PDF 2025 (#3)

Generate pdf

Author: s2t2

Makefile

@@ -1,26 +1,39 @@
 
 
-
-
-build:
-	quarto render docs/
-	open docs/_build/index.html
-
 preview:
 	quarto preview docs/
 
 render:
 	quarto render docs/
 
+pdf:
+	quarto render docs/ --to pdf
+
+docx:
+	quarto render docs/ --to docx
+
 open:
 	open docs/_build/index.html
 
 open-pdf:
 	open docs/_build/Predictive-Modeling-in-Python.pdf
 
+clean:
+	rm -rf docs/_build
+	rm -rf docs/.quarto
 
+build:
+	$(MAKE) render
+	$(MAKE) open
 
 render-fresh:
-	rm -rf docs/_build
-	rm -rf docs/.quarto
-	quarto render docs/
+	$(MAKE) clean
+	$(MAKE) render
+
+render-fresh-pdf:
+	$(MAKE) clean
+	$(MAKE) pdf
+
+render-fresh-docx:
+	$(MAKE) clean
+	$(MAKE) docx

docs/_quarto.yml

@@ -93,7 +93,7 @@ book:
           text: "Regression Overview"
 
         - href: notes/regression/linear.qmd
-          text: "Linear Regression w/ `sklearn`"
+          text: "Linear Regression"
 
         - href: notes/regression/ols.qmd
           text: "Linear Regression w/ `statsmodels`"
@@ -269,3 +269,21 @@ format:
   #  toc: true # include a table of contents
   #  toc-depth: 2 # control depth of table of contents
   #  embed-fonts: true # embed fonts in the DOCX
+
+
+  pdf:
+
+    # use XeLaTeX or LuaLaTeX so fontspec works:
+    latex-engine: xelatex
+
+    mainfont: "Source Sans 3" # "Merriweather"
+    sansfont: "Source Sans 3" # "Helvetica Neue"
+    monofont: "Menlo"
+    #fontsize: 11pt
+    #linestretch: 1.2
+
+    includes:
+      in-header: _pdf_styles.tex
+
+  #PrettyPDF-pdf:
+  #  keep-tex: true

docs/notes/autoregressive-models/arima.qmd

@@ -1,3 +1,9 @@
+---
+jupyter: python3
+execute:
+  cache: true # re-render only when source changes
+---
+
 # Auto-Regressive Models
 
 ```{python}

docs/notes/autoregressive-models/autocorrelation.qmd

@@ -1,3 +1,9 @@
+---
+jupyter: python3
+execute:
+  cache: true # re-render only when source changes
+---
+
 # Autocorrelation
 
 **Autocorrelation** is a statistical concept that measures the relationship between a variable's current value and its past values over successive time intervals.

docs/notes/classification/index.qmd

@@ -1,3 +1,9 @@
+---
+jupyter: python3
+execute:
+  cache: true # re-render only when source changes
+---
+
 # Classification
 
 **Classification** is a supervised learning task where the variable we are trying to predict is discrete, whether that is binary or categorical.

docs/notes/ml-foundations/data-encoding.qmd

@@ -1,3 +1,9 @@
+---
+jupyter: python3
+execute:
+  cache: true # re-render only when source changes
+---
+
 # Data Encoding
 
 

docs/notes/time-series-forecasting/index.qmd

@@ -1,3 +1,9 @@
+---
+jupyter: python3
+execute:
+  cache: true # re-render only when source changes
+---
+
 # Regression for Time Series Forecasting (with `sklearn`)
 
 Let's explore an example of how to use regression to perform trend analysis with time series data.

docs/notes/time-series-forecasting/seasonality.qmd

@@ -1,3 +1,9 @@
+---
+jupyter: python3
+execute:
+  cache: true # re-render only when source changes
+---
+
 # Regression for Seasonality Analysis
 
 We've explored using a regression for time series forecasting, but what if there are seasonal or cyclical patterns in the data?