HousePricePredictor-California-1990

• A Machine Learning Model for Predicting Price of House from California Census 1990
• This Machine Learning Model is Derived From Hands on ML Book by Aurelien Geron.
• Your Python Version Should Be Between python3.7 or python 3.10 because kivy don't Supports python3.11

Description about Model

• This is a Machine Learning Model that can Predict Prices of House Which are at California.
• The Price Predicted By This Model is according to Census of 1990.
• I Have Used Sciket-learn in This Project

Description about Webiste

• I have Used Python-Django FrameWork in this Project.
• I have used Tailwind CSS as a Frontend FrameWork
• Here i Converted My Model in .pkl file With The help of joblib module in python.
• I Have Created a model.py file in which i have the pipeline and a function name request_prediction().
• if i Give The data into request_prediction() function it first executes the pipeline in which my data is transformed in many ways and then it is feeded to the machine learning model and output is returned.
• Then its Returned Information is Presented as Prediction in our next Page Using Django.

Description about GUI's

• I have Used Python-Tkinter and Python-Kivy FrameWork in this Project.
• Here i used the same .pkl file in this Project.
• I have also used The same model.py file.
• I Have Taken all inputs in form of string then converted it in float then feed it to Machine Learning Model.

How to Get This Running on Your System

• Clone the Repository Using SSH

git clone [email protected]:PraddyumnYadav/HousePricePredictor-California-1990.git

or HTTPS

git clone https://github.com/PraddyumnYadav/HousePricePredictor-California-1990.git

• cd into HousePricePredictor-California-1990.

cd HousePricePredictor-California-1990

• Intall virtualenv Module.

pip install virtualenv

• Create a Virutal Enviornment.

virutualenv venv

• Activate the Virutal Enviornment.

source ./venv/bin/activate

• Install All the Dependencies.

pip install -r requirements.txt

• Now You are Ready to run this on Your Local Machine.

Conclusion

If You Notice any Bugs in our Code or You Think That you Can Make This Code a Little Bit Better Than Don't Hesistate You Can Do Two Things in That Case

• First Thing is That You Can Raise an Issue.
• Second Thing is That You Can Open a Pull Request I Will be Very Happy to See Your Code and if it is Good Than i Will Definetly Add it to My Main Repository.

Thank You