ahmedembeddedx/housing-data-predictive-model

Welcome to the House Pricing Predictor project! This predictive model is designed to estimate house prices based on various features. The project is developed by Zaeem Ul Islam and Ahmed Abdullah. You can find the project repository here.

House Pricing Predictor

Project Overview

Welcome to the House Pricing Predictor project! This predictive model is designed to estimate house prices based on various features. The project is developed by Zaeem Ul Islam and Ahmed Abdullah. You can find the project repository here.

Dataset

The dataset for this project is available here. It contains essential information about houses, including square footage, number of bedrooms, location, and more. Cleaning and preprocessing of the data have been performed to ensure its suitability for training and evaluating the machine learning model.

Project Workflow

1. Data Collection: The dataset was sourced from Zameen. Cleaning and preprocessing were performed to enhance data quality.

2. Data Exploration: An in-depth analysis of the dataset was conducted to gain insights into data distribution, correlations, and potential patterns.

3. Feature Engineering: Relevant features were selected and engineered to enhance the model's predictive capabilities.

4. Model Building: Machine learning algorithms were implemented to build an accurate house pricing prediction model.

5. Model Evaluation: The model's performance was assessed using various evaluation metrics and cross-validation techniques.

6. Deployment: The model has been deployed as a user-friendly web application, allowing end-users to predict house prices conveniently.

Getting Started

To run or develop this project locally, follow these steps:

1. Clone this repository to your local machine:

   git clone https://github.com/ahmedembeddedx/Housing_Data_Predictive_Model.git

2. Install the required dependencies:

   pip install -r requirements.txt

3. Access the application in your web browser at House Price Predictor.

Usage

1. Input the relevant house information, such as square footage, number of bedrooms, location, etc.

2. Click the "Predict" button to obtain the estimated house price.

Technologies Used

• Python
• Pandas
• Scikit-Learn
• Flask
• HTML/CSS
• JavaScript

Author

• Ahmed Abdullah
• Zaeem Ul Islam

Contact

If you have any questions, suggestions, or issues related to this project, please feel free to contact the author:

• Ahmed Abdullah

We hope this House Pricing Predictor is valuable for your housing-related decisions!