Tag: Data Science Series
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NLP in Python, From Bag of Words to Transformers (Data Science Series, Part 18)
Four ways to turn text into features, measured on the same split: counts, TF-IDF, frozen embeddings and a fine tuned transformer. With the latency and cost each one buys you, and where text features belong in a churn model.
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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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Hyperparameter Tuning in Python: Halving Search, Optuna and Honest Model Selection (Data Science Series, Part 15)
A random search, a halving search and an Optuna study run against the same churn model, with wall times, the honest test score, and the nested cross validation number that showed how much of the gain was imaginary.
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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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Decision Trees, Random Forests and Gradient Boosting in Python (Data Science Series, Part 12)
A single decision tree scored a perfect 1.0000 on its training data and 0.6222 on held out folds. Here is how bagging and boosting repair that, and why on our churn data the ensembles barely beat plain logistic regression.
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Model Evaluation in Python: Metrics, Cross Validation and Data Leakage (Data Science Series, Part 11)
A single train test split moved our churn AUC by nine points depending on the seed. Here is how to pick a metric that matches the decision, read a cross validation spread honestly, and catch the leakage that manufactures scores you should not believe.
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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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Probability and Distributions a Modeller Actually Needs (Data Science Series, Part 7)
Probability is what separates a model that ranks customers from a model you can attach money to. Here is the working subset a modeller needs, with a churn worked example showing why a well ranked model can still lose cash.
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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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