This document outlines a project to build a tool to predict housing prices in Bangalore. It describes collecting housing data, cleaning it, and using machine learning algorithms like linear regression, gradient boosting, random forest, and extra trees regression to predict prices. The models were trained and tested on the data, with the best performing model selected. A Flask API was created to host the model and a client interface was designed.

1. Bangalore-housing-price-
prediction
19012011058 Tanvi Patel
19012011090 Jinal Vasoya

2. Outline
Introduction
Project Summary
Technology & Tools Used
How it Works?
What it Does?

3. Introduction
 Problems faced during buying a house
1) Buying a house is a stressful thing
2) Buyers are generally not aware of factors that influence the house prices.
3) Many problems are faced during buying a house.
4) Hence real estate agents are trusted with the communication between
buyers and sellers as well as laying down a legal contract for the transfer.
This just creates a middle man and increases the cost of houses

4. Project Summary
 Designed a tool that estimates house prices to help people when they
looking for new house/Apartment.
 Applied Different Data cleaning strategies to handle null values and
outliers.
 Applied various machine learning models with Hyper parameter tuning
techniques to get the best results with minimum errors.
 Built a client-facing API using flask.

5. Technology & Tools Used
• Python
• Java Script
• CSS
• HTML
• Jupyter NoteBook
• Packages: pandas, numpy, sklearn, matplotlib, seaborn, xgboost, flask,
json, pickle

6. Coun…
Model Building:
First, I transformed the categorical variables into dummy variables.
I also split the data into train and test sets with a test size of 20%.
I tried different models:
 Multiple Linear Regressor
 Gradient Boosting Regressor
 Random Forest Regressor
 Extra-trees Regressor

7. How It Works?
• Collecting data: first step was to collect data we collected data from kaggle
form our training data set.
• Then we trained the model using machine learning algorithm which in this
case is
 Multiple Linear Regressor
 Gradient Boosting Regressor
 Random Forest Regressor
 Extra-trees Regressor
• Based on the generated graphs we predict the cost of the house.
• Created flask API by using Flask that was hosted on local server and AWS-
EC2. Also, designed client side interface.