Singapore Housing Prices ML Prediction — Analyse Singapore’s Property Price

In this final part, I will share some popular machine learning algorithms to predict the housing prices and the live model that I have built. My objective is to find a model that can generate a high accuracy of the housing prices, based on the available dataset, such that given a new property and with the required information, we will know whether the property is over or under-valued.


Brief introduction of the machine learning algorithms used

I explore 5 machine learning algorithms that are used to predict the housing prices in Singapore, namely multi-linear regression, lasso, ridge, decision tree and neural network.

Multi-linear regression model, as its name suggest, is a widely used model that assumes linearity between the independent variables and dependent variable (price). This will be my baseline model for comparison.

Lasso and ridge are models to reduce model complexity and overfitting when there are too many parameters. For example, the lasso model will effectively shrink some of the variables, such that it only takes into account some of the important factors. While there are only 17 variables, in the dataset and the number of variables may not be considered extensive, it will still be a good exercise to analyse the effectiveness of these models.

Decision tree is an easily understandable model which uses a set of binary rules to achieve the target value. This is extremely useful for decision making as a tree diagram can be plotted to aid in understanding the importance of each variable (the higher the variable in the tree, the more important the variable).

Last, I have also explored a simple multi-layer perceptron neural network model. Simply put, the data inputs is put through a few layers of “filters” (feed forward hidden layers) and the model learns how to minimise the loss function by changing the values in the “filters” matrices.

#predictive-analytics #predictive-modeling #machine-learning #sklearn #housing-prices

What is GEEK

Buddha Community

Singapore Housing Prices ML Prediction — Analyse Singapore’s Property Price

Singapore Housing Prices ML Prediction — Analyse Singapore’s Property Price

In this final part, I will share some popular machine learning algorithms to predict the housing prices and the live model that I have built. My objective is to find a model that can generate a high accuracy of the housing prices, based on the available dataset, such that given a new property and with the required information, we will know whether the property is over or under-valued.


Brief introduction of the machine learning algorithms used

I explore 5 machine learning algorithms that are used to predict the housing prices in Singapore, namely multi-linear regression, lasso, ridge, decision tree and neural network.

Multi-linear regression model, as its name suggest, is a widely used model that assumes linearity between the independent variables and dependent variable (price). This will be my baseline model for comparison.

Lasso and ridge are models to reduce model complexity and overfitting when there are too many parameters. For example, the lasso model will effectively shrink some of the variables, such that it only takes into account some of the important factors. While there are only 17 variables, in the dataset and the number of variables may not be considered extensive, it will still be a good exercise to analyse the effectiveness of these models.

Decision tree is an easily understandable model which uses a set of binary rules to achieve the target value. This is extremely useful for decision making as a tree diagram can be plotted to aid in understanding the importance of each variable (the higher the variable in the tree, the more important the variable).

Last, I have also explored a simple multi-layer perceptron neural network model. Simply put, the data inputs is put through a few layers of “filters” (feed forward hidden layers) and the model learns how to minimise the loss function by changing the values in the “filters” matrices.

#predictive-analytics #predictive-modeling #machine-learning #sklearn #housing-prices

Mike  Kozey

Mike Kozey

1656151740

Test_cov_console: Flutter Console Coverage Test

Flutter Console Coverage Test

This small dart tools is used to generate Flutter Coverage Test report to console

How to install

Add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

dev_dependencies:
  test_cov_console: ^0.2.2

How to run

run the following command to make sure all flutter library is up-to-date

flutter pub get
Running "flutter pub get" in coverage...                            0.5s

run the following command to generate lcov.info on coverage directory

flutter test --coverage
00:02 +1: All tests passed!

run the tool to generate report from lcov.info

flutter pub run test_cov_console
---------------------------------------------|---------|---------|---------|-------------------|
File                                         |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
 print_cov_constants.dart                    |    0.00 |    0.00 |    0.00 |    no unit testing|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|

Optional parameter

If not given a FILE, "coverage/lcov.info" will be used.
-f, --file=<FILE>                      The target lcov.info file to be reported
-e, --exclude=<STRING1,STRING2,...>    A list of contains string for files without unit testing
                                       to be excluded from report
-l, --line                             It will print Lines & Uncovered Lines only
                                       Branch & Functions coverage percentage will not be printed
-i, --ignore                           It will not print any file without unit testing
-m, --multi                            Report from multiple lcov.info files
-c, --csv                              Output to CSV file
-o, --output=<CSV-FILE>                Full path of output CSV file
                                       If not given, "coverage/test_cov_console.csv" will be used
-t, --total                            Print only the total coverage
                                       Note: it will ignore all other option (if any), except -m
-p, --pass=<MINIMUM>                   Print only the whether total coverage is passed MINIMUM value or not
                                       If the value >= MINIMUM, it will print PASSED, otherwise FAILED
                                       Note: it will ignore all other option (if any), except -m
-h, --help                             Show this help

example run the tool with parameters

flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock
---------------------------------------------|---------|---------|---------|-------------------|
File                                         |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|

report for multiple lcov.info files (-m, --multi)

