feat: new article

DiogoRibeiro7 · DiogoRibeiro7 · commit fd2fcc6a5835 · 2024-10-09T15:39:21.000+01:00
diff --git a/_posts/2020-01-03-assessing_goodness-of-fit_non-parametric_data.md b/_posts/2020-01-03-assessing_goodness-of-fit_non-parametric_data.md
@@ -2,8 +2,6 @@
 author_profile: false
 categories:
 - Statistics
-- Data Science
-- Machine Learning
 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.
diff --git a/_posts/2020-01-04-multiple_comparisons_problem:_bonferroni_correction_other_solutions.md b/_posts/2020-01-04-multiple_comparisons_problem:_bonferroni_correction_other_solutions.md
@@ -2,8 +2,6 @@
 author_profile: false
 categories:
 - Statistics
-- Data Science
-- Hypothesis Testing
 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.
diff --git a/_posts/2020-03-01-type_one_type_two_erros.md b/_posts/2020-03-01-type_one_type_two_erros.md
@@ -2,8 +2,6 @@
 author_profile: false
 categories:
 - Statistics
-- Data Science
-- Hypothesis Testing
 classes: wide
 date: '2020-03-01'
 excerpt: Explore Type I and Type II errors in hypothesis testing. Learn how to balance error rates, interpret significance levels, and understand the implications of statistical errors in real-world scenarios.
diff --git a/_posts/2020-03-30-sustainability_analytics_how_data_science_drives_green_innovation.md b/_posts/2020-03-30-sustainability_analytics_how_data_science_drives_green_innovation.md
@@ -1,9 +1,7 @@
 ---
 author_profile: false
 categories:
-- Sustainability
 - Data Science
-- Green Technology
 classes: wide
 date: '2020-03-30'
 excerpt: Data science is a key driver of sustainability, offering insights that help optimize resources, reduce waste, and improve the energy efficiency of supply chains.
diff --git a/_posts/2020-04-01-the_friedman_test.md b/_posts/2020-04-01-the_friedman_test.md
@@ -1,9 +1,7 @@
 ---
 author_profile: false
 categories:
-- Statistics
 - Data Analysis
-- Non-Parametric Tests
 classes: wide
 date: '2020-04-01'
 excerpt: The Friedman test is a non-parametric alternative to repeated measures ANOVA, designed for use with ordinal data or non-normal distributions. Learn how and when to use it in your analyses.
diff --git a/_posts/2020-04-27-prediction_errors_bias_variance_model.md b/_posts/2020-04-27-prediction_errors_bias_variance_model.md
@@ -1,9 +1,7 @@
 ---
 author_profile: false
 categories:
-- Mathematics
 - Machine Learning
-- Data Science
 classes: wide
 date: '2020-04-27'
 excerpt: Learn about different methods for estimating prediction error, addressing the bias-variance tradeoff, and how cross-validation, bootstrap methods, and Efron & Tibshirani's .632 estimator help improve model evaluation.
diff --git a/_posts/2020-05-01-shapiro_wilk_test.md b/_posts/2020-05-01-shapiro_wilk_test.md
@@ -2,8 +2,6 @@
 author_profile: false
 categories:
 - Statistics
-- Data Analysis
-- Hypothesis Testing
 classes: wide
 date: '2020-05-01'
 excerpt: Learn about the Shapiro-Wilk and Anderson-Darling tests for normality, their differences, and how they guide decisions between parametric and non-parametric statistical methods.
diff --git a/_posts/2020-07-26-measurement_errors.md b/_posts/2020-07-26-measurement_errors.md
@@ -1,10 +1,7 @@
 ---
 author_profile: false
 categories:
-- Mathematics
 - Statistics
-- Data Science
-- Data Analysis
 classes: wide
 date: '2020-07-26'
 excerpt: Explore the different types of observational errors, their causes, and their impact on accuracy and precision in various fields, such as data science and engineering.
