- 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 andpython 3.10 because kivy don't Supports python3.11
- Clone the Repository.
git clone git@github.com:PraddyumnYadav/HousePricePredictor-California-1990.git
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 System.
- 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
- 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.
- I have Used Python-Tkinter 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.
- It Works as Same As Tkinter FrameWork.
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.