diff --git a/_posts/2020-11-05-probability_theory_basics.md b/_posts/2020-11-05-probability_theory_basics.md
new file mode 100644
index 0000000..4ecfa29
--- /dev/null
+++ b/_posts/2020-11-05-probability_theory_basics.md
@@ -0,0 +1,41 @@
+---
+author_profile: false
+categories:
+- Statistics
+classes: wide
+date: '2020-11-05'
+excerpt: An introduction to probability theory concepts every data scientist should know.
+header:
+  image: /assets/images/data_science_10.jpg
+  og_image: /assets/images/data_science_10.jpg
+  overlay_image: /assets/images/data_science_10.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_10.jpg
+  twitter_image: /assets/images/data_science_10.jpg
+keywords:
+- Probability theory
+- Random variables
+- Distributions
+- Data science
+seo_description: Learn the core principles of probability theory, from random variables to common distributions, with practical examples for data science.
+seo_title: 'Probability Theory Basics for Data Science'
+seo_type: article
+summary: This post reviews essential probability concepts like random variables, expectation, and common distributions, illustrating how they underpin data science workflows.
+tags:
+- Probability
+- Statistics
+- Data science
+title: 'Probability Theory Basics for Data Science'
+---
+
+Probability theory provides the mathematical foundation for modeling uncertainty. By understanding random variables and probability distributions, data scientists can quantify risks and make informed decisions.
+
+## Random Variables and Distributions
+
+A random variable assigns numerical values to outcomes in a sample space. Key distributions such as the binomial, normal, and Poisson describe how probabilities are spread across possible outcomes. Knowing these distributions helps in selecting appropriate models and estimating parameters.
+
+## Expectation and Variance
+
+Two fundamental measures of a random variable are its **expected value** and **variance**. The expected value represents the long-run average, while the variance measures how spread out the outcomes are. These metrics are critical for evaluating models and comparing predictions.
+
+Mastering probability theory enables data scientists to better interpret model outputs and reason about uncertainty in real-world applications.
diff --git a/_posts/2020-11-10-simple_linear_regression_intro.md b/_posts/2020-11-10-simple_linear_regression_intro.md
new file mode 100644
index 0000000..eb1e5b6
--- /dev/null
+++ b/_posts/2020-11-10-simple_linear_regression_intro.md
@@ -0,0 +1,36 @@
+---
+author_profile: false
+categories:
+- Data Science
+classes: wide
+date: '2020-11-10'
+excerpt: Understand how simple linear regression models the relationship between two variables using a single predictor.
+header:
+  image: /assets/images/data_science_11.jpg
+  og_image: /assets/images/data_science_11.jpg
+  overlay_image: /assets/images/data_science_11.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_11.jpg
+  twitter_image: /assets/images/data_science_11.jpg
+keywords:
+- Linear regression
+- Least squares
+- Data analysis
+seo_description: Discover the mechanics of simple linear regression and how to interpret slope and intercept when fitting a straight line to data.
+seo_title: 'A Primer on Simple Linear Regression'
+seo_type: article
+summary: This article introduces simple linear regression and the least squares method, showing how a single predictor explains variation in a response variable.
+tags:
+- Regression
+- Statistics
+- Data science
+title: 'A Primer on Simple Linear Regression'
+---
+
+Simple linear regression is a foundational technique for modeling the relationship between a predictor variable and a response variable. By fitting a straight line, we can quantify how changes in one variable are associated with changes in another.
+
+## The Least Squares Method
+
+The most common approach to estimating the regression line is **ordinary least squares (OLS)**. OLS finds the line that minimizes the sum of squared residuals between the observed data points and the line's predictions. The slope indicates the strength and direction of the relationship, while the intercept shows the expected value when the predictor is zero.
+
+Understanding simple linear regression is a stepping stone toward more complex modeling techniques, providing crucial intuition about correlation and causation.
diff --git a/_posts/2020-11-20-bayesian_inference_basics.md b/_posts/2020-11-20-bayesian_inference_basics.md
new file mode 100644
index 0000000..f1e5057
--- /dev/null
+++ b/_posts/2020-11-20-bayesian_inference_basics.md
@@ -0,0 +1,39 @@
+---
+author_profile: false
+categories:
+- Statistics
+classes: wide
+date: '2020-11-20'
+excerpt: Explore the fundamentals of Bayesian inference and how prior beliefs combine with data to form posterior conclusions.
+header:
+  image: /assets/images/data_science_12.jpg
+  og_image: /assets/images/data_science_12.jpg
+  overlay_image: /assets/images/data_science_12.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_12.jpg
+  twitter_image: /assets/images/data_science_12.jpg
+keywords:
+- Bayesian statistics
+- Priors
+- Posterior distributions
+- Data science
+seo_description: An overview of Bayesian inference, demonstrating how to update prior beliefs with new evidence to make data-driven decisions.
+seo_title: 'Bayesian Inference Explained'
+seo_type: article
+summary: Learn how Bayesian inference updates prior beliefs into posterior distributions, providing a flexible framework for reasoning under uncertainty.
+tags:
+- Bayesian
+- Inference
+- Statistics
+title: 'Bayesian Inference Explained'
+---
+
+Bayesian inference offers a powerful perspective on probability, treating unknown quantities as distributions that update when new evidence appears.
+
+## Priors and Posteriors
+
+The process begins with a **prior distribution** that captures our initial beliefs about a parameter. After observing data, we apply Bayes' theorem to obtain the **posterior distribution**, reflecting how our beliefs should change.
+
+## Why Use Bayesian Methods?
