Tag: Python
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From Notebook to Python Package: Structuring a Data Science Project Someone Else Can Run (Data Science Series, Part 21)
A working churn notebook is not a deliverable. Here is the src layout, pyproject.toml, path handling, test suite and artifact versioning that turn 175 lines of model code into something a colleague installs and runs in one command.
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PyTorch Essentials in Python: Tensors, Autograd and a Training Loop You Can Read (Data Science Series, Part 17)
Tensors, autograd and a hand written training loop in PyTorch 2.13, rebuilt on the churn model from Part 16. Includes the three errors that cost me the most time and an honest CPU versus GPU comparison.
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Neural Network Fundamentals in Python, Built Up From Logistic Regression (Data Science Series, Part 16)
A neural network is logistic regression with a layer in the middle. I build one in thirty lines of numpy, then show the scaling mistake that dropped average precision from 0.5445 to 0.1062 on the churn model.
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Imbalanced Data and Resampling in Python Without Fooling Yourself (Data Science Series, Part 14)
Rare positive classes break accuracy, flatter ROC AUC and invite resampling mistakes that leak. Here is what actually moved the money on a churn model, with real numbers for SMOTE, class weights and threshold tuning.
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Clustering and PCA in Python: Unsupervised Learning on Real Data (Data Science Series, Part 13)
K means and principal component analysis on the same 486 row churn frame we have modelled since Part 6. Real silhouette scores, four profiled segments, and an honest test of whether cluster labels help a classifier at all.
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Logistic Regression in Python and Your First Classifier (Data Science Series, Part 10)
Build a churn classifier with logistic regression, read its coefficients as odds ratios, and learn why 73.77 percent accuracy can mean your model never predicted a single churn.
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Linear Regression in Python From the Inside Out (Data Science Series, Part 9)
Least squares is three lines of numpy once you see the geometry. This part fits, checks and defends a linear model, then shows the collinearity failure that produces nonsense coefficients without ever raising an error.
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Statistical Inference for Machine Learning: Sampling, Confidence Intervals and What a p Value Is Not (Data Science Series, Part 8)
Every metric you report is one draw from a distribution. Here is how to put a confidence interval on it, when to bootstrap, and the three readings of a p value that quietly wreck model selection.
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Feature Engineering in Python: Where Model Accuracy Actually Comes From (Data Science Series, Part 6)
Encoding, ratio features and cross fitted target encoding on the churn table, with a measured leakage demo that turns a column of random noise into an AUC of 0.800.
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Getting Data Into Python: APIs, Files, SQL Pulls and Formats That Bite (Data Science Series, Part 5)
Most model errors enter at the moment data is read. Here is how I pull from paginated APIs, run SQL into pandas safely, and pick a file format, with measured numbers from the churn project.
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NumPy and pandas Past the Basics: Vectorisation, Merges, Reshaping and Memory (Data Science Series, Part 4)
Most pandas mistakes do not raise an error. They cost you memory, hours of runtime, and rows you did not know you gained. Here is what changed my day to day handling of the churn dataset.
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Python Setup for Data Science That Will Not Break: Virtual Environments, Notebooks and Reproducibility (Data Science Series, Part 3)
A working Python setup for data science that survives a laptop change, a colleague, and a rerun six months later. Virtual environments, tool choice, notebook discipline, pinned versions and seeds, with real errors and the fixes.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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