Updated: Feb 9, 2023
1-Click Kit 
We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on.The decision tree is the most powerful and widely used classification and prediction tool. A Decision tree is a tree structure that looks like a flowchart, with each internal node representing a test on an attribute, each branch representing a test outcome, and each leaf node (terminal node) holding a class label.
The Housing Prices Prediction System predicts house prices using various Data Mining techniques and selects the models with the highest accuracy score. In this system, to log in to the system the admin can log in with a username and password. The admin can manage the training data and has the authority to add, update, delete and view data. The admin can view the list of registered users and their information.
Using machine learning algorithms, we can train our model on a set of data and then predict the ratings for new items. This is all done in Python using numpy, pandas, matplotlib, scikit-learn and seaborn.
kandi kit provides you with a fully deployable House Price Prediction. Source code included so that you can customize it for your requirement.
Deployment Information
Our complete solution and all dependent assets are available in the below library project. This is a fully working deployable project for demo.
The entire solution is available as a package to download and install from the source code repository.
Follow below instructions to download and deploy the solution.
For Windows OS
- Download, extract and double-click the kit installer file to install the kit. Do ensure to extract the zip file before running it.
- The installation may take from 2 to 10 minutes based on bandwidth.
- After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook.
- To run the kit manually, press 'N' and locate the zip file 'House-price-prediction.zip'
- Extract the zip file and navigate to the directory 'House-price-prediction'
- Open the command prompt in the extracted directory 'House -price-prediction' and run the command 'jupyter notebook'
Machine Learning Libraries
The following libraries could be used to create machine learning models which focus on the vision, extraction of data, image processing, and more. Thus making it handy for the users.
pandasby pandas-dev
38689 Version:v2.0.2Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pandasby pandas-dev
Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)
Data Visualization
The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.
matplotlibby matplotlib
17559 Version:v3.7.1
License: No License (null)matplotlib: plotting with Python
matplotlibby matplotlib
Python 17559 Version:v3.7.1 License: No License
seabornby mwaskom
10797 Version:v0.12.2Statistical data visualization in Python
seabornby mwaskom
Python 10797 Version:v0.12.2 License: Permissive (BSD-3-Clause)
Kit Solution Source
Housing Prices Prediction System predicts house prices
house-price-predictionby kandi1clickkits
0 Version:Current License: No License (null)house-price-predictionby kandi1clickkits
Jupyter Notebook 0 Version:Current License: No License
Support
If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc .