🏠 House Price Category Prediction using Logistic Regression

A machine learning project that classifies synthetic house prices into categories using Logistic Regression. It uses features such as area, number of bedrooms, and bathrooms to predict whether a house falls into a low, medium, or high price range.

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📁 Files

• synthetic_house_data.csv: The synthetic dataset used for training and testing.
• Houseprice_prediction.py: The main Python script for training, prediction, and visualization.
• README.md: Project documentation.

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📌 Price Category Mapping

Category	Price Range
0	Below $300,000
1	$300,000 to $449,999
2	$450,000 and above

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🔍 Project Overview

This project includes the following steps:

1. ✅ Load and inspect data
2. 🏷️ Transform raw price into categorical classes
3. ✂️ Split dataset into training and test sets
4. 🤖 Train a logistic regression model
5. 📊 Evaluate model performance (accuracy, confusion matrix, and classification report)
6. 📉 Visualize actual vs. predicted price categories

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🧠 Model

Algorithm: Logistic Regression (Multi-class)
Library: scikit-learn
Evaluation Metrics:

• Accuracy
• Precision, Recall, F1-score
• Confusion Matrix

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🖥️ How to Run

1. Clone the repository

git clone https://github.com/your-username/house-price-category-prediction.git
cd house-price-category-prediction