ -22,22 +20,19 @@ keywords:
 - Large sample size
 - Statistical distribution
 - Python
-seo_description: A comparison of the Shapiro-Wilk and Anderson-Darling tests for normality,
-  analyzing their strengths and weaknesses based on sample size and distribution.
-seo_title: 'Shapiro-Wilk vs Anderson-Darling: Normality Tests for Small and Large
-  Samples'
+- python
+seo_description: A comparison of the Shapiro-Wilk and Anderson-Darling tests for normality, analyzing their strengths and weaknesses based on sample size and distribution.
+seo_title: 'Shapiro-Wilk vs Anderson-Darling: Normality Tests for Small and Large Samples'
 seo_type: article
-summary: This article compares the Shapiro-Wilk and Anderson-Darling tests, emphasizing
-  how sample size and distribution characteristics influence the choice of method
-  when assessing normality.
+summary: This article compares the Shapiro-Wilk and Anderson-Darling tests, emphasizing how sample size and distribution characteristics influence the choice of method when assessing normality.
 tags:
 - Normality testing
 - Shapiro-wilk test
 - Anderson-darling test
 - Sample size
 - Python
-title: 'Shapiro-Wilk Test vs. Anderson-Darling: Checking for Normality in Small vs.
-  Large Samples'
+- python
+title: 'Shapiro-Wilk Test vs. Anderson-Darling: Checking for Normality in Small vs. Large Samples'
 ---
 
 ## Shapiro-Wilk Test vs. Anderson-Darling: Checking for Normality in Small vs. Large Samples

_posts/statistics/2019-12-31-deep_dive_into_why_multiple_imputation_indefensible.md

@@ -4,8 +4,7 @@ categories:
 - Statistics
 classes: wide
 date: '2019-12-31'
-excerpt: Let's examine why multiple imputation, despite being popular, may not be
-  as robust or interpretable as it's often considered. Is there a better approach?
+excerpt: Let's examine why multiple imputation, despite being popular, may not be as robust or interpretable as it's often considered. Is there a better approach?
 header:
   image: /assets/images/data_science_20.jpg
   og_image: /assets/images/data_science_20.jpg
@@ -18,14 +17,10 @@ keywords:
 - Missing data
 - Single stochastic imputation
 - Deterministic sensitivity analysis
-seo_description: Exploring the issues with multiple imputation and why single stochastic
-  imputation with deterministic sensitivity analysis is a superior alternative.
+seo_description: Exploring the issues with multiple imputation and why single stochastic imputation with deterministic sensitivity analysis is a superior alternative.
 seo_title: 'The Case Against Multiple Imputation: An In-depth Look'
 seo_type: article
-summary: Multiple imputation is widely regarded as the gold standard for handling
-  missing data, but it carries significant conceptual and interpretative challenges.
-  We will explore its weaknesses and propose an alternative using single stochastic
-  imputation and deterministic sensitivity analysis.
+summary: Multiple imputation is widely regarded as the gold standard for handling missing data, but it carries significant conceptual and interpretative challenges. We will explore its weaknesses and propose an alternative using single stochastic imputation and deterministic sensitivity analysis.
 tags:
 - Multiple imputation
 - Missing data

_posts/statistics/2020-01-01-causality_correlation.md

@@ -4,8 +4,7 @@ categories:
 - Statistics
 classes: wide
 date: '2020-01-01'
-excerpt: Understand how causal reasoning helps us move beyond correlation, resolving
-  paradoxes and leading to more accurate insights from data analysis.
+excerpt: Understand how causal reasoning helps us move beyond correlation, resolving paradoxes and leading to more accurate insights from data analysis.
 header:
   image: /assets/images/data_science_4.jpg
   og_image: /assets/images/data_science_1.jpg
@@ -19,14 +18,10 @@ keywords:
 - Berkson's paradox
 - Correlation
 - Data science
-seo_description: Explore how causal reasoning, through paradoxes like Simpson's and
-  Berkson's, can help us avoid the common pitfalls of interpreting data solely based
-  on correlation.
+seo_description: Explore how causal reasoning, through paradoxes like Simpson's and Berkson's, can help us avoid the common pitfalls of interpreting data solely based on correlation.
 seo_title: 'Causality Beyond Correlation: Understanding Paradoxes and Causal Graphs'
 seo_type: article
-summary: An in-depth exploration of the limits of correlation in data interpretation,
-  highlighting Simpson's and Berkson's paradoxes and introducing causal graphs as
-  a tool for uncovering true causal relationships.
+summary: An in-depth exploration of the limits of correlation in data interpretation, highlighting Simpson's and Berkson's paradoxes and introducing causal graphs as a tool for uncovering true causal relationships.
 tags:
 - Simpson's paradox
 - Berkson's paradox

_posts/statistics/2020-01-02-maximum_likelihood_estimation_statistical_modeling.md

