From Raw CSV to Model Comparison in 3 Lines of Python

A hands-on tutorial with dissectml — the library that combines deep EDA with model comparison. Let me show you something. This is how most data scientists start a project: import pandas as pd from ydata_profiling import ProfileReport from sklearn.model_selection import train_test_split , cross_val_score from sklearn.ensemble import RandomForestClassifier , GradientBoostingClassifier from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler , LabelEncoder from sklearn.pipeline import Pipeline from sklearn.metrics import classification_report , confusion_matrix import matplotlib.pyplot as plt import seaborn as sns import shap # ... 150 more lines of boilerplate And this is the same thing with dissectml: import dissectml as dml report = dml . analyze ( df , target = " survived " ) report . export ( " report.html " ) Same output. Same depth. Three lines. Let me walk you through what happens under the hood. Setup pip install dissectml For this tuto