diff --git a/_posts/2020-09-01-threshold_classification_zero_inflated_time_series.md b/_posts/2020-09-01-threshold_classification_zero_inflated_time_series.md
@@ -1,7 +1,6 @@
 ---
 author_profile: false
 categories:
-- Data Science
 - Time Series Analysis
 classes: wide
 date: '2020-09-01'
diff --git a/_posts/2020-09-02-log_rank_test_survival_analysis_comparing_survival_curves.md b/_posts/2020-09-02-log_rank_test_survival_analysis_comparing_survival_curves.md
@@ -1,8 +1,6 @@
 ---
 author_profile: false
 categories:
-- Biostatistics
-- Medical Research
 - Data Science
 classes: wide
 date: '2020-09-02'
diff --git a/_posts/2020-10-01-time_series_models_predicting_emergency.md b/_posts/2020-10-01-time_series_models_predicting_emergency.md
@@ -1,7 +1,6 @@
 ---
 author_profile: false
 categories:
-- Healthcare Analytics
 - Machine Learning
 classes: wide
 date: '2020-10-01'
diff --git a/_posts/2020-12-01-predictive_maintenance_data_science.md b/_posts/2020-12-01-predictive_maintenance_data_science.md
@@ -2,7 +2,6 @@
 author_profile: false
 categories:
 - Data Science
-- Predictive Maintenance
 classes: wide
 date: '2020-12-01'
 excerpt: Learn how data science revolutionizes predictive maintenance through key techniques like regression, anomaly detection, and clustering to forecast machine failures and optimize maintenance schedules.
diff --git a/_posts/2021-01-01-pde_data_science.md b/_posts/2021-01-01-pde_data_science.md
@@ -1,7 +1,6 @@
 ---
 author_profile: false
 categories:
-- Data Science
 - Mathematics
 classes: wide
 date: '2021-01-01'
diff --git a/_posts/2021-02-17-traffic_safety_kde.md b/_posts/2021-02-17-traffic_safety_kde.md
@@ -1,8 +1,7 @@
 ---
 author_profile: false
 categories:
-- Traffic Safety
-- Urban Planning
+- Data Science
 classes: wide
 date: '2021-02-17'
 excerpt: A deep dive into using Kernel Density Estimation (KDE) for identifying traffic accident hotspots and improving road safety, including practical applications and case studies from Japan.
diff --git a/_posts/2021-03-01-polynomial_regression.md b/_posts/2021-03-01-polynomial_regression.md
@@ -1,7 +1,6 @@
 ---
 author_profile: false
 categories:
-- Data Science
 - Machine Learning
 classes: wide
 date: '2021-03-01'
diff --git a/_posts/2021-03-01-type_1_type_2_errors.md b/_posts/2021-03-01-type_1_type_2_errors.md
@@ -1,7 +1,6 @@
 ---
 author_profile: false
 categories:
-- Data Science
 - Statistics
 classes: wide
 date: '2021-03-01'
diff --git a/_posts/2021-04-01-asymmetric_confidence_interval.md b/_posts/2021-04-01-asymmetric_confidence_interval.md
@@ -2,7 +2,6 @@
 author_profile: false
 categories:
 - Statistics
-- Data Analysis
 classes: wide
 date: '2021-04-01'
 excerpt: Discover the reasons behind asymmetric confidence intervals in statistics and how they impact research interpretation.
diff --git a/_posts/2021-04-27-forest_fires_kde.md b/_posts/2021-04-27-forest_fires_kde.md
@@ -1,9 +1,7 @@
 ---
 author_profile: false
 categories:
-- Forest Fire Management
-- GIS
-- Environmental Science
+- Data Science
 classes: wide
 date: '2021-04-27'
 excerpt: A study using GIS-based techniques for forest fire hotspot identification and analysis, validated with contributory factors like population density, precipitation, elevation, and vegetation cover.
diff --git a/_posts/2024-05-10-survival_analysis.md b/_posts/2024-05-10-survival_analysis.md
@@ -1,9 +1,7 @@
 ---
 author_profile: false
 categories:
-- Mathematics
 - Statistics
-- Data Science
 classes: wide
 date: '2024-05-10'
 excerpt: Explore the role of survival analysis in management, focusing on time-to-event data and techniques like the Kaplan-Meier estimator and Cox proportional hazards model for business decision-making.
diff --git a/_posts/2024-06-30-RSSI_humanbody.md b/_posts/2024-06-30-RSSI_humanbody.md
@@ -1,9 +1,7 @@
 ---
 author_profile: false
 categories:
-- Wireless Communication
 - Signal Processing
-- Network Engineering
 classes: wide
 date: '2024-06-30'
 excerpt: Explore the impact of human presence on RSSI and the challenges it introduces, along with effective mitigation strategies in wireless communication systems.
