HomeValuePro

• 

  HomeValuePro

Inspiration

We wondered how real estate prices seem to fluctuate like crazy? Well, we got intrigued and decided to dive into it. So, we started tinkering around with data and algorithms to build this funky model that predicts house prices . Plus, we figured it would be a cool way to learn more about machine learning.

What it does

Alright, imagine you're telling our model about your dream house. You mention things like the street it's on, if there's a nearby alley, how the lot looks (is it big or small?), and if the land is flat or sloped. Plus, we can't forget about the neighborhood vibe. You give my model all that info, and it crunches the numbers to give you a rough idea of what your dream home might cost. It's like having a real estate guru in your pocket.

How we built it

We have built this model using data analysis methods like advanced linear regression analysis of a house prices dataset of a city we live nearby of and have extensively used Jupyter notebooks for their collaborative functionality.

Challenges we ran into

Handling outliers in the dataset was essential. Outliers could have significantly affected model performance, so we had to decide whether to remove them or apply robust regression techniques. Dealing with missing or incomplete data required careful consideration. We had to use imputation techniques or strategies for handling missing values to ensure the integrity of the dataset. Identifying relevant features and avoiding multi-collinearity was also challenging and required domain knowledge.

What we learned

This project provided us with hands-on experience in building and refining prediction models, reinforcing the importance of data quality, model selection, and domain expertise in the process.

What's next for HomeValuePro

After deploying the model, continuous monitoring and updates are essential for maintaining accuracy. Future work includes incorporating real-time data for predictions and exploring advanced machine learning techniques for improved performance. This phase also involves collaborating with domain experts to factor in additional features and refining the model's predictions for varied scenarios and potential trends.

Built With

• jupyter
• kaggle
• kaggleapi
• python