Banglore House Prediction Using Flask Server (Python)

🌐 Links 🌐

📂 Repo

In this repository, I've implemented a Machine Learning-based Bangalore House Price Prediction model. With the aid of a few characteristics like availability, size, total square feet, bath, location, and so on, this model forecasts the price of a property in Bangalore.

Table of Content

1. Manifest
2. Prerequisites
3. Things that I have Done on these DataSet

🧑🏻‍🏫 Manifest

- Client --> This a client folder which contains the Front-End part of the project
     app.css --> Cascade File
     app.html --> HTML File
     app.js --> Java Script File
- Model --> 
    Banglore_housing.ipynb --> Ipynb file where I do all the Machine Learning Stuffs and dump it to a pickle file
    Bengaluru_House_Data.csv --> CSV File 
- Server -->
    Artifacts -->
        banglore_house_price_prediction.pickle - Pickel file extracted from the IPYNB File
        coulmns.json --> File that contains information of Columns 
    Server.py --> Server File
    util.py --> Util File
- README.md ---> This markdown file you are reading.

🤔 Prerequisites

• Python Installed

• Python Basics Understanding

• Understanding of Machine Learning libraries Such as Scikit Learn, Pandas, Numpy and Matplotlib

Things that I have Done on these DataSet

1. Exploratory data analysis
2. Dealing with a missing values or noisy data
3. Data preprocessing
4. Create new features from existing features
5. Remove outliers
6. Data visualisation
7. Splitting data into the training and testing
8. Train linear regression model and test.