Everyone loves to talk about “training models”… but the real magic happens before you even touch the model. Let’s walk through the pipeline every ML project goes through, from messy spreadsheets to clean, ready to train data.
Start by seeing your data. Plot distributions, scatter plots, correlations. Patterns (and problems) pop up instantly.
import seaborn as sns
sns.pairplot(df, hue="target")
Missing values, duplicates, weird outliers, they’re everywhere. Handle them early or they’ll bite you later. Think: dropna(), filling missing values, or domain driven fixes.
Raw columns aren’t always enough. You can create new signals (e.g., combining “height” & “weight” → BMI) or transform categorical variables into numeric with one-hot encoding.
Most models hate unscaled data. Imagine one feature in kilometers and another in millimeters chaos. Normalize or standardize to bring fairness.
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
Don’t cheat yourself. Always set aside unseen data to test how well your model really generalizes.
rom sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Only after these steps do you even think about model training.
Skip them, and you’ll end up with only a great algorithm that fails miserably in the real world.
Check my Machine Learning full list for deeper understanding of the process before model building.