+
+Bayesian techniques are particularly useful when data is scarce or when incorporating domain knowledge is essential. They provide a coherent approach to uncertainty that can complement or outperform classical methods in many situations.
diff --git a/_posts/2020-11-25-hypothesis_testing_real_world_applications.md b/_posts/2020-11-25-hypothesis_testing_real_world_applications.md
new file mode 100644
index 0000000..94c2049
--- /dev/null
+++ b/_posts/2020-11-25-hypothesis_testing_real_world_applications.md
@@ -0,0 +1,39 @@
+---
+author_profile: false
+categories:
+- Statistics
+classes: wide
+date: '2020-11-25'
+excerpt: See how hypothesis testing helps draw meaningful conclusions from data in practical scenarios.
+header:
+  image: /assets/images/data_science_13.jpg
+  og_image: /assets/images/data_science_13.jpg
+  overlay_image: /assets/images/data_science_13.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_13.jpg
+  twitter_image: /assets/images/data_science_13.jpg
+keywords:
+- Hypothesis testing
+- P-values
+- Significance
+- Data science
+seo_description: Learn how to apply hypothesis tests in real-world analyses and avoid common pitfalls when interpreting p-values and confidence levels.
+seo_title: 'Applying Hypothesis Testing in the Real World'
+seo_type: article
+summary: This post walks through frequentist hypothesis testing, showing how to formulate null and alternative hypotheses and interpret the results in practical data science tasks.
+tags:
+- Hypothesis testing
+- Statistics
+- Experiments
+title: 'Applying Hypothesis Testing in the Real World'
+---
+
+Hypothesis testing allows data scientists to objectively assess whether an observed pattern is likely due to chance or reflects a genuine effect.
+
+## Null vs. Alternative Hypotheses
+
+Every test starts with a **null hypothesis**, representing the status quo, and an **alternative hypothesis**, representing a potential effect. By choosing a significance level and calculating a p-value, we can decide whether to reject the null hypothesis.
+
+## Common Pitfalls
+
+Misinterpreting p-values or failing to consider effect sizes can lead to misguided conclusions. Always pair statistical significance with domain context to ensure results are meaningful.
diff --git a/_posts/2020-11-30-data_visualization_best_practices.md b/_posts/2020-11-30-data_visualization_best_practices.md
new file mode 100644
index 0000000..6698e12
--- /dev/null
+++ b/_posts/2020-11-30-data_visualization_best_practices.md
@@ -0,0 +1,41 @@
+---
+author_profile: false
+categories:
+- Data Science
+classes: wide
+date: '2020-11-30'
+excerpt: Discover best practices for creating clear and compelling data visualizations that communicate insights effectively.
+header:
+  image: /assets/images/data_science_14.jpg
+  og_image: /assets/images/data_science_14.jpg
+  overlay_image: /assets/images/data_science_14.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_14.jpg
+  twitter_image: /assets/images/data_science_14.jpg
+keywords:
+- Data visualization
+- Charts
+- Communication
+- Best practices
+seo_description: Guidelines for selecting chart types, choosing colors, and avoiding clutter when visualizing data for stakeholders.
+seo_title: 'Data Visualization Best Practices'
+seo_type: article
+summary: Learn how to design effective visualizations by focusing on clarity, appropriate chart selection, and thoughtful use of color and labels.
+tags:
+- Visualization
+- Data science
+- Communication
+title: 'Data Visualization Best Practices'
+---
+
+Effective data visualization bridges the gap between complex datasets and human understanding. Following proven design principles ensures that your charts highlight the important messages without distractions.
+
+## Choosing the Right Chart
+
+Different data types call for different chart styles. Use bar charts for comparisons, line charts for trends, and scatter plots for relationships. Avoid pie charts when precise comparisons are needed.
+
+## Keep It Simple
+
+Cluttered visuals can obscure the message. Limit the number of colors and remove unnecessary grid lines or 3D effects. Focus the audience's attention on the key insights.
+
+Clear and concise visualizations help stakeholders grasp findings quickly, making your analyses more persuasive and actionable.
diff --git a/_posts/2021-10-05-data_preprocessing_pipelines.md b/_posts/2021-10-05-data_preprocessing_pipelines.md
new file mode 100644
index 0000000..8c6e86e
--- /dev/null
+++ b/_posts/2021-10-05-data_preprocessing_pipelines.md
@@ -0,0 +1,47 @@
+---
+author_profile: false
+categories:
+- Data Science
+classes: wide
+date: '2021-10-05'
+excerpt: Learn how to design robust data preprocessing pipelines that prepare raw data for modeling.
+header:
+  image: /assets/images/data_science_6.jpg
+  og_image: /assets/images/data_science_6.jpg
+  overlay_image: /assets/images/data_science_6.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_6.jpg
+  twitter_image: /assets/images/data_science_6.jpg
+keywords:
+- Data preprocessing
+- Pipelines
+- Data cleaning
+- Feature engineering
+seo_description: Discover best practices for building reusable data preprocessing pipelines that handle missing values, encoding, and feature scaling.
+seo_title: Building Data Preprocessing Pipelines for Reliable Models
+seo_type: article
+summary: This post outlines the key steps in constructing data preprocessing pipelines using tools like scikit-learn to ensure consistent model inputs.
+tags:
+- Data preprocessing
+- Machine learning
+- Feature engineering
+title: Designing Effective Data Preprocessing Pipelines
+---
+
+Real-world datasets rarely come perfectly formatted for modeling. A well-designed **data preprocessing pipeline** ensures that you apply the same transformations consistently across training and production environments.
+
+## Handling Missing Values
+
+Start by assessing the extent of missing data. Common strategies include dropping incomplete rows, filling numeric columns with the mean or median, and using the most frequent category for categorical features.
