🏠 House Price Prediction - A Learning Project

This repository hosts a basic machine learning project focused on predicting house prices.
The goal of this project is to provide a foundational understanding of the supervised machine learning workflow, with a particular focus on regression analysis.

It’s a great starting point for beginners to grasp key concepts from data exploration to model evaluation.

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📌 Project Overview

The main objective is to build a machine learning model that can predict the sale price of a house based on a dataset of various features.

This project showcases a practical application of machine learning by taking a real-world problem and solving it with code.

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✨ Key Features and Learning Goals

• Data Preprocessing: Handle and clean raw datasets, deal with missing values, and prepare data for modeling.
• Exploratory Data Analysis (EDA): Use data visualization to identify patterns and relationships between features and house price.
• Simple Regression Model: Train a basic regression model using scikit-learn (e.g., Linear Regression).
• Model Evaluation: Measure performance with MAE, MSE, and R².
• Code Documentation: Write clean, commented code with a well-structured Jupyter Notebook.

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🛠️ Technologies Used

• Python – Core programming language
• Pandas – Data manipulation & analysis
• NumPy – Numerical operations
• Scikit-learn – ML modeling & evaluation
• Matplotlib / Seaborn – Data visualization
• Google Colab – Interactive workflow

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🚀 How to Get Started

Clone the repository:

git clone https://github.com/shanu1981/House-Price-Prediction.git

Navigate to the project directory:

cd house-price-prediction

Install dependencies:

pip install -r requirements.txt

📊 Results

Linear Regression

Random Forest Regression