🏠 House Price Prediction using Linear Regression

A Machine Learning project that predicts house prices using the King County House Sales Dataset.

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📌 Project Overview

This project implements a Linear Regression model to predict house prices using the King County House Sales Dataset.

The notebook demonstrates the complete Machine Learning workflow including:

• Data Collection
• Data Cleaning
• Exploratory Data Analysis (EDA)
• Feature Selection
• Model Training
• Model Evaluation
• Price Prediction

The objective is to understand how different house features influence market prices while building a predictive model using Python and Scikit-learn.

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🚀 Features

• 📊 Data Loading & Inspection
• 🧹 Data Cleaning & Preprocessing
• 📈 Exploratory Data Analysis (EDA)
• 🔍 Feature Selection
• 🤖 Linear Regression Model
• 📉 Model Performance Evaluation
• 🏡 House Price Prediction
• 📋 Visualization of Results

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📂 Dataset

Dataset: King County House Sales Dataset

Details	Value
Records	21,613
Format	CSV
Target Variable	Price
File	kc_house_data.csv

Features Included

• Bedrooms
• Bathrooms
• Living Area
• Lot Area
• Floors
• Waterfront
• View
• Condition
• Grade
• Year Built
• Location
• Price (Target)

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🛠️ Tech Stack

Technology	Purpose
Python	Programming Language
Pandas	Data Manipulation
NumPy	Numerical Computing
Matplotlib	Data Visualization
Seaborn	Statistical Visualization
Scikit-learn	Machine Learning
Jupyter Notebook	Development Environment

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📁 Project Structure

House_Price_Prediction_Linear_Regression/
│
├── Akshat_Pandey.ipynb
├── kc_house_data.csv
├── README.md
└── images/
    ├── banner.png
    ├── dataset.png
    ├── heatmap.png
    ├── regression.png
    └── prediction.png

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🔄 Machine Learning Workflow

Load Dataset
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Data Cleaning
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Exploratory Data Analysis
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Feature Selection
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Train-Test Split
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Linear Regression Model
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Model Evaluation
      │
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House Price Prediction

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📊 Project Screenshots

📌 Dataset Preview

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📈 Correlation Heatmap

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📉 Regression Analysis

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🏡 Prediction Result

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📈 Machine Learning Algorithm

Linear Regression

Linear Regression is a supervised Machine Learning algorithm that predicts continuous numerical values by finding the best-fit linear relationship between independent variables and the target variable.

The model is trained to estimate house prices based on multiple property characteristics.

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🎯 Learning Outcomes

This project helped me understand:

• Data preprocessing
• Exploratory Data Analysis (EDA)
• Feature engineering
• Linear Regression
• Train-Test Split
• Model Evaluation
• Real-world Machine Learning workflow
• Data Visualization

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💡 Future Improvements

• Implement Ridge & Lasso Regression
• Compare Multiple ML Models
• Hyperparameter Tuning
• Build a Flask Web Application
• Deploy on Streamlit
• Add Interactive Visualizations

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👨‍💻 Author

Akshat Pandey

🎓 B.Tech (Computer Science & Engineering)

💻 Machine Learning Enthusiast

🐙 GitHub: https://github.com/BuildWith-AXAT

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⭐ If you found this project useful, consider giving it a Star!

Made with ❤️ by Akshat Pandey