+
+## Encoding Categorical Variables
+
+Many machine learning algorithms require numeric inputs. Techniques like **one-hot encoding** or **ordinal encoding** convert categories into numbers. Scikit-learn's `ColumnTransformer` allows you to apply different encoders to different columns in a single pipeline.
+
+## Scaling and Normalization
+
+Scaling features to a common range prevents variables with large magnitudes from dominating a model. Standardization (mean of zero, unit variance) is typical for linear models, while min-max scaling keeps values between 0 and 1.
+
+## Putting It All Together
+
+Use scikit-learn's `Pipeline` to chain preprocessing steps with your model. This approach guarantees that the exact same transformations are applied when predicting on new data, reducing the risk of data leakage and improving reproducibility.
diff --git a/_posts/2021-10-15-decision_tree_algorithms.md b/_posts/2021-10-15-decision_tree_algorithms.md
new file mode 100644
index 0000000..303a4fd
--- /dev/null
+++ b/_posts/2021-10-15-decision_tree_algorithms.md
@@ -0,0 +1,43 @@
+---
+author_profile: false
+categories:
+- Machine Learning
+classes: wide
+date: '2021-10-15'
+excerpt: Understand how decision tree algorithms split data and how pruning improves generalization.
+header:
+  image: /assets/images/data_science_7.jpg
+  og_image: /assets/images/data_science_7.jpg
+  overlay_image: /assets/images/data_science_7.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_7.jpg
+  twitter_image: /assets/images/data_science_7.jpg
+keywords:
+- Decision trees
+- Classification
+- Tree pruning
+- Machine learning
+seo_description: Learn the mechanics of decision tree algorithms, including entropy-based splits and pruning techniques that prevent overfitting.
+seo_title: How Decision Trees Work and Why Pruning Matters
+seo_type: article
+summary: This article walks through the basics of decision tree construction and explains common pruning methods to create better models.
+tags:
+- Decision trees
+- Classification
+- Overfitting
+title: Demystifying Decision Tree Algorithms
+---
+
+Decision trees are intuitive models that recursively split data into smaller groups based on feature values. Each split aims to maximize homogeneity within branches while separating different classes.
+
+## Choosing the Best Split
+
+Metrics like **Gini impurity** and **entropy** measure how mixed the classes are in each node. The algorithm searches over possible splits and selects the one that yields the largest reduction in impurity.
+
+## Preventing Overfitting
+
+A tree grown until every leaf is pure often memorizes the training data. **Pruning** removes branches that provide little predictive power, leading to a simpler tree that generalizes better to new samples.
+
+## When to Use Decision Trees
+
+Decision trees handle both numeric and categorical features and require minimal data preparation. They also serve as the building blocks for powerful ensemble methods like random forests and gradient boosting.
diff --git a/_posts/2021-11-10-model_evaluation_metrics.md b/_posts/2021-11-10-model_evaluation_metrics.md
new file mode 100644
index 0000000..f8820a2
--- /dev/null
+++ b/_posts/2021-11-10-model_evaluation_metrics.md
@@ -0,0 +1,44 @@
+---
+author_profile: false
+categories:
+- Machine Learning
+classes: wide
+date: '2021-11-10'
+excerpt: Explore key metrics for evaluating classification and regression models.
+header:
+  image: /assets/images/data_science_8.jpg
+  og_image: /assets/images/data_science_8.jpg
+  overlay_image: /assets/images/data_science_8.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_8.jpg
+  twitter_image: /assets/images/data_science_8.jpg
+keywords:
+- Model evaluation
+- Accuracy
+- Precision
+- Recall
+- Regression metrics
+seo_description: A concise overview of essential metrics like precision, recall, F1-score, and RMSE for measuring model performance.
+seo_title: Essential Metrics for Evaluating Machine Learning Models
+seo_type: article
+summary: Learn how to interpret common classification and regression metrics to choose the best model for your data.
+tags:
+- Accuracy
+- F1-score
+- RMSE
+title: A Guide to Model Evaluation Metrics
+---
+
+Choosing the right evaluation metric is critical for comparing models and selecting the best one for your problem.
+
+## Classification Metrics
+
+- **Accuracy** measures the fraction of correct predictions. It works well when classes are balanced but can be misleading with imbalanced datasets.
+- **Precision** and **recall** capture how well the model retrieves relevant instances without producing too many false positives or negatives. The **F1-score** provides a balance between the two.
+
+## Regression Metrics
+
+- **Mean Absolute Error (MAE)** evaluates the average magnitude of errors.
+- **Root Mean Squared Error (RMSE)** penalizes larger errors more heavily, making it useful when large deviations are particularly undesirable.
+
+Selecting evaluation metrics that align with business goals will help you make informed decisions about which model to deploy.
diff --git a/_posts/2022-10-15-time_series_decomposition.md b/_posts/2022-10-15-time_series_decomposition.md
new file mode 100644
index 0000000..5c8a0cc
--- /dev/null
+++ b/_posts/2022-10-15-time_series_decomposition.md
@@ -0,0 +1,52 @@
+---
+author_profile: false
+categories:
+- Data Science
+- Time Series
+classes: wide
+date: '2022-10-15'
+excerpt: Learn how time series decomposition reveals trend, seasonality, and residual components for clearer forecasting insights.
+header:
+  image: /assets/images/data_science_12.jpg
+  og_image: /assets/images/data_science_12.jpg
+  overlay_image: /assets/images/data_science_12.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_12.jpg
+  twitter_image: /assets/images/data_science_12.jpg
+keywords:
+- Time series
+- Trend
+- Seasonality
+- Forecasting
+- Decomposition
+seo_description: Discover how to separate trend and seasonal patterns from a time series using additive or multiplicative decomposition.
