Bangalore House Price Prediction

This Project is Also Deployed on -> https://gaurav-van-house-price-predictor-streamlit-heroku-app-g56zmy.streamlitapp.com/

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

  ---

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

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

• numpy
• pandas
• matplotlib
• seaborn
• sklearn
• pickle
• flask
• streamlit
• json

Built With

• data-analysis
• data-science
• json
• jupyter-notebook
• machine-learning
• matplotlib
• numpy
• pandas
• pickle
• python
• scikit-learn
• seaborn
• streamlit