Towards Data Science
How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification | Towards Data Science
An end-to-end classical NLP experiment on Kaggle’s Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context.
🚀 Looking for a portfolio-ready NLP project?
I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.
You’ll see how far classical NLP can go with:
📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking
It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.
You’ll see how far classical NLP can go with:
📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking
It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
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💡 Replacing if-else with Match-Case
Starting with Python 3.10, we have a powerful tool: Structural Pattern Matching (match-case). This is not just an analog of switch-case from other languages; it's much more flexible. 🚀
Imagine you're writing a command handler for a bot. 🤖
❌ How NOT to do it:
⚡ How to do it properly:
The code looks like a clear table, and your eye doesn't get caught up in a bunch of
You can pass data structures in the
It's easy to combine cases. 🧩
#Python #Programming #MatchCase #CodingTips #Python310 #Developer
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Starting with Python 3.10, we have a powerful tool: Structural Pattern Matching (match-case). This is not just an analog of switch-case from other languages; it's much more flexible. 🚀
Imagine you're writing a command handler for a bot. 🤖
❌ How NOT to do it:
def handle_command(command):
if command == "start":
return "Hello! I'm a bot."
elif command == "help":
return "Here's a list of available commands..."
elif command == "stop":
return "Goodbye!"
else:
return "Unknown command."
⚡ How to do it properly:
def handle_command(command):
match command:
case "start":
return "Hello! I'm a bot."
case "help":
return "Here's a list of available commands..."
case "stop":
return "Goodbye!"
case _: # The underscore symbol catches everything else (default)
return "Unknown command."
The code looks like a clear table, and your eye doesn't get caught up in a bunch of
elif statements. 🧐You can pass data structures in the
case statements and check their structure and content on the fly. 🔍It's easy to combine cases. 🧩
#Python #Programming #MatchCase #CodingTips #Python310 #Developer
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Python has a built-in topological dependency sorter!🚀
If you're working with tasks that have dependencies — for example, in build systems, CI/CD pipelines, or workflow orchestration — the order of execution often has to be determined manually.
Usually through graphs, DFS,, or custom execution order logic.
But Python's standard library already has graphlib.TopologicalSorter.
After preparation, the sorter returns the correct execution order.
Result:
Especially useful for workflow management systems, dependency resolution, orchestration systems, and any tasks with a dependency graph.
🔥 TopologicalSorter allows you to solve dependency problems using Python's built-in tools without having to implement graph algorithms manually.
#Python #DependencyResolution #WorkflowOrchestration #CICD #BuildSystems #TopologicalSort
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If you're working with tasks that have dependencies — for example, in build systems, CI/CD pipelines, or workflow orchestration — the order of execution often has to be determined manually.
Usually through graphs, DFS,, or custom execution order logic.
But Python's standard library already has graphlib.TopologicalSorter.
ts = TopologicalSorter()
ts.add("deploy", "test")
ts.add("test", "build")
After preparation, the sorter returns the correct execution order.
tuple(ts.static_order())
Result:
("build", "test", "deploy")Especially useful for workflow management systems, dependency resolution, orchestration systems, and any tasks with a dependency graph.
#Python #DependencyResolution #WorkflowOrchestration #CICD #BuildSystems #TopologicalSort
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✨ Unpacking the remaining elements 🧩
Sometimes you need to extract the first and last elements from a list, while grouping everything in the middle separately. Instead of struggling with slicing ([1:-1]), use the asterisk (*). ⭐️
#Python #Coding #DataScience #DevLife #Programming #Tech
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Sometimes you need to extract the first and last elements from a list, while grouping everything in the middle separately. Instead of struggling with slicing ([1:-1]), use the asterisk (*). ⭐️
data = ["CEO", "Middle Python Dev", "Junior Dev", "QA", "HR"]
# The asterisk automatically collects everything "extra" into a separate list.
boss, *team, hr = data
print(boss) # CEO
print(team) # ['Middle Python Dev', 'Junior Dev', 'QA']
print(hr) # HR
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Cheat sheet on Python Frameworks:
Django: A full-featured web framework with built-in ORM, admin panel, and security features.
Flask: A lightweight microframework with a minimal set of features and high flexibility.
ORM & Admin: Built-in to Django, but need to be connected separately in Flask.
Security: Django has built-in security mechanisms, while in Flask, they need to be configured manually.
Testing: Django offers built-in testing tools, while Flask relies on third-party libraries.
Use Cases: Django is suitable for large and complex projects, while Flask is better for small applications, APIs, and prototypes.
#Python #WebDev #Django #Flask #Backend #Programming
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Django: A full-featured web framework with built-in ORM, admin panel, and security features.
Flask: A lightweight microframework with a minimal set of features and high flexibility.
ORM & Admin: Built-in to Django, but need to be connected separately in Flask.
Security: Django has built-in security mechanisms, while in Flask, they need to be configured manually.
Testing: Django offers built-in testing tools, while Flask relies on third-party libraries.
Use Cases: Django is suitable for large and complex projects, while Flask is better for small applications, APIs, and prototypes.
#Python #WebDev #Django #Flask #Backend #Programming
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