It support to run for multiple lcov.info files with the followings directory structures:
1. No root module
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
2. With root module
<root>/coverage/lcov.info
<root>/lib/src
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
You must run test_cov_console on <root> dir, and the report would be grouped by module, here is
the sample output for directory structure 'with root module':
flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock --multi
---------------------------------------------|---------|---------|---------|-------------------|
File                                         |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_a -                            |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_b -                            |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|

Output to CSV file (-c, --csv, -o, --output)

flutter pub run test_cov_console -c --output=coverage/test_coverage.csv

#### sample CSV output file:
File,% Branch,% Funcs,% Lines,Uncovered Line #s
lib/,,,,
test_cov_console.dart,0.00,0.00,0.00,no unit testing
lib/src/,,,,
parser.dart,100.00,100.00,97.22,"97"
parser_constants.dart,100.00,100.00,100.00,""
print_cov.dart,100.00,100.00,82.91,"29,49,51,52,171,174,177,180,183,184,185,186,187,188,279,324,325,387,388,389,390,391,392,393,394,395,398"
print_cov_constants.dart,0.00,0.00,0.00,no unit testing
All files with unit testing,100.00,100.00,86.07,""

Installing

Use this package as an executable

Install it

You can install the package from the command line:

dart pub global activate test_cov_console

Use it

The package has the following executables:

$ test_cov_console

Use this package as a library

Depend on it

Run this command:

With Dart:

 $ dart pub add test_cov_console

With Flutter:

 $ flutter pub add test_cov_console

This will add a line like this to your package's pubspec.yaml (and run an implicit dart pub get):

dependencies:
  test_cov_console: ^0.2.2

Alternatively, your editor might support dart pub get or flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:test_cov_console/test_cov_console.dart';

example/lib/main.dart

import 'package:flutter/material.dart';

void main() {
  runApp(MyApp());
}

class MyApp extends StatelessWidget {
  // This widget is the root of your application.
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Flutter Demo',
      theme: ThemeData(
        // This is the theme of your application.
        //
        // Try running your application with "flutter run". You'll see the
        // application has a blue toolbar. Then, without quitting the app, try
        // changing the primarySwatch below to Colors.green and then invoke
        // "hot reload" (press "r" in the console where you ran "flutter run",
        // or simply save your changes to "hot reload" in a Flutter IDE).
        // Notice that the counter didn't reset back to zero; the application
        // is not restarted.
        primarySwatch: Colors.blue,
        // This makes the visual density adapt to the platform that you run
        // the app on. For desktop platforms, the controls will be smaller and
        // closer together (more dense) than on mobile platforms.
        visualDensity: VisualDensity.adaptivePlatformDensity,
      ),
      home: MyHomePage(title: 'Flutter Demo Home Page'),
    );
  }
}

class MyHomePage extends StatefulWidget {
  MyHomePage({Key? key, required this.title}) : super(key: key);

  // This widget is the home page of your application. It is stateful, meaning
  // that it has a State object (defined below) that contains fields that affect
  // how it looks.

  // This class is the configuration for the state. It holds the values (in this
  // case the title) provided by the parent (in this case the App widget) and
  // used by the build method of the State. Fields in a Widget subclass are
  // always marked "final".

  final String title;

  @override
  _MyHomePageState createState() => _MyHomePageState();
}

class _MyHomePageState extends State<MyHomePage> {
  int _counter = 0;

  void _incrementCounter() {
    setState(() {
      // This call to setState tells the Flutter framework that something has
      // changed in this State, which causes it to rerun the build method below
      // so that the display can reflect the updated values. If we changed
      // _counter without calling setState(), then the build method would not be
      // called again, and so nothing would appear to happen.
      _counter++;
    });
  }

  @override
  Widget build(BuildContext context) {
    // This method is rerun every time setState is called, for instance as done
    // by the _incrementCounter method above.
    //
    // The Flutter framework has been optimized to make rerunning build methods
    // fast, so that you can just rebuild anything that needs updating rather
    // than having to individually change instances of widgets.
    return Scaffold(
      appBar: AppBar(
        // Here we take the value from the MyHomePage object that was created by
        // the App.build method, and use it to set our appbar title.
        title: Text(widget.title),
      ),
      body: Center(
        // Center is a layout widget. It takes a single child and positions it
        // in the middle of the parent.
        child: Column(
          // Column is also a layout widget. It takes a list of children and
          // arranges them vertically. By default, it sizes itself to fit its
          // children horizontally, and tries to be as tall as its parent.
          //
          // Invoke "debug painting" (press "p" in the console, choose the
          // "Toggle Debug Paint" action from the Flutter Inspector in Android
          // Studio, or the "Toggle Debug Paint" command in Visual Studio Code)
          // to see the wireframe for each widget.
          //
          // Column has various properties to control how it sizes itself and
          // how it positions its children. Here we use mainAxisAlignment to
          // center the children vertically; the main axis here is the vertical
          // axis because Columns are vertical (the cross axis would be
          // horizontal).
          mainAxisAlignment: MainAxisAlignment.center,
          children: <Widget>[
            Text(
              'You have pushed the button this many times:',
            ),
            Text(
              '$_counter',
              style: Theme.of(context).textTheme.headline4,
            ),
          ],
        ),
      ),
      floatingActionButton: FloatingActionButton(
        onPressed: _incrementCounter,
        tooltip: 'Increment',
        child: Icon(Icons.add),
      ), // This trailing comma makes auto-formatting nicer for build methods.
    );
  }
}

