House Price Prediction
Part of the AI Engineer Roadmap
In short
Every property site shows an 'estimated price'. In this project you build your own, and along the way you touch every step of a real regression workflow.
A classic regression project: predict continuous house prices from tabular features — a rite of passage that exercises EDA, feature engineering and regression together.
Think of it like this
The local property broker
A good broker hears '2BHK, third floor, near the metro' and quotes a price in seconds. He's not magic. He has seen hundreds of deals and learnt which details push the price up. Your model learns the same thing from past sales.
Watch it happen
Your build plan
1/6Download the Kaggle House Prices or California Housing data. Note what each column means.
The big ideas
What you need to know
It's a regression task
You predict a number, not a category. Errors are measured in money, so RMSE and MAE are your scores.
Log the target
A few mansions stretch the price scale. Modelling log(price) stops them from dominating the errors.
Location needs encoding
Neighbourhood names are text. One-hot encoding turns each one into its own 0/1 column the model can use.
Always beat a baseline
Start with something simple. A complex model that barely beats linear regression isn't worth the extra effort.
See it in code
Walk through the code
1import numpy as np2import pandas as pd3from sklearn.compose import ColumnTransformer4from sklearn.ensemble import RandomForestRegressor5from sklearn.model_selection import cross_val_score6from sklearn.pipeline import make_pipeline7from sklearn.preprocessing import OneHotEncoder8 9df = pd.read_csv("train.csv") # Kaggle House Prices data10X = df[["GrLivArea", "OverallQual", "YearBuilt", "Neighborhood"]]11y = np.log1p(df["SalePrice"]) # prices are skewed, so model the log12 13prep = ColumnTransformer([("cat", OneHotEncoder(handle_unknown="ignore"), ["Neighborhood"])], remainder="passthrough")14model = make_pipeline(prep, RandomForestRegressor(random_state=0))15rmse = -cross_val_score(model, X, y, cv=5, scoring="neg_root_mean_squared_error")16print("RMSE on log price:", rmse.mean())1/3Pick a few strong features to start. log1p shrinks the long tail of expensive houses.
The tricky part is mixing text and number columns. ColumnTransformer encodes the text column and passes the numbers straight through.
Save yourself the pain
Common mistakes
Oops
Throwing all 80 columns in on day one.
Do this instead
Start with five or six strong features, get a working score, then add more and check if it actually improves.
Oops
Reporting RMSE on log price as if it were rupees.
Do this instead
Convert back with np.expm1 before quoting errors in money, or clearly say the score is on the log scale.
Your mission
Milestone 1: a baseline that works
Quick check
Why do many house price models predict log(price) instead of price?
Go deeper
- 1Use a real dataset (e.g. Ames Housing) and go beyond a single default model.
- 2Compare linear regression, random forest and XGBoost on the same data.
- 3Report performance with proper metrics (RMSE, MAE) and explain your feature choices.
More projects at this stop
Customer Churn Classifier
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