1. Introduction
The primary goal of this project is to implement a Property Price Prediction System using the Linear Regression algorithm integrated into a Flask web application. The system will take property-related parameters, including area_type, availability, location, size, society, total_sqft, bath, balcony, and predict the property price.
2. Problem Statement
Property price prediction involves intricate factors, making it an ideal candidate for machine learning. This project specifically focuses on implementing a Linear Regression model to accurately estimate property prices based on key features. The Flask web application will serve as the interface for users to input property details and receive price predictions.
3. Methodology
3.1 Data Collection
The dataset used for training and testing the Linear Regression model includes parameters such as area_type, availability, location, size, society, total_sqft, bath, balcony, and the target variable, price.
3.2 Data Preprocessing
Preprocessing steps involve handling missing values, outliers, and categorical variables. Feature scaling and normalization will be applied to ensure uniformity across features.
3.3 Model Development
The project utilizes the Linear Regression algorithm for its simplicity and interpretability. The model will be trained on the processed dataset to predict property prices.
3.4 Flask Web Application
A Flask web application will be developed to allow users to input property details easily. The trained Linear Regression model will be integrated into the application to provide real-time property price predictions.
4. Implementation
4.1 Technology Stack
Machine Learning Library: Scikit-learn (for Linear Regression).
Web Framework: Flask.
Frontend: HTML, CSS, and JavaScript.
4.2 Workflow
Data Collection: Gather a diverse dataset containing relevant property information.
Data Preprocessing: Clean and preprocess the dataset to prepare it for training.
Model Training: Train the Linear Regression model on the processed dataset.
Flask Application Development: Create an intuitive interface for users to input property details.
Integration: Connect the trained Linear Regression model with the Flask application for real-time predictions.
User Interface: Design a user-friendly interface to display predicted property prices.
5. Challenges
Data Quality: Ensuring the quality and reliability of the dataset.
Model Accuracy: Fine-tuning the Linear Regression model for accurate predictions.
Scalability: Designing the system to handle simultaneous user requests.
6. Conclusion
This project focuses on developing a Property Price Prediction System using the simplicity and transparency of the Linear Regression algorithm. By integrating this model into a Flask web application, users can easily obtain property price predictions based on various input parameters. The combination of Linear Regression and Flask offers a practical solution for predicting property prices in real-time.
