Dear Team, dear visitors,

In this project, our goal is simple: crush the competition on what appears to be a dull classification task — and turn it into a showcase of surgical precision, wicked feature engineering, and ruthless cross-validation.

We're tackling the classic Forest Cover Type Prediction challenge, where the objective is to classify 30x30m land cells in the Roosevelt National Forest (Colorado, USA) into one of seven tree cover types:

Spruce/Fir Lodgepole Pine Ponderosa Pine Cottonwood/Willow Aspen Douglas-fir Krummholz

No remote sensing here — only raw cartographic data: elevation, slope, aspect, distances to hydrology, roads, and fire points, as well as categorical indicators for 40 soil types and 4 wilderness areas.

At first glance, this may look like a random forest problem — and it is. But not the way you think. What separates top competitors is not just modeling, but feature engineering that decodes the subtle ecological patterns hidden in the raw coordinates. For example:

• Combining elevation and hillshade to simulate sunlight exposure.
• Computing true euclidean distance to hydrology, to model vegetation access to water.
• Wrapping aspect into sine and cosine features to handle its circularity.
• Decoding ELU codes from the soil types to extract underlying climatic and geological zones.
• Capturing interactions between terrain and orientation to simulate microclimate effects.

And of course, no Kaggle-worthy solution comes without LightGBM, CatBoost, and carefully stratified cross-validation — repeated, stacked, and blended until our confusion matrix begs for mercy.

This is not a toy problem. It's not even about trees. It's about taking a boring, “random-like” dataset and bending it to our will with pure modeling finesse.

We’re not predicting forests.

We’re predicting victory.