House price prediction using PyCaret

👉🏻What is PyCaret?

Pycaret is an open-source machine learning library in python to train and deploy supervised and unsupervