AI/ML/NLP Research Track · Lesson 11 of 16
Project 3: Tabular Prediction Model
Project 3: Prediction Model from Tabular Data
Tabular ML is extremely useful in real business workflows (risk scoring, churn, pricing, demand prediction).
Workflow
- define target and leakage rules
- split train/validation/test
- build preprocessing pipeline
- train baseline model
- evaluate + tune + explain
Pipeline Example
Python
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestClassifier
pre = ColumnTransformer([
("num", Pipeline([
("imputer", SimpleImputer(strategy="median")),
("scaler", StandardScaler())
]), num_cols),
("cat", Pipeline([
("imputer", SimpleImputer(strategy="most_frequent")),
("onehot", OneHotEncoder(handle_unknown="ignore"))
]), cat_cols)
])
model = Pipeline([
("prep", pre),
("clf", RandomForestClassifier(random_state=42))
])Evaluation
- classification: F1 + ROC-AUC
- regression: MAE + RMSE
- calibration/business thresholds where needed
Deliverables
- End-to-end notebook with reproducible pipeline
- Validation metrics and interpretation
- Feature importance or SHAP-based explanation