Preparing raw or heterogeneous data for modeling through cleaning, encoding, imputation, scaling, and related input transformations.
Tagged articles
24 articles in this tag.
You trained a model. You saved it. Now a new file of rows arrives, and each one needs a prediction. This is where a saved model proves its worth—or quietly…
Read tutorial
Swap OrdinalEncoder for OneHotEncoder, rerun the script, and the score moves. That single number feels like a verdict: one encoder is better. It isn't. You…
Read tutorial
The real question is not whether a column contains text or numbers. It is whether the model should treat those values as ordered quantities or as separate…
Read tutorial
Clustering always returns groups. Even on random noise, even on data with no structure worth naming, every algorithm will happily partition your points and…
Read tutorial
One dataset, many feature types, one honest workflow. That is the problem ColumnTransformer solves.
Read tutorial
Fill the missing values, split the data, train the model, and watch your score climb. It feels like progress. It is usually a leak.
Read tutorial
You train a model. The validation score comes back at 0.98. You feel like a genius. Then the model goes into the real world and performs like a coin flip.
Read tutorial
You have a list of sentences, a folder of images, or a pile of Python dictionaries. You call .fit() on your model, and it refuses. The error message is not…
Read tutorial
Two houses sit side by side in your table. One is 1,800 square feet with 3 bedrooms. The other is 2,000 square feet with 5 bedrooms. To a k-NN model, the…
Read tutorial
You train the same model twice on the same data. The only difference: you scaled the features before the second run. The results are not slightly…
Read tutorial
You fit a model to something like house prices or income. Most values bunch on the left, and a long tail stretches to the right. The model behaves…
Read tutorial
Your model is only as smart as the table you hand it. Most beginners skip straight to fitting an algorithm, then blame the model when results disappoint.…
Read tutorial
Most beginners treat machine learning as a collection of algorithms to memorize. They collect names like trading cards—logistic regression, random forest,…
Read tutorial
Every beginner hits the same wall: you load a real dataset, and it is full of holes. Columns you need are dotted with NaN. Your model refuses to run. So…
Read tutorial
The most dangerous bug in a beginner text classifier is not in the model. It is in the order of operations. Fit your vectorizer on the whole dataset before…
Read tutorial
Your data is clean. Your rows are ready. Then you feed the model a column of colors—"red", "green", "blue"—and it refuses to train. The error message is…
Read tutorial
Every beginner hits the same fork in the road. You plot your data, spot a few points sitting far from the pack, and freeze. Delete them? Keep them? The…
Read tutorial
Most people assume PCA "removes unimportant features." It does not. It rotates your data into new directions ranked by variance, then drops the quietest…
Read tutorial
You fit PCA, print the explained variance ratio, and see that PC1 captures 92% of the variance. That looks like a strong result. It might be an artifact of…
Read tutorial
You can call PCA(), read explained_variance_ratio_, and get a working result. Then someone asks why the principal directions are eigenvectors of the…
Read tutorial
You scale the whole table, split it, train a model, and get 0.94. You ship it, and real data hands you 0.71. Nothing broke in production. The break…
Read tutorial
Here is the uncomfortable truth about missing data: in a real dataset, you never see the values that went missing. You cannot check whether your imputation…
Read tutorial
You one-hot encoded a categorical column and watched it explode into hundreds of columns. Now your dataset is mostly zeros—and your first instinct might be…
Read tutorial
Target encoding is one of the most seductive traps in feature engineering. You encode a high-cardinality feature like city or merchant_id, watch your…
Read tutorial