dribblewithclong/House-Prices-Prediction-in-Ho-Chi-Minh-City

HO CHI MINH CITY HOUSE PRICE PREDICTION

Introduction

• In this project, I will build a web app for predicting the house price in Ho Chi Minh City based on the datasets scraped at website Propzy.
• App: https://hcmhouseprice.herokuapp.com/

Preview

Workflow

0. Structures

├───assets  (containing file for web layout design)
│       style.css
├───data chunk (containing separated data for each district)
│
├───data  (containing data for processing)
│
├── app.py        
├── crawl_data.ipynb
├── eda_cleaning.ipynb
├── feature_engineering_selection.ipynb
├── model.ipynb
├── final_model.sav
├── Procfile           
└── requirements.txt

1. Data Scraping

• For scraping, I using BeautifulSoup to collect data from website Propzy.

2. Data Cleaning

• Drop duplicated values
• Extracted and create new information from text desription of each house.
• Correct wrong price and numeric value of observations
• Correct missing values

3. Data Exploratory Analysis

• Examine missing values
• Analyze numerical variables and their distribution
• Analyze categorical variables and their cardinality
• Detect outliers
• Analyze relationship between all the features of house and the house price

4. Feature Engineering

• Remove outliers
• Complete missing values
• Transform numerical variables due to its skew distribution
• Encode categorical variables for model building
• Create new feature from heading title
• Oversampling data
• Cluster and PCA

5. Feature Selection

• Drop redundant features
• Remove highly correlated features
• Examine features importance
• Remove anomaly observations

6. Model Bulding and Tuning

• Perform K-fold cross validation
• Use Random Forest, XGB and LightGBM algorithm for training datasets
• Perform RandomizedSearchCV for optimizing score

7. Web App Deployment

• For app deployment, I using Dash to design and represent. I also using Heroku for hosting web app.

Conclusion

• This project aims to help people to somewhat determine a price for their real estate to sell as well as to be able to determine if the houses they intend to buy are being sold for a reasonable price. However, above all, the main purpose of this project is to have a fun time when playing with machine learning.