diff --git a/files_with_multiple_categories.txt b/files_with_multiple_categories.txt
@@ -0,0 +1,144 @@
+2024-06-14-matthew_correlation.md
+2021-05-26-kernel_math.md
+2024-05-17-markov_chain.md
+2020-03-30-sustainability_analytics:_how_data_science_drives_green_innovation.md
+2020-09-02-log_rank_test_survival_analysis_comparing_survival_curves.md
+2024-08-25-vehicle_routing_problem.md
+2024-07-09-error_bars.md
+2021-04-30-big_data_climate_change_mitigation.md
+2024-07-15-outlier_detection_doping.md
+2024-10-01-automated_prompt_engineering.md
+2020-04-01-the_friedman_test.md
+2021-01-01-pde_data_science.md
+2021-03-01-type_1_type_2_errors.md
+2024-06-30-rssi_body_effects.md
+2024-09-15-forest_fiers.md
+2024-06-07-z-score.md
+2024-09-17-ml_healthcare.md
+2024-07-16-einstein.md
+2024-07-05-savitzky_golay.md
+2024-10-12-how_data_science_reshaping_business_strategy_age_machine_learning.md
+2024-09-03-climate_change.md
+2022-03-23-degrees_freedom.md
+2023-08-21-large_languague_models.md
+2024-08-03-feature_engineering.md
+2024-09-05-real_time_data_streaming.md
+2024-07-18-outlier_pca.md
+2024-10-06-evaluating_distributions.md
+2024-05-10-data_analysis_gdp.md
+2023-12-30-expected_shortfall.md
+2024-09-19-build_ds_team.md
+2024-09-05-detecting_drift.md
+2024-08-24-kruskal_wallis.md
+2024-07-21-iknn.md
+2024-06-04-poisson_distribution.md
+2024-09-03-fundamentals_matter.md
+2024-08-27-coeeficient_variation.md
+2021-05-10-estimating_uncertainty_neural_networks_using_monte_carlo_dropout.md
+2020-10-01-time_series_models_predicting_emergency.md
+2023-09-03-binary_classification.md
+2024-05-21-probability_integral_transform.md
+2024-05-22-research_paper.md
+2024-07-17-outlier_algo.md
+2024-08-24-circular_economy.md
+2024-10-02-entropy.md
+2020-12-01-predictive_maintenance_data_science.md
+2024-06-13-stepwise_regression.md
+2024-05-16-regularization_machine_learning.md
+2024-08-15-structural_equations.md
+2024-06-29-latente.md
+2024-06-29-glm.md
+2023-08-22-paul-erdos.md
+2024-05-09-kernel_clustering_r.md
+2024-09-06-sequential_detection_switches.md
+2023-10-02-overview_natural_language_processing_data_science.md
+2024-10-07-extending_simple_model.md
+2024-07-06-stepwise_selection.md
+2024-06-12-dbscan.md
+2020-04-27-prediction_errors_bias_variance_model.md
+2024-02-20-validate_models.md
+2024-09-01-math_and_music.md
+2024-07-19-clt_revisited.md
+2024-05-20-probability_and_odds.md
+2021-04-01-asymmetric_confidence_interval.md
+2024-06-06-wine_sensory_evaluation.md
+2020-01-03-assessing_goodness-of-fit_non-parametric_data.md
+2020-02-01-anova_kruskal_walis.md
+2024-09-17-feature_engenniring.md
+2024-02-14-advanced_sequential_change-point.md
+2021-12-24-linear_programming.md
+2024-07-13-nilm_algorithms.md
+2024-02-17-climate_var.md
+2024-09-18-baysean_statistics.md
+2021-02-17-traffic_safety_kde.md
+2024-05-10-stratified_sampling.md
+2024-09-24-sample_size_clinical.md
+2020-03-01-type_one_type_two_erros.md
+2024-05-11-importance_sampling.md
+2024-05-22-peer_review.md
+2024-08-16-utility_functions_python.md
+2024-02-01-customer_life_value.md
+2024-06-26-missing_data.md
+2024-11-30-outliers.md
+2024-08-02-drift_tecting.md
+2024-05-15-feature_engineering.md
+2024-09-07-energie_efficiency.md
+2024-07-20-sequential_change.md
+2024-09-04-outlier_detection.md
+2024-06-15-emi_rssi_signal.md
+2024-08-31-pape.md
+2024-05-14-kullback.md
+2024-09-11-cross_validation.md
+2020-01-04-multiple_comparisons_problem:_bonferroni_correction_other_solutions.md
+2024-03-07-ai_history.md
+2024-10-08-implementing_time_series.md
+2024-09-30-ds_projects.md
+2024-06-02-explain_nurse.md
+2024-07-12-nilm.md