@@ -4,9 +4,7 @@ categories:
 - Statistics
 classes: wide
 date: '2020-01-02'
-excerpt: Discover the fundamentals of Maximum Likelihood Estimation (MLE), its role
-  in data science, and how it impacts businesses through predictive analytics and
-  risk modeling.
+excerpt: Discover the fundamentals of Maximum Likelihood Estimation (MLE), its role in data science, and how it impacts businesses through predictive analytics and risk modeling.
 header:
   image: /assets/images/data_science_3.jpg
   og_image: /assets/images/data_science_3.jpg
@@ -22,20 +20,19 @@ keywords:
 - Mle
 - Bash
 - Python
-seo_description: Explore Maximum Likelihood Estimation (MLE), its importance in data
-  science, machine learning, and real-world applications.
+seo_description: Explore Maximum Likelihood Estimation (MLE), its importance in data science, machine learning, and real-world applications.
 seo_title: 'MLE: A Key Tool in Data Science'
 seo_type: article
-summary: This article covers the essentials of Maximum Likelihood Estimation (MLE),
-  breaking down its mathematical foundation, importance in data science, practical
-  applications, and limitations.
+summary: This article covers the essentials of Maximum Likelihood Estimation (MLE), breaking down its mathematical foundation, importance in data science, practical applications, and limitations.
 tags:
 - Statistical modeling
 - Bash
 - Maximum likelihood estimation
 - Data science
 - Mle
 - Python
+- python
+- bash
 title: 'Maximum Likelihood Estimation (MLE): Statistical Modeling in Data Science'
 ---
 

_posts/statistics/2020-01-03-assessing_goodnessoffit_nonparametric_data.md

@@ -4,9 +4,7 @@ categories:
 - Statistics
 classes: wide
 date: '2020-01-03'
-excerpt: The Kolmogorov-Smirnov test is a powerful tool for assessing goodness-of-fit
-  in non-parametric data. Learn how it works, how it compares to the Shapiro-Wilk
-  test, and explore real-world applications.
+excerpt: The Kolmogorov-Smirnov test is a powerful tool for assessing goodness-of-fit in non-parametric data. Learn how it works, how it compares to the Shapiro-Wilk test, and explore real-world applications.
 header:
   image: /assets/images/data_science_3.jpg
   og_image: /assets/images/data_science_3.jpg
@@ -20,15 +18,10 @@ keywords:
 - Non-parametric statistics
 - Distribution fitting
 - Shapiro-wilk test
-seo_description: This article introduces the Kolmogorov-Smirnov test for assessing
-  goodness-of-fit in non-parametric data, comparing it with other tests like Shapiro-Wilk,
-  and exploring real-world use cases.
+seo_description: This article introduces the Kolmogorov-Smirnov test for assessing goodness-of-fit in non-parametric data, comparing it with other tests like Shapiro-Wilk, and exploring real-world use cases.
 seo_title: 'Kolmogorov-Smirnov Test: A Guide to Non-Parametric Goodness-of-Fit Testing'
 seo_type: article
-summary: This article explains the Kolmogorov-Smirnov (K-S) test for assessing the
-  goodness-of-fit of non-parametric data. We compare the K-S test to other goodness-of-fit
-  tests, such as Shapiro-Wilk, and provide real-world use cases, including testing
-  whether a dataset follows a specific distribution.
+summary: This article explains the Kolmogorov-Smirnov (K-S) test for assessing the goodness-of-fit of non-parametric data. We compare the K-S test to other goodness-of-fit tests, such as Shapiro-Wilk, and provide real-world use cases, including testing whether a dataset follows a specific distribution.
 tags:
 - Kolmogorov-smirnov test
 - Goodness-of-fit tests

_posts/statistics/2020-01-04-multiple_comparisons_problem_bonferroni_correction_other_solutions.md

@@ -4,9 +4,7 @@ categories:
 - Statistics
 classes: wide
 date: '2020-01-04'
-excerpt: The multiple comparisons problem arises in hypothesis testing when performing
-  multiple tests increases the likelihood of false positives. Learn about the Bonferroni
-  correction and other solutions to control error rates.
+excerpt: The multiple comparisons problem arises in hypothesis testing when performing multiple tests increases the likelihood of false positives. Learn about the Bonferroni correction and other solutions to control error rates.
 header:
   image: /assets/images/data_science_6.jpg
   og_image: /assets/images/data_science_6.jpg
@@ -21,15 +19,10 @@ keywords:
 - False discovery rate
 - Hypothesis testing
 - Python
-seo_description: This article explains the multiple comparisons problem in hypothesis
-  testing and discusses solutions such as Bonferroni correction, Holm-Bonferroni,
-  and FDR, with practical applications in fields like medical studies and genetics.
+seo_description: This article explains the multiple comparisons problem in hypothesis testing and discusses solutions such as Bonferroni correction, Holm-Bonferroni, and FDR, with practical applications in fields like medical studies and genetics.
 seo_title: 'Understanding the Multiple Comparisons Problem: Bonferroni and Other Solutions'
 seo_type: article
-summary: This article explores the multiple comparisons problem in hypothesis testing,
-  discussing solutions like the Bonferroni correction, Holm-Bonferroni method, and
-  False Discovery Rate (FDR). It includes practical examples from experiments involving
-  multiple testing, such as medical studies and genetics.
+summary: This article explores the multiple comparisons problem in hypothesis testing, discussing solutions like the Bonferroni correction, Holm-Bonferroni method, and False Discovery Rate (FDR). It includes practical examples from experiments involving multiple testing, such as medical studies and genetics.
 tags:
 - Multiple comparisons problem
 - Bonferroni correction