shaiknawazshareef/random-forest-classification-using-machine-learning

This is a small, script-driven car-acceptability classification project. The user runs the script, which loads and inspects the CSV, splits the data, encodes categorical inputs, and trains and evaluates a random forest. It then visualizes feature importance and repeats the experiment without the doors feature, reporting a confusion matrix and classification report. These responsibilities are implemented in one Python file, so the graph separates workflow stages rather than implying a larger service architecture.