+seo_title: 'Time Series Decomposition Made Simple'
+seo_type: article
+summary: This article explains how decomposing a time series helps isolate long-term trends and recurring seasonal effects so you can model data more effectively.
+tags:
+- Time series
+- Forecasting
+- Data analysis
+- Python
+title: 'Time Series Decomposition: Separating Trend and Seasonality'
+---
+
+Time series data often combine several underlying components: a long-term **trend**, repeating **seasonal** patterns, and random **residual** noise. By decomposing a series into these pieces, you can better understand its behavior and build more accurate forecasts.
+
+## Additive vs. Multiplicative Models
+
+In an **additive** model, the components simply add together:
+
+$$ y_t = T_t + S_t + R_t $$
+
+where $T_t$ is the trend, $S_t$ is the seasonal component, and $R_t$ represents the residuals. A **multiplicative** model instead multiplies these terms:
+
+$$ y_t = T_t \times S_t \times R_t $$
+
+Choose the form that best fits the scale of seasonal fluctuations in your data.
+
+## Extracting the Components
+
+Python libraries like `statsmodels` or `pandas` offer built-in functions to perform decomposition. Once the trend and seasonality are isolated, you can analyze them separately or remove them before applying forecasting models such as ARIMA.
+
+Understanding each component allows you to explain past observations and produce more transparent predictions for future values.
diff --git a/_posts/2025-06-06-exploratory_data_analysis_intro.md b/_posts/2025-06-06-exploratory_data_analysis_intro.md
new file mode 100644
index 0000000..3f7f469
--- /dev/null
+++ b/_posts/2025-06-06-exploratory_data_analysis_intro.md
@@ -0,0 +1,67 @@
+---
+author_profile: false
+categories:
+- Data Science
+classes: wide
+date: '2025-06-06'
+excerpt: Discover the essential steps of Exploratory Data Analysis (EDA) and how to gain insights from your data before building models.
+header:
+  image: /assets/images/data_science_5.jpg
+  og_image: /assets/images/data_science_5.jpg
+  overlay_image: /assets/images/data_science_5.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_5.jpg
+  twitter_image: /assets/images/data_science_5.jpg
+keywords:
+- Exploratory data analysis
+- Data visualization
+- Python
+- Pandas
+- Data cleaning
+seo_description: Learn the fundamentals of Exploratory Data Analysis using Python, including data cleaning, visualization, and summary statistics.
+seo_title: "Beginner's Guide to Exploratory Data Analysis (EDA)"
+seo_type: article
+summary: This guide covers the core principles of Exploratory Data Analysis, demonstrating how to inspect, clean, and visualize datasets to uncover patterns and inform subsequent modeling steps.
+tags:
+- EDA
+- Data science
+- Python
+- Visualization
+title: "Exploratory Data Analysis: A Beginner's Guide"
+---
+
+Exploratory Data Analysis (EDA) is the process of examining a dataset to understand its main characteristics before applying more formal statistical modeling or machine learning. By exploring your data upfront, you can identify patterns, spot anomalies, and test assumptions that might otherwise go unnoticed.
+
+## 1. Inspecting the Data
+
+The first step in EDA is getting to know the dataset. Begin by loading it into a DataFrame with a tool like Pandas. Examine the column names, data types, and a few example rows to confirm that everything loaded correctly. Descriptive statistics such as mean, median, and standard deviation offer a quick snapshot of numerical columns, while frequency tables can help summarize categorical variables.
+
+## 2. Cleaning and Preparing
+
+Real-world datasets often contain missing values, duplicate rows, and inconsistent formats. Cleaning the data involves handling these issues—whether by removing or imputing missing values, correcting data types, or standardizing text fields. Proper cleaning ensures that later analysis is reliable and reproducible.
+
+## 3. Visualizing Distributions and Relationships
+
+Visualization is central to EDA. Histograms and box plots reveal the distribution of numerical variables, while bar charts summarize categorical counts. Scatter plots and correlation matrices help uncover relationships between features. Tools like Matplotlib and Seaborn make it easy to create compelling visualizations that highlight trends and outliers.
+
+## 4. Drawing Initial Conclusions
+
+With the data cleaned and visualized, you can begin forming hypotheses about potential relationships or interesting patterns. These early insights guide further analysis, whether that means feature engineering, model selection, or identifying areas where more data might be needed.
+
+EDA serves as a critical foundation for any data science project. By taking the time to explore your data thoroughly, you set yourself up for more accurate models and better-informed decisions.
+
+## 5. Using Summary Statistics
+
+Summary statistics provide quick insights into the central tendencies and spread of your variables. Simple commands like `describe()` in Pandas generate the mean, median, and interquartile range for each numeric column. You can also calculate correlations to see how variables relate to one another before building more complex models.
+
+## 6. Interactive Notebooks and Dashboards
+
+Interactive tools make EDA more dynamic. Jupyter notebooks let you mix code and commentary so you can document findings as you go. Libraries such as Plotly and Altair add interactivity to your charts, while dashboards in tools like Streamlit or Tableau allow stakeholders to explore the data for themselves.
+
+## 7. Common Pitfalls to Avoid
+
+Conducting EDA can reveal trends, but it is easy to overinterpret them. Avoid drawing definitive conclusions from small samples or ignoring the impact of outliers. Document each transformation so you can reproduce your work and ensure that visualizations are not misleading.
