CaliforniaHousePricing

This project intends to build a ML Model to predict House Prices in California bsaed on dataset California housing dataset. Exploratory Data Analysis was performed on this dataset to identify possible outliers and to understand the relation between the given independant features. A linear regression model was trained to fit through the dataset. A webpage with an interface which demands input values of independent features to predict the possible house price is also included in the scope of this project.

Software and Tools Requirement

1. anaconda
2. python
3. scikit-learn
4. numpy
5. pandas
6. seaborn
7. jupyter
8. ipykernel
9. pickle
10. flask
11. matplotlib

Installation

create conda environment will all requirements by command:

conda env create -f environment.yml

Folder structure

CaliforniaHousePricing
│   .gitignore
│   app.py
│   environment.yml
│   LICENSE
│   README.md
│   regmodel.pkl
│   Regression_Impl.ipynb
│   scaling.pkl
├───templates
│       debug.log
│       index.html

File info

• Regression_Impl.ipynb Jupyter notebook implementing EDA and Linear Regression
• app.py Webpage backend
• templates\index.html Webpage Frontend