Author: DigitalKatalis
Source Code: https://github.com/DigitalKatalis/test_cov_console 
License: BSD-3-Clause license

#flutter #dart #test 

Mery tris

Mery tris

1623601080

BIG NEWS!!! POLYGON (MATIC) PRICE PREDICTION 2021 - PRICE EXPLOSION INCOMING!!

📺 The video in this post was made by K Crypto
The origin of the article: https://www.youtube.com/watch?v=-uWpSe8GuP0
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
Cryptocurrency trading is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
(There is no limit to the amount of credit you can earn through referrals)
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#bitcoin #blockchain #polygon #price prediction 2021 #price explosion incoming #big news!!! polygon (matic) price prediction 2021 - price explosion incoming

Office Cleaning Services Singapore | Nea Certified | Homeworz

Cleaning doesn’t mean mopping, vacuuming, and emptying trash bins. It has more significant values and procedures to place in order. When you hire professional office cleaning services, Singapore ensures that appropriate services are served to get your office back into a neat and tidy place. Experienced professionals ensure that all the hidden dust, dirt, allergens, and pathogens that remain in premises are eliminated appropriately.

Some Significant reasons to hire a professional cleaning service Singapore company over an ordinary one:

  • Analysation of the amount of work that needs to be done
  • Qualified staff that use their experience and knowledge
  • Cost-effective services with health safety promises
  • Accountability after work
  • Easy to approach and questioning if work is not performed appropriately
  • Bring Industry standards equipment into practice for quick and prompt services

What are some benefits of hiring professional office cleaning services in Singapore?

  1. Productivity
    Your employees feel more productive when they get a fresh, clean, and tidy working environment. Nobody wants to perform their duties where a clean working environment is not promoted. Bad and unclean surfaces may lead to serious health issues for people who work there.
    Furthermore, it is mandatory as per the government’s terms to keep the work environment efficient and clean for people who spend half of their valuable time to boost the business’s sales. Lately, a survey has found that more productivity can be obtained if organizations promote and support healthy work environment culture.

  2. Boost Sales
    Businesses are easy to open but hard to run appropriately due to high competition. However, a clean work environment is a small thing if you can take care of. In return, you may get double business to believe it or not. Lately, an incident occurred when a business owner performed hard duties but could not obtain huge clients.
    One day he approached an overseas client, and they agreed to visit the company’s office. The client found the premises well maintained, clean and admirable to perform work duties. The client was very impressed, and they signed the contract with the company to work together for upcoming years. Here the kudos goes to the owner who loved to keep the premises clean and office cleaning services Singapore performed cleaning duties.

  3. Long Tenure Cost Savings
    In general, most companies hesitate to hire professional cleaning services in Singapore. They have the ideology that such services are expensive and might hamper their office budget. But they seem far from reality. The truth is a regular cleaning employee-only vacuum; mop your floor and empty dustbins. Whereas professional service providers will perform ample work duties that include high and low dusting, thoroughly vacuuming carpets, mopping floors with adequate cleaning agents, remove dust from tables and blinds, and so on.

The Final Words
Your office is your pride, and you won’t want to demolish your satisfaction because of the inappropriate environment. So without having a second thought, opt for office cleaning services in Singapore today and see the difference.

#cleaning services singapore #carpet cleaning singapore #cleaning companies singapore #house cleaning singapore #office cleaning services singapore

Trevor  Russel

Trevor Russel

1618115820

New Hackathon For Data Scientists — Workation Price Prediction Challenge

MachineHack, in association with Analytics India Magazine, has come up with yet another hackathon for the machine learning community — the Workation Price Prediction Challenge.

In the light of the new normal, different websites have started providing packages to work from different locations. The concept of workation — a portmanteau of work and vacation– is gaining currency. However, it is challenging to find a good place with all the amenities, including high-speed internet and a comfortable stay within the budget.

Thus, to solve the real-world problem of finding the best deals for workations, MachineHack is challenging the machine learning community to build a model for predicting the price per person for workation trips.

To facilitate this, MachineHack has collected workation packages in and around India — starting from Kashmir to Kanyakumari and from Gujarat to Assam. The data has more than 18000+ rows of different packages with details like start location, hotel type, cost per person, destination, itinerary, and many more. Using this dataset, along with the knowledge of machine learning, deep learning, and model building, the participants need to create a model that can efficiently and accurately predict a workation trip’s expense.

#data science hackathon #hackathon #hackathon for data scientists #machine learning hackathon #ml hackathon #predicting workation price #workation price prediction challenge