+
+## Conclusion
+
+Exploratory Data Analysis is both an art and a science. By leveraging descriptive statistics, thoughtful visualizations, and interactive tools, you can uncover valuable insights that guide every subsequent step of your project. A disciplined approach to EDA will keep your analyses on track and lead to stronger, more reliable results.
diff --git a/_posts/2025-06-07-why_math_statistics_foundations_data_science.md b/_posts/2025-06-07-why_math_statistics_foundations_data_science.md
new file mode 100644
index 0000000..9c4a84e
--- /dev/null
+++ b/_posts/2025-06-07-why_math_statistics_foundations_data_science.md
@@ -0,0 +1,57 @@
+---
+author_profile: false
+categories:
+- Data Science
+classes: wide
+date: '2025-06-07'
+excerpt: Mastering mathematics and statistics is essential for understanding data science algorithms and avoiding common pitfalls when building models.
+header:
+  image: /assets/images/data_science_10.jpg
+  og_image: /assets/images/data_science_10.jpg
+  overlay_image: /assets/images/data_science_10.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_10.jpg
+  twitter_image: /assets/images/data_science_10.jpg
+keywords:
+- Mathematics for data science
+- Statistics fundamentals
+- Machine learning theory
+- Algorithms
+seo_description: Explore why a solid grasp of math and statistics is crucial for data scientists and how ignoring the underlying theory can lead to faulty models.
+seo_title: 'Math and Statistics: The Bedrock of Data Science'
+seo_type: article
+summary: To excel in data science, you need more than coding skills. This article explains how mathematics and statistics underpin popular algorithms and why understanding them prevents costly mistakes.
+tags:
+- Mathematics
+- Statistics
+- Machine learning
+- Data science
+- Algorithms
+title: 'Why Data Scientists Need Math and Statistics'
+---
+
+A common misconception is that data science is mostly about applying libraries and frameworks. While tools are helpful, they cannot replace a solid understanding of **mathematics** and **statistics**. These disciplines provide the language and theory that power every algorithm behind the scenes.
+
+## The Role of Mathematics
+
+At the core of many machine learning algorithms are mathematical concepts such as **linear algebra** and **calculus**. Linear algebra explains how models handle vectors and matrices, enabling operations like matrix decomposition and gradient calculations. Calculus is vital for understanding optimization techniques that drive model training. Without these foundations, it is difficult to grasp how algorithms converge or why they sometimes fail to do so.
+
+## Why Statistics Matters
+
+Statistics helps data scientists quantify uncertainty, draw reliable conclusions, and validate models. Techniques like **hypothesis testing**, **confidence intervals**, and **probability distributions** reveal whether observed patterns are significant or simply random noise. Lacking statistical insight can lead to overfitting or underestimating model errors.
+
+## Understanding Algorithms Beyond Code
+
+Popular algorithms—such as decision trees, regression models, and neural networks—are built on mathematical principles. Knowing the theory behind them clarifies their assumptions and limitations. Blindly applying a model without understanding its mechanics can produce misleading results, especially when the data violates those assumptions.
+
+## The Pitfalls of Ignoring Theory
+
+When the underlying mathematics is ignored, it becomes challenging to debug models, tune hyperparameters, or interpret outcomes. Relying solely on automated tools may produce working code, but it often masks fundamental issues like data leakage, improper scaling, or incorrect loss functions. These mistakes can have severe consequences in real-world applications.
+
+## Building a Strong Foundation
+
+Learning the basics of calculus, linear algebra, and statistics does not require becoming a mathematician. However, dedicating time to these topics builds intuition about how models work. This deeper knowledge empowers data scientists to select appropriate algorithms, customize them for specific problems, and communicate results effectively.
+
+## Conclusion
+
+Data science thrives on a solid grounding in mathematics and statistics. Understanding the theory behind algorithms not only improves model performance but also safeguards against hidden errors. Investing in these fundamentals is essential for anyone aspiring to be a competent data scientist.
diff --git a/_posts/2025-06-08-data_visualization_tools.md b/_posts/2025-06-08-data_visualization_tools.md
new file mode 100644
index 0000000..bbd7744
--- /dev/null
+++ b/_posts/2025-06-08-data_visualization_tools.md
@@ -0,0 +1,46 @@
+---
+author_profile: false
+categories:
+- Data Science
+classes: wide
+date: '2025-06-08'
+excerpt: Explore top data visualization tools that help analysts turn raw numbers into compelling stories.
+header:
+  image: /assets/images/data_science_11.jpg
+  og_image: /assets/images/data_science_11.jpg
+  overlay_image: /assets/images/data_science_11.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_11.jpg
+  twitter_image: /assets/images/data_science_11.jpg
+keywords:
+- Data visualization tools
+- Dashboards
+- Charts
+- Reporting
+seo_description: Learn about popular data visualization tools and how they aid in communicating insights from complex datasets.
+seo_title: 'Data Visualization Tools for Modern Analysts'
+seo_type: article
+summary: This article reviews leading data visualization platforms and libraries, highlighting their strengths for EDA and reporting.
+tags:
+- Visualization
+- Dashboards
+- Reporting
+- Data science
+title: 'Data Visualization Tools for Modern Data Science'
+---
+
+Data visualization bridges the gap between raw numbers and actionable insights. With the right toolset, analysts can transform spreadsheets and databases into engaging charts and dashboards that reveal hidden patterns.
+
+## 1. Matplotlib and Seaborn
+
+These Python libraries are the bread and butter of many data scientists. Matplotlib offers low-level control for creating virtually any chart, while Seaborn builds on top of it with sensible defaults and statistical plots.
+
+## 2. Plotly and Bokeh
+
+For interactive web-based visualizations, Plotly and Bokeh stand out. They enable dynamic charts that allow users to zoom, hover, and filter, making presentations more engaging and informative.
