Assessfy Foundation Projects Lab Beginner 5 milestones 100 marks

House Price Predictor (Your First Regression Model)

Discipline: AI & Data Level: Foundation Industry: AI & Data Function: Engineering Team: up to 3 Assessment: 5 milestones (100 marks)

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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Core skills

About this project

What you'll build: A model that estimates a house price from features like area, bedrooms and location, using a public housing dataset.

Why it's a good starter: Regression is the other half of beginner ML (alongside classification), and price prediction is intuitive and easy to sanity-check.

What you'll learn: Exploring numeric data, handling a few missing values, training a linear regression model and measuring error with RMSE.

Milestones

1. Setup & Plan

10 marks 5d

Load a housing dataset and explore it: submit summary statistics and 2 charts (e.g. price vs area).

2. Prepare the features

30 marks 10d

Handle missing values, pick 4-6 useful columns, and encode any simple categorical field. Submit the cleaned feature table.

3. Train & measure the model

30 marks 10d

Train a Linear Regression model, predict on a test split, and report RMSE and R². Done when you can explain what the error means in rupees.

4. Testing & Polish

15 marks 6d

Try one improvement (add a feature or try a different model) and compare the RMSE before/after.

5. Demo & Short Report

15 marks 5d

Demo a prediction for a made-up house and submit a 1-page report with your error metric and what most affects price.

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Skills you'll learn

AI & DataPythonRegressionData cleaningFeature basicsscikit-learn

Tools used

Pythonpandasscikit-learnmatplotliba public housing CSV

Prerequisites

Basic PythonSchool maths

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House Price Predictor (Your First Regression Model)

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