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House Price Prediction Using Machine Learning (XGBoost)

House Price Prediction Using Machine Learning (XGBoost)

The House Price Prediction System is a practical machine learning project that predicts property values using housing characteristics from the California Housing Dataset. It uses exploratory data analysis, correlation analysis, train-test splitting, and XGBoost Regression to generate accurate house value predictions. Built entirely in Python using Pandas, Scikit-learn, and XGBoost, the project demonstrates a complete end-to-end machine learning workflow and provides beginners with hands-on experience in regression modeling.

6 Modules

59 Lessons

English

1 Hr

Reading Plan

MODULE 1

Introduction

7 mins

Introduction of the Project

1 min

What Is Data Science? Key Concepts and Core Features

1 min

What Is Machine Learning?

1 min

Understanding Regression Problems

1 min

Understanding the House Price Prediction Problem

1 min

Understanding the California Housing Dataset

1 min

Aim of the Project

1 min

MODULE 2

Environment Setup and Exploratory Data Analysis

13 mins

Environment Setup and Exploratory Data Analysis

1 min

Why Use Google Colab?

1 min

Importing Required Libraries

1 min

Loading the California Housing Dataset

1 min

Creating a Pandas DataFrame

1 min

Inspecting the Dataset

1 min

Understanding Dataset Dimensions

1 min

Checking for Missing Values

1 min

Statistical Summary of the Dataset

1 min

Introduction to Exploratory Data Analysis

1 min

Understanding Correlation

1 min

Creating a Correlation Matrix

1 min

Visualizing Correlations Using a Heatmap

1 min

MODULE 3

Preparing Data for Machine Learning

13 mins

Preparing Data for Machine Learning

1 min

Why Data Preparation Matters

1 min

Understanding Features and Labels

1 min

Separating Features and Target Variables

1 min

Understanding Train-Test Split

1 min

Why We Need Unseen Data

1 min

Splitting the Dataset

1 min

Creating Training and Testing Sets

1 min

Understanding the Resulting Datasets

1 min

Inspecting Training Data

1 min

Inspecting Testing Data

1 min

Understanding Data Leakage

1 min

Preparing for Model Training

1 min

MODULE 4

Building the House Price Prediction Model Using XGBoost

12 mins

Building the House Price Prediction Model Using XGBoost

1 min

What Is XGBoost?

1 min

Understanding Ensemble Learning

1 min

Understanding Decision Trees

1 min

Understanding Gradient Boosting

1 min

Importing the XGBoost Regressor

1 min

Creating the Model

1 min

Training the Model

1 min

How the Model Learns

1 min

Understanding Model Parameters

1 min

Why XGBoost Is Effective for House Price Prediction

1 min

Training Completion

1 min

MODULE 5

Model Evaluation and Prediction

10 mins

Model Evaluation and Prediction

1 min

Making Predictions Using the Trained Model

1 min

Understanding Regression Evaluation Metrics

1 min

Understanding R square Score

1 min

Understanding Mean Absolute Error (MAE)

1 min

Why Training Performance Alone Is Not Enough

1 min

Evaluating the Model on Test Data

1 min

Calculating Test R square Score

1 min

Understanding Generalization

1 min

Visualizing Actual vs Predicted Prices

1 min

MODULE 6

Project Conclusion

4 mins

Project Conclusion

1 min

Live Link and Output Overview

1 min

MCQs

1 min

Resources

1 min

Contributors

Vishalini A

House Price Prediction Using Machine Learning (XGBoost)

Learn how to build a House Price Prediction System using Machine Learning and the California Housing Dataset. This beginner-friendly handbook walks you through exploratory data analysis, feature preparation, XGBoost model training, evaluation using R² Score and MAE, and prediction visualization.

House Price Prediction Using Machine Learning (XGBoost)

House Price Prediction Using Machine Learning for beginners

This handbook provides hands-on experience in machine learning by building a complete House Price Prediction System from scratch. It covers data loading, exploratory data analysis, correlation analysis, train-test splitting, XGBoost Regression, model evaluation, and prediction visualization in a clear and beginner-friendly manner.

Prerequisites

This course is suitable for:

Basic knowledge of Python programming
Basic understanding of Machine Learning concepts
A Google account to access Google Colab
Internet connection to access datasets and required libraries
Familiarity with Pandas and NumPy is helpful but not mandatory

Run & Test your Code with our very own IDE that supports 16 languages

Open IDE

6 Modules

59 Lessons

English

1 Hr

Contributors

Vishalini A

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