Housing_Price_Prediction

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Housing_Price_Prediction

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# 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](https://www.kaggle.com/ruiqurm/lianjia) 
 A. Problem Statement 
 B. Best Possible Solutions 
 C. Introduction About Project 
 D. Tools and Libraries
2. [Data Collection](https://www.kaggle.com/ruiqurm/lianjia)
3. Generic Flow Of Project
4. [EDA](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Housing_Price_Predication_Project_EDA.ipynb) 
 A. Data Cleaning 
 B. Feature Engineering 
 C. Data Normalization
5. [Choosing Best ML Model](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Machine_Learning_Model_Creation.ipynb)
6. [Model Creation](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Machine_Learning_Model_Creation.ipynb)
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](https://www.kaggle.com/ruiqurm/lianjia)).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
![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/18.png)

### 4. EDA
![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/1.png)
#### 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.

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/2.png) 
we handle NAN value using appropriate solutions.

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/3.png) 
DOM Column have more than 50% value are missing it's better to delete that column

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/4.png) 
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

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/5.png) 
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.

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/6.png) 
Summary of the Density Plot 
a. most of the output features is lies between 0-2500

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/7.png) 
Summary of Scatterplot 
a. Most of the House Followers 0-400.

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/8.png) 
Summery of Scatterplot with respect to renovationCondition 
a. most of the expensive houses have HardCover as a renovation condition

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/9.png) 
Summary of lineplot 
a. Most of the peoples average are lies in 12500-150000 ...

#### B. Feature Engineering
we found outlier in our data ..

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/10.png) 
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.
![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/11.png)

using the feature engineering we got out top 30 features with respect to totalPrice .
![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/12.png)

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. 
![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/13.png)

### 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

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/14.png) 
Using the linearRegression we got only 75 % accuracy.

![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/15.png) 
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. 
![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/16.png) 
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.
![](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/17.png)

### 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
![Working Projecct](https://github.com/bibek376/Housing_Price_Prediction/blob/master/Picture_For_README/1_Video.gif)

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