+2021-03-01-polynomial_regression.md
+2023-05-05-mean_time_between_failures.md
+2024-09-16-ml_and_forest_fires.md
+2022-09-27-entropy_information_theory.md
+2023-09-20-rolling_windows.md
+2021-04-27-forest_fires_kde.md
+2020-07-26-measurement_errors.md
+2024-07-03-ancova.md
+2021-05-12-understanding_heart_rate_variability_through_lens_coefficient_variation_health_monitoring.md
+2024-09-06-normality.md
+2024-09-10-wilcoxon.md
+2024-05-09-understanding_t-sne.md
+2024-07-04-logram_test.md
+2024-07-13-clt.md
+2024-07-01-lasso.md
+2024-07-14-confidence-intervales.md
+2024-09-12-importance_sampling.md
+2020-09-01-threshold_classification_zero_inflated_time_series.md
+2024-05-14-p_value.md
+2024-06-05-data_science_in_health_tech.md
+2021-09-24-crime_analysis.md
+2024-09-22-randomized_inference.md
+2024-05-15-ai_fairness.md
+2020-05-01-shapiro_wilk_test.md
+2022-03-14-levenes_test_vs._bartletts_test_checking_homogeneity_variances.md
+2024-09-27-entropy_data_science.md
+2024-10-05-simple_distribution.md
+2023-08-12-guassian_processes.md
+2024-06-06-essential_statistical.md
+2024-06-11-survival_analysis.md
+2024-06-19-frequentis_bayesian.md
+2024-09-09-kmeans.md
+2024-08-28-mathematics.md
+2024-08-31-pedestrian_movement.md
+2021-07-26-regression_tasks.md
+2024-08-19-pre_comit_tutorial.md
+2024-05-19-bhattacharyya_distance.md
+2024-06-19-outliers_advanced_topics.md
+2024-09-20-model_customer_behaviour.md
+2024-07-08-psod.md
+2024-07-31-custom_libraries.md
+2024-09-14-ml_supply_chain.md
+2024-05-19-gini_coefficiente.md
+2022-01-02-ols.md
+2024-09-01-graph_theory.md
+2024-07-14-copulas.md
+2024-07-20-fpof.md
diff --git a/markdown_category_checker.py b/markdown_category_checker.py
@@ -0,0 +1,47 @@
+import os
+import re
+import yaml
+from typing import List
+
+def read_markdown_files_from_folder(folder_path: str) -> List[str]:
+    # List all markdown files in the given folder
+    return [f for f in os.listdir(folder_path) if f.endswith('.md')]
+
+def extract_frontmatter(file_content: str) -> dict:
+    # Extract the YAML frontmatter from the markdown file using regex
+    frontmatter_match = re.match(r'---\n(.*?)\n---', file_content, re.DOTALL)
+    if frontmatter_match:
+        frontmatter_str = frontmatter_match.group(1)
+        try:
+            return yaml.safe_load(frontmatter_str)
+        except yaml.YAMLError:
+            return {}
+    return {}
+
+def check_categories(frontmatter: dict) -> bool:
+    # Check if 'categories' key exists and contains more than one element
+    if 'categories' in frontmatter and isinstance(frontmatter['categories'], list):
+        return len(frontmatter['categories']) > 1
+    return False
+
+def process_markdown_files(folder_path: str, output_txt_file: str):
+    markdown_files = read_markdown_files_from_folder(folder_path)
+    files_with_multiple_categories = []
+
+    for md_file in markdown_files:
+        with open(os.path.join(folder_path, md_file), 'r', encoding='utf-8') as file:
+            content = file.read()
+            frontmatter = extract_frontmatter(content)
+            if check_categories(frontmatter):
+                files_with_multiple_categories.append(md_file)
+
+    # Write filenames to output text file
+    with open(output_txt_file, 'w', encoding='utf-8') as output_file:
+        for filename in files_with_multiple_categories:
+            output_file.write(f'{filename}\n')
+
+
+folder_path = './_posts'  # Change this to your folder path
+output_txt_file = 'files_with_multiple_categories.txt'
+process_markdown_files(folder_path, output_txt_file)
+print(f'Processing complete. Files with multiple categories saved to {output_txt_file}')