+
+## 3. Tableau and Power BI
+
+When you need to share results with non-technical stakeholders, business intelligence tools like Tableau and Power BI offer drag-and-drop interfaces and polished dashboards. They integrate well with various data sources and support advanced analytics extensions.
+
+Effective visualization helps convey complex analyses in a format that anyone can understand. Choosing the right tool depends on the audience, data type, and level of interactivity required.
diff --git a/_posts/2025-06-09-feature_engineering_time_series.md b/_posts/2025-06-09-feature_engineering_time_series.md
new file mode 100644
index 0000000..c559a14
--- /dev/null
+++ b/_posts/2025-06-09-feature_engineering_time_series.md
@@ -0,0 +1,46 @@
+---
+author_profile: false
+categories:
+- Machine Learning
+classes: wide
+date: '2025-06-09'
+excerpt: Learn specialized feature engineering techniques to make time series data more predictive for machine learning models.
+header:
+  image: /assets/images/data_science_12.jpg
+  og_image: /assets/images/data_science_12.jpg
+  overlay_image: /assets/images/data_science_12.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_12.jpg
+  twitter_image: /assets/images/data_science_12.jpg
+keywords:
+- Time series features
+- Lag variables
+- Rolling windows
+- Seasonality
+seo_description: Discover practical methods for crafting informative features from time series data, including lags, moving averages, and trend extraction.
+seo_title: 'Feature Engineering for Time Series Data'
+seo_type: article
+summary: This post explains how to engineer features such as lagged values, rolling statistics, and seasonal indicators to improve model performance on sequential data.
+tags:
+- Feature engineering
+- Time series
+- Machine learning
+- Forecasting
+title: 'Crafting Time Series Features for Better Models'
+---
+
+Time series data contains rich temporal information that standard tabular methods often overlook. Careful feature engineering can reveal trends and cycles that lead to more accurate predictions.
+
+## 1. Lagged Variables
+
+One of the simplest yet most effective techniques is creating lag features. By shifting the series backward in time, you supply the model with previous observations that may influence current values.
+
+## 2. Rolling Statistics
+
+Moving averages and rolling standard deviations smooth the data and highlight short-term changes. They help capture momentum and seasonality without introducing noise.
+
+## 3. Seasonal Indicators
+
+Adding flags for month, day of week, or other periodic markers enables models to recognize recurring patterns, improving forecasts for sales, web traffic, and more.
+
+Combining these approaches can significantly enhance a time series model's predictive power, especially when paired with algorithms like ARIMA or gradient boosting.
diff --git a/_posts/2025-06-10-arima_forecasting_python.md b/_posts/2025-06-10-arima_forecasting_python.md
new file mode 100644
index 0000000..44b731b
--- /dev/null
+++ b/_posts/2025-06-10-arima_forecasting_python.md
@@ -0,0 +1,46 @@
+---
+author_profile: false
+categories:
+- Statistics
+classes: wide
+date: '2025-06-10'
+excerpt: A practical introduction to building ARIMA models in Python for reliable time series forecasting.
+header:
+  image: /assets/images/data_science_13.jpg
+  og_image: /assets/images/data_science_13.jpg
+  overlay_image: /assets/images/data_science_13.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_13.jpg
+  twitter_image: /assets/images/data_science_13.jpg
+keywords:
+- ARIMA
+- Time series forecasting
+- Python
+- Statsmodels
+seo_description: Learn how to fit ARIMA models using Python's statsmodels library, evaluate their performance, and avoid common pitfalls.
+seo_title: 'ARIMA Forecasting with Python'
+seo_type: article
+summary: This tutorial walks through the basics of ARIMA modeling, from identifying parameters to validating forecasts on real data.
+tags:
+- ARIMA
+- Forecasting
+- Python
+- Time series
+title: 'ARIMA Modeling in Python: A Quick Start Guide'
+---
+
+ARIMA models remain a cornerstone of classical time series analysis. Python's `statsmodels` package makes it straightforward to specify, fit, and evaluate these models.
+
+## 1. Identifying the ARIMA Order
+
+Plot the autocorrelation (ACF) and partial autocorrelation (PACF) to determine suitable values for the AR (p) and MA (q) terms. Differencing can help stabilize non-stationary series before fitting.
+
+## 2. Fitting the Model
+
+With parameters chosen, use `statsmodels.tsa.arima.model.ARIMA` to estimate the coefficients. Review summary statistics to ensure reasonable residual behavior.
+
+## 3. Forecast Evaluation
+
+Evaluate predictions using metrics like mean absolute error (MAE) or root mean squared error (RMSE). Cross-validation on rolling windows helps confirm that the model generalizes well.
+
+While ARIMA is a classical technique, it remains a powerful baseline and a stepping stone toward more complex forecasting methods.
diff --git a/_posts/2025-06-11-introduction_neural_networks.md b/_posts/2025-06-11-introduction_neural_networks.md
new file mode 100644
index 0000000..3ed0d24
--- /dev/null
+++ b/_posts/2025-06-11-introduction_neural_networks.md
@@ -0,0 +1,41 @@
+---
+author_profile: false
+categories:
+- Machine Learning
+classes: wide
+date: '2025-06-11'
+excerpt: Neural networks power many modern AI applications. This article introduces their basic structure and training process.
+header:
+  image: /assets/images/data_science_14.jpg
+  og_image: /assets/images/data_science_14.jpg
+  overlay_image: /assets/images/data_science_14.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_14.jpg
+  twitter_image: /assets/images/data_science_14.jpg
+keywords:
+- Neural networks
+- Deep learning
+- Backpropagation
+- Activation functions
+seo_description: Get a beginner-friendly overview of neural networks, covering layers, activation functions, and how training works via backpropagation.
