I. Analytic Approach (Data-Oriented Thinking)

1. Purpose of the Analytic Approach

Once the business problem is clearly understood, the next step is to determine how data analysis can answer the question.

The analytic approach involves:

Clarifying the type of question being asked
Selecting the most appropriate analytical method

2. Mapping the Question to an Analytic Approach

The refined analytical question is:

“Given the characteristics of a house, what price should we estimate?”

This question requires:

A numerical output
Prediction based on historical patterns

This is a Regression Problem

3. Selecting the Type of Analysis

Question Type	Analytic Approach
Predict a numeric value	Regression
Yes / No decision	Classification
Discover patterns	Clustering
Describe trends	Descriptive Analysis

For house price prediction:

Regression analysis is the most appropriate
Machine Learning may be used to capture complex relationships

4. Why Machine Learning May Be Used

Machine Learning can:

Identify non-linear relationships between features and price
Improve prediction accuracy compared to simple rules

However:

ML is a tool, not the objective
The choice of model comes after this step

II. Comparison of the Two Approaches

Problem Approach	Analytic Approach
Focuses on goals and value	Focuses on methods
Business-driven	Data-driven
Defines what success means	Defines how to measure it
No models mentioned	Model types identified

III. Analysis Design

1. Type of Data Science Problem

Business Need	Analytics Interpretation
Estimate a numerical house price	Predict a continuous numeric value

This is Supervised Learning – Regression

2. Frome Input to Output Mapping

Role	Business Meaning	Data Representation
Input (X)	Property attributes	Area, location, rooms, year built, etc.
Output (y)	House value	Sale price

Mathematically:

\[Price=f(Property,Location,Market)\]

3. What Is the Model Expected to Do?

The model must:

Learn the relationship between house characteristics and historical prices
Generalize to unseen houses
Produce a numeric price estimate

Not:

Classify houses

Rank houses

Recommend houses

4. Success Metrics (Analytics View)

Business Success Criteria: “Average prediction error below an acceptable threshold”

Primary metric: MAE

Secondary: RMSE

5. Baseline Definition

Business wants to know: “Is this better than current practice?”

Baseline:

Mean house price
Or price per m² × area

Model	Purpose
Simple heuristic	Business benchmark
ML model	Value-added comparison

6. Output Form

Field	Description
predicted_price	Estimated house value
confidence_interval (optional)	Uncertainty

7. Deployment Implication (from Analytics)

User inputs numeric & categorical features
System returns a number
UI must support structured input

8. Analytic Design Summary

This project is a supervised regression problem where historical house transaction data will be used to train a model that predicts continuous house prices from property, location, and market features. The solution will be evaluated primarily using MAE and RMSE against a baseline pricing heuristic.