Machine Learning Project | Predicting House Prices from Scratch скачать в хорошем качестве

Machine Learning Project | Predicting House Prices from Scratch

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: ezeekits@gmail.com