+seo_title: 'Neural Networks Explained Simply'
+seo_type: article
+summary: This overview demystifies neural networks by highlighting how layered structures learn complex patterns from data.
+tags:
+- Neural networks
+- Deep learning
+- Machine learning
+title: 'A Gentle Introduction to Neural Networks'
+---
+
+At their core, neural networks consist of layers of interconnected nodes that learn to approximate complex functions. Each layer transforms its inputs through weights and activation functions, gradually building richer representations.
+
+## 1. Layers and Activations
+
+A typical network starts with an input layer, followed by one or more hidden layers, and ends with an output layer. Activation functions like ReLU, sigmoid, or tanh introduce non-linearity, enabling the network to model complicated relationships.
+
+## 2. Training via Backpropagation
+
+During training, the network makes predictions and measures how far they deviate from the true labels. The backpropagation algorithm computes gradients of the error with respect to each weight, allowing an optimizer such as gradient descent to adjust the network toward better performance.
+
+Neural networks underpin everything from image recognition to natural language processing. Understanding their basic mechanics is the first step toward exploring the broader world of deep learning.
diff --git a/_posts/2025-06-12-hyperparameter_tuning_strategies.md b/_posts/2025-06-12-hyperparameter_tuning_strategies.md
new file mode 100644
index 0000000..b313525
--- /dev/null
+++ b/_posts/2025-06-12-hyperparameter_tuning_strategies.md
@@ -0,0 +1,42 @@
+---
+author_profile: false
+categories:
+- Machine Learning
+classes: wide
+date: '2025-06-12'
+excerpt: Hyperparameter tuning can drastically improve model performance. Explore common search strategies and tools.
+header:
+  image: /assets/images/data_science_15.jpg
+  og_image: /assets/images/data_science_15.jpg
+  overlay_image: /assets/images/data_science_15.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_15.jpg
+  twitter_image: /assets/images/data_science_15.jpg
+keywords:
+- Hyperparameter tuning
+- Grid search
+- Random search
+- Bayesian optimization
+seo_description: Learn when to use grid search, random search, and Bayesian optimization to tune machine learning models effectively.
+seo_title: 'Effective Hyperparameter Tuning Methods'
+seo_type: article
+summary: This guide covers systematic approaches for searching the hyperparameter space, along with libraries that automate the process.
+tags:
+- Hyperparameters
+- Model selection
+- Optimization
+- Machine learning
+title: 'Hyperparameter Tuning Strategies'
+---
+
+Choosing the right hyperparameters can make or break a machine learning model. Because the search space is often large, systematic strategies are essential.
+
+## 1. Grid and Random Search
+
+Grid search exhaustively tests combinations of predefined parameter values. While thorough, it can be expensive. Random search offers a quicker alternative by sampling combinations at random, often finding good solutions faster.
+
+## 2. Bayesian Optimization
+
+Bayesian methods build a probabilistic model of the objective function and choose the next parameters to evaluate based on expected improvement. Libraries like Optuna and Hyperopt make this approach accessible.
+
+Automated tools can handle much of the heavy lifting, but understanding the underlying strategies helps you choose the best one for your problem and compute budget.
diff --git a/_posts/2025-06-13-model_deployment_best_practices.md b/_posts/2025-06-13-model_deployment_best_practices.md
new file mode 100644
index 0000000..08a259e
--- /dev/null
+++ b/_posts/2025-06-13-model_deployment_best_practices.md
@@ -0,0 +1,44 @@
+---
+author_profile: false
+categories:
+- Data Science
+classes: wide
+date: '2025-06-13'
+excerpt: Deploying machine learning models to production requires planning and robust infrastructure. Here are key practices to ensure success.
+header:
+  image: /assets/images/data_science_16.jpg
+  og_image: /assets/images/data_science_16.jpg
+  overlay_image: /assets/images/data_science_16.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_16.jpg
+  twitter_image: /assets/images/data_science_16.jpg
+keywords:
+- Model deployment
+- MLOps
+- Monitoring
+- Scalability
+seo_description: Understand essential steps for taking models from development to production, including containerization, monitoring, and retraining.
+seo_title: 'Best Practices for Model Deployment'
+seo_type: article
+summary: This post outlines reliable approaches for serving machine learning models in production environments and keeping them up to date.
+tags:
+- Deployment
+- MLOps
+- Production
+- Data science
+title: 'Model Deployment: Best Practices and Tips'
+---
+
+A model is only as valuable as its impact in the real world. Deployment bridges the gap between experimental results and practical applications.
+
+## 1. Containerization
+
+Packaging models in containers such as Docker ensures consistent environments across development and production. This reduces dependency issues and simplifies scaling.
+
+## 2. Monitoring and Logging
+
+Once deployed, models must be monitored for performance degradation and data drift. Logging predictions and input data enables debugging and long-term analysis.
+
+## 3. Continuous Improvement
+
+Retraining pipelines and automated rollback strategies help keep models accurate as data changes over time. MLOps tools streamline these processes, making deployments more robust.
diff --git a/_posts/2025-06-14-data_ethics_machine_learning.md b/_posts/2025-06-14-data_ethics_machine_learning.md
new file mode 100644
index 0000000..f1bd7c1
--- /dev/null
+++ b/_posts/2025-06-14-data_ethics_machine_learning.md
@@ -0,0 +1,42 @@
+---
+author_profile: false
+categories:
+- Data Science
+classes: wide
+date: '2025-06-14'
+excerpt: Ethical considerations are critical when deploying machine learning systems that affect real people.
