Housing_Price_Prediction_In_Beijing

Installation Guide

1. Clone or Fork the Project
2. Create a Virtual Enviroment
3. go to same virtual enviroment and write below cmd
4. pip install -r requirements.txt

Project Highlights

1. Research Paper
2. User Friendly
3. Accuracy
4. Open Source

Table Of Content

1. Project Descriptiom
   A. Problem Statement
   B. Best Possible Solutions
   C. Introduction About Project
   D. Tools and Libraries
2. Data Collection
3. Generic Flow Of Project
4. EDA
   A. Data Cleaning
   B. Feature Engineering
   C. Data Normalization
5. Choosing Best ML Model
6. Model Creation
7. Model Deployment
8. Model Conclusion
9. Project Innovation
10. Limitation And Next Step
11. Working Project Video

1. Project Description

A. Problem Statement

Thousands of houses are sold everyday. There are some questions every buyer asks himself like: What is the actual price that this house deserves? Am I paying a fair price?

B. Best Possible Solutions

a.Housing Expert
b.Intuition About House
c.Using Machine Learning

C. Introduction About Project

House Price prediction are very stressful work as we have to consider different things while buying a house like the structure and the rooms kitchen parking space and gardens. People don’t know about the factor which influence the house price. But by using the Machine learning we can easily find the house which is to be prefect for us and helps to predict the price accurately.

D. Tools and Libraries

Tools

a.Python
b.Jupyter Notebook
c. Flask
d. HTML
e. CSS
f. JS
g. Heroku
h. GitHub

Libraries

a.Pandas
b.Scikit Learn
c.Numpy
d.Seaborn
e.Matpoltlib

2. Data Collection

For this project we used the data that is available on kaggle.(click here for data).There are 26 columns and 318851 Rows. These are the major point about the data set.
url: the url which fetches the data
id: the id of transaction
Lng: and Lat coordinates, using the BD09 protocol.
Cid: community id
tradeTime: the time of transaction
DOM: active days on market
followers: the number of people follow the transaction.
totalPrice: the total price
price: the average price by square
square: the square of house
livingRoom: the number of living room
drawingRoom: the number of drawing room
kitchen: the number of kitchen
bathroom the number of bathroom
floor: the height of the house. I will turn the Chinese characters to English in the next version.
buildingType: including tower( 1 ) , bungalow( 2 )，combination of plate and tower( 3 ), plate( 4 ).
constructionTime: the time of construction
renovationCondition: including other( 1 ), rough( 2 ),Simplicity( 3 ), hardcover( 4 )
buildingStructure: including unknow( 1 ), mixed( 2 ), brick and wood( 3 ), brick and concrete( 4 ),steel( 5 ) and steel-concrete composite ( 6 ).
ladderRatio: the proportion between number of residents on the same floor and number of elevator of ladder. It describes how many ladders a resident have on average.
elevator: have ( 1 ) or not have elevator( 0 )
fiveYearsProperty: if the owner have the property for less than 5 years.

3. Generic Flow Of Project

4. EDA

A.Data Cleaning

we have 26 columns ,from these we don't want some column(i.e. url,id,cid) then we will perform data cleaning wich involve following steps. our target variable is totalPrice
a. Impute/Remove missing values or Null values (NaN)
b. Remove unnecessary and corrupted data.
c. Date/Text parsing if required.

we handle NAN value using appropriate solutions.

DOM Column have more than 50% value are missing it's better to delete that column

some column have unique character. we solve these problem using split method and create seprate column for unique character.

We also have a categorical data we handle such kind of data using dummies variable concept. following are the columns which have categorical data.
a. renovationCondition
b. buildingStructure
c. buildingType
d. district
e. elevator
f. floor_type

Summary of the Heat-Map
a. totalPrice is highly corellated with community average,square,bathroom,livingroom and Trde Time.
b. totalprice is highly negative corellated with ladderRatio,lat and lng.

Summary of the Density Plot
a. most of the output features is lies between 0-2500

Summary of Scatterplot
a. Most of the House Followers 0-400.

Summery of Scatterplot with respect to renovationCondition
a. most of the expensive houses have HardCover as a renovation condition

Summary of lineplot
a. Most of the peoples average are lies in 12500-150000 ...

B. Feature Engineering

we found outlier in our data ..

from the above figure we can notice that we have an outlier present in our dataset.
for outlier we can use IQR method and after using IQR method.Now, our data looks fine.

using the feature engineering we got out top 30 features with respect to totalPrice .

So,these are the top 20 features for our model
a. tradeTime
b. CommunityAverage
c. square
d. livingRoom
e. bathRoom
f. drawingRoom
g. renovationCondition
h. buildingStructure
i. elevator
j. constructionTime
k. Followers

C. Data Normalization

Normalization (min-max Normalization)
In this approach we scale down the feature in between 0 to 1

we have numerical column where we can apply min-max Normalization.

5. Choosing Best ML Model

List of the model that we can use for our problem
a. LinearRegression model
b. KNN Model
c. Decesion Tree
d. Random Forest

Using the linearRegression we got only 75 % accuracy.

Using the Random Forest we got 98 % accuracy on train data and 89 % on test data .so,we can consider RandomForest as a Best Algorithm for this problem.

6. Model Creation

So,using a RandomForest we got good accuracy , we can Hyperparameter tuning for best accuracy.

Algorithm that can be used for Hyperparameter tuning are :-

a. GridSearchCV
b. RandomizedSearchCV
c. Bayesian Optimization-Automate Hyperparameter Tuning (Hyperopt)
d. Sequential model based optimization
e. Optuna-Automate Hyperparameter Tuning
f. Genetic Algorithm

Main parameters used by RandomForest Algorithm are :-

a. n_estimators ---> The number of trees in the forest.
b. criterion--->{"mse", "mae"}-->The function to measure the quality of a split
c. max_features--->{"auto", "sqrt", "log2"}--> The number of features to consider when looking for the best split:

So, After Hyperparameter Tuning we got 90 % accuracy on test data and 94 % accuracy on train data.
Now,Accuracy of model seems to be very good .so we can save the model using pickle.

7. Model Deployment

After creating model ,we integrate that model with beautiful UI. for the UI part we used HTML,CSS,JS and Flask.

8. Model Conclusion

Model predict 90% accurately on test data and 94% accurately on train data .

9. Project Innovation

a. Easy to use
b. open source
c. Best accuracy
d. GUI Based Application

10. Limitation And Next Step

Limitation are :-
a. Mobile Application
b. Accuracy can be improve
c. Model Size is heavy(~310 mb )
d. Feature is limited

Next Step are :-
a. we will work on mobile application
b. we will reduce the size of model using PCA .

11. Working Project Video