Welcome to the HomeFindr project repository! HomeFindr is your ultimate guide in the journey of finding your dream home in the bustling metro cities of India. This README serves as your gateway to understanding the project, its modules, features, and how to seamlessly navigate through it.
HomeFindr is your trusted companion on the journey of finding the perfect home. With a focus on empowering users with data-driven insights, HomeFindr revolutionizes the home search process. Initially tailored for the vibrant city of Gurgaon, HomeFindr envisions expanding its reach to other metro cities in India, ensuring everyone finds their ideal abode.
HomeFindr's Analytics Module serves as the cornerstone of the platform, offering users invaluable insights to facilitate informed decision-making. Here's a breakdown of its key components:
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Spatial Analysis: Dive deep into the distribution of property prices across different sectors of Gurgaon, enabling users to grasp price trends and make informed comparisons.
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Price vs Square Foot Analysis: Visualize the relationship between property prices and their corresponding square footage, empowering users to evaluate the value proposition of various properties.
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Number of Rooms Pie Chart: Gain a comprehensive understanding of property distribution based on the number of rooms they offer, allowing users to refine their search based on their space requirements.
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Top Feature Word Cloud: Engage with an interactive visual representation of the most prevalent property features, offering a quick glimpse into available amenities and characteristics.
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Price Prediction Module: Leverage advanced predictive analytics to estimate property prices based on historical data and market trends, assisting users in anticipating potential price fluctuations.
At the heart of HomeFindr's innovation lies its Recommender System Module. By harnessing the power of user preferences, historical data, and property features, this module generates personalized recommendations aligned with users' housing aspirations. Say goodbye to endless scrolling and hello to tailored recommendations that match your needs seamlessly.
Transforming raw data into actionable intelligence, the Insights Module equips users with comprehensive reports, trends, and visualizations. Whether you're seeking investment insights or market forecasts, this module empowers users to make well-informed decisions, backed by data-driven analysis.
Embark on your home search journey with HomeFindr by following these simple steps:
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Clone the Repository:
git clone https://github.com/Dishantkharkar/HomeFindr_project.git cd HomeFindr_project -
Set Up Virtual Environment (Optional but Recommended):
python -m venv venv source venv/bin/activate -
Install Dependencies:
pip install -r requirements.txt -
Run the Application:
streamlit run Home.py -
Access the Web Application: Open your preferred web browser and navigate to HomeFindr Website
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Explore Analytics: Visit the HomeFindr website to delve into various analytics and insights related to property prices, features, and trends.
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Get Personalized Recommendations: Input your preferences to receive personalized property recommendations through the innovative Recommender System.
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Gain Market Insights: Utilize the Insights Module to gain a deeper understanding of the real estate market, empowering you to make well-informed decisions.
We welcome contributions from the community! Whether you have ideas, suggestions, or improvements, feel free to submit a pull request. Please refer to our Contribution Guidelines for more details.
This project is licensed under the MIT License.