+header:
+  image: /assets/images/data_science_17.jpg
+  og_image: /assets/images/data_science_17.jpg
+  overlay_image: /assets/images/data_science_17.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_17.jpg
+  twitter_image: /assets/images/data_science_17.jpg
+keywords:
+- Data ethics
+- Bias mitigation
+- Responsible AI
+- Transparency
+seo_description: Examine the ethical challenges of machine learning, from biased data to algorithmic transparency, and learn best practices for responsible AI.
+seo_title: 'Data Ethics in Machine Learning'
+seo_type: article
+summary: This article discusses how to address fairness, accountability, and transparency when building machine learning solutions.
+tags:
+- Ethics
+- Responsible AI
+- Bias
+- Machine learning
+title: 'Why Data Ethics Matters in Machine Learning'
+---
+
+Machine learning models influence decisions in finance, healthcare, and beyond. Ignoring their ethical implications can lead to harmful outcomes and loss of trust.
+
+## 1. Sources of Bias
+
+Bias often enters through historical data that reflects social inequities. Careful data auditing and diverse datasets help reduce unfair outcomes.
+
+## 2. Transparency and Accountability
+
+Model interpretability techniques and transparent documentation allow stakeholders to understand how predictions are made and to challenge them when necessary.
+
+By considering ethics from the outset, data scientists can create systems that not only perform well but also align with broader societal values.
diff --git a/_posts/2025-06-15-smote_pitfalls.md b/_posts/2025-06-15-smote_pitfalls.md
new file mode 100644
index 0000000..d1dd7e4
--- /dev/null
+++ b/_posts/2025-06-15-smote_pitfalls.md
@@ -0,0 +1,51 @@
+---
+author_profile: false
+categories:
+- Machine Learning
+classes: wide
+date: '2025-06-15'
+excerpt: SMOTE generates synthetic samples to rebalance datasets, but using it blindly can create unrealistic data and biased models.
+header:
+  image: /assets/images/data_science_18.jpg
+  og_image: /assets/images/data_science_18.jpg
+  overlay_image: /assets/images/data_science_18.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_18.jpg
+  twitter_image: /assets/images/data_science_18.jpg
+keywords:
+- SMOTE
+- Oversampling
+- Imbalanced data
+- Machine learning pitfalls
+seo_description: Understand the drawbacks of applying SMOTE for imbalanced datasets and why improper use may reduce model reliability.
+seo_title: 'When SMOTE Backfires: Avoiding the Risks of Synthetic Oversampling'
+seo_type: article
+summary: Synthetic Minority Over-sampling Technique (SMOTE) creates artificial examples to balance classes, but ignoring its assumptions can distort your dataset and harm model performance.
+tags:
+- SMOTE
+- Class imbalance
+- Machine learning
+title: "Why SMOTE Isn't Always the Answer"
+---
+
+Synthetic Minority Over-sampling Technique, or **SMOTE**, is a popular approach for handling imbalanced classification problems. By interpolating between existing minority-class instances, it produces new, synthetic samples that appear to boost model performance.
+
+## 1. Distorting the Data Distribution
+
+SMOTE assumes that minority points can be meaningfully combined to create realistic examples. In many real-world datasets, however, minority observations may form discrete clusters or contain noise. Interpolating across these can introduce unrealistic patterns that do not actually exist in production data.
+
+## 2. Risk of Overfitting
+
+Adding synthetic samples increases the size of the minority class but does not add truly new information. Models may overfit to these artificial points, learning overly specific boundaries that fail to generalize when faced with genuine data.
+
+## 3. High-Dimensional Challenges
+
+In high-dimensional feature spaces, distances become less meaningful. SMOTE relies on nearest neighbors to generate new points, so as dimensionality grows, the synthetic samples may fall in regions that have little relevance to the real-world problem.
+
+## 4. Consider Alternatives
+
+Before defaulting to SMOTE, evaluate simpler techniques such as collecting more minority data, adjusting class weights, or using algorithms designed for imbalanced tasks. Sometimes, strategic undersampling or cost-sensitive learning yields better results without fabricating new observations.
+
+## Conclusion
+
+SMOTE can help balance datasets, but it should be applied with caution. Blindly generating synthetic data can mislead your models and mask deeper issues with class imbalance. Always validate whether the new samples make sense for your domain and explore alternative strategies first.
diff --git a/frontmatter/__init__.py b/frontmatter/__init__.py
new file mode 100644
index 0000000..b92d25a
--- /dev/null
+++ b/frontmatter/__init__.py
@@ -0,0 +1,41 @@
+import re
+
+class Post(dict):
+    """Simple container for front matter metadata and content."""
+    def __init__(self, content='', **metadata):
+        super().__init__(metadata)
+        self.content = content
+
+def _parse_yaml(yaml_str):
+    data = {}
+    for line in yaml_str.splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        if ':' in line:
+            key, value = line.split(':', 1)
+            data[key.strip()] = value.strip().strip('"').strip("'")
+    return data
+
+def _to_yaml(data):
+    lines = [f"{k}: {v}" for k, v in data.items()]
+    return '\n'.join(lines)
+
+def dumps(post):
+    fm = _to_yaml(dict(post))
+    return f"---\n{fm}\n---\n{post.content}"
+
+def load(fp):
+    if hasattr(fp, 'read'):
+        text = fp.read()
+    else:
+        with open(fp, 'r', encoding='utf-8') as f:
+            text = f.read()
+    match = re.match(r'^---\n(.*?)\n---\n?(.*)', text, re.DOTALL)
+    if match:
+        fm_yaml, body = match.group(1), match.group(2)
+        metadata = _parse_yaml(fm_yaml)
+    else:
+        body = text
+        metadata = {}
+    return Post(content=body, **metadata)