https://github.com/gaurav-van/house_price_predictor_streamlit_web_app

Data Science Project to Predict House Prices in Bangalore using the concept of Regression. This Repository is used for Deployment of the Project
https://github.com/gaurav-van/house_price_predictor_streamlit_web_app

data-analysis data-science exploratory-data-analysis machine-learning prediction python regression streamlit

Last synced: 9 days ago
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Data Science Project to Predict House Prices in Bangalore using the concept of Regression. This Repository is used for Deployment of the Project

• Host: GitHub
• URL: https://github.com/gaurav-van/house_price_predictor_streamlit_web_app
• Owner: Gaurav-Van
• Created: 2022-04-15T01:24:23.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2024-05-08T04:50:28.000Z (9 months ago)
• Last Synced: 2024-12-08T02:12:54.093Z (2 months ago)
• Topics: data-analysis, data-science, exploratory-data-analysis, machine-learning, prediction, python, regression, streamlit
• Language: Python
• Homepage:
• Size: 372 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# House_Price_Predictor_Streamlit_web_app



Note: This Repository is required for deployment of this project on Streamlit Cloud.



Web App Link: https://gaurav-van-house-price-predictor-streamlit-heroku-app-g56zmy.streamlitapp.com/



Project Repo: https://github.com/Gaurav-Van/Data_Science__Machine_Learning-Projects



This Model / Project / Web app predicts the price of a Real Estate property / House on the basis of Features like : (area_type, location, 

total_sqft, balcony, bathroom, BHK) 


Model Used - Linear Regression (Multiple Linear Regression)

The Project / Web App is built in Python using the Following Libraries:

 * numpy

 * pandas

 * matplotlib

 * seaborn

 * sklearn

 * pickle

 * flask

 * streamlit

 * json



## Concept Used

1. Data Collection - From Kaggle: https://www.kaggle.com/datasets/amitabhajoy/bengaluru-house-price-data



2. Data Pre-Procesing


* Removing Not-so-important columns

* Checking and removing or replacement of null values

* Outlier detection using Box plot, Outlier treatment using Flooring and Capping 

* Adding new data on the basis of Domain Knowledge




3. EDA - Performing Data analysis on the basis of Domain Knowledge [ do check the jupyter file ] 

4. Model Building



* Encoding 

* As i am dealing with Regression problem, that too linear models so no need of Feature Scalling 

* Dividing the data by Train test split

* Testing Model's Score on divided data [ train_test_split and cross_val_score]

* Model Used - Linear Regression (Multiple Linear Regression)

5. Deployment -  Building web app with the help of streamlit and deploying it on heroku cloud