Machine Learning Project | Predicting House Prices from Scratch

Описание: Welcome to Chapter 18 of the Machine Learning tutorial series. This chapter is a complete end-to-end beginner project where we build a real Machine Learning model to predict house prices using a real-world dataset. This lesson is designed to connect everything you have learned so far into one practical, meaningful project.

Instead of learning isolated concepts, you will now see how Machine Learning works as a full workflow — exactly how it is done in real industry projects.

What this chapter is about:

This project focuses on predicting house prices based on features such as location, size, number of rooms, and other property-related attributes. House price prediction is one of the most popular and realistic machine learning problems, making it perfect for beginners.

What you will learn step by step:

Understanding the Problem Statement
We begin by clearly defining the business problem: predicting house prices based on available data. You will learn how to think like a data scientist and understand what the model is expected to solve.

Exploring the Dataset
You will learn how to:
Load a real dataset
Understand each column and its meaning
Identify numerical and categorical features
Detect missing values and inconsistencies

This step teaches you how to “read” data before writing any model code.

Data Cleaning and Preprocessing
We apply all preprocessing concepts learned earlier:
Handling missing values
Encoding categorical variables
Feature scaling and normalization
Preparing clean input features

You will understand why preprocessing is one of the most important steps in Machine Learning.

Feature Selection and Engineering
We discuss:
Which features matter most for house price prediction
How irrelevant features can reduce model performance
How to improve predictions using better feature representation

Splitting Data into Training and Testing Sets
You will learn:
Why we split data
How training and testing data differ
How to avoid data leakage
Using proper evaluation strategies

Building the Machine Learning Model
We implement a regression model to predict house prices.
You will understand:
Why regression is used for price prediction
How the model learns patterns from data
How predictions are generated

Model Training
We train the model step by step and explain:
What happens during training
How errors are minimized
How model parameters are learned

Evaluating the Model
You will evaluate the model using:
Mean Absolute Error
Mean Squared Error
R-squared score

Each metric is explained in simple terms with real-life meaning.

Improving Model Performance
We discuss:
Underfitting vs overfitting
How preprocessing affects accuracy
How better features improve predictions

Saving the Trained Model
You will apply knowledge from Chapter 17:
Saving the trained house price model
Reloading the model without retraining
Making predictions using the saved model

Real-World Applications
We explain how this exact project concept is used in:
Real estate platforms
Property valuation systems
Financial forecasting tools
Smart city planning

By the end of this chapter, you will be able to:
Build a complete ML project from scratch
Understand the full ML workflow
Confidently work with real datasets
Train, evaluate, and save ML models
Apply your knowledge to real-world problems

This chapter officially transitions you from “learning concepts” to “building real machine learning projects”.

Useful Links:
GitHub: https://github.com/Ezee-Kits/
YouTube:    / @ezee_kits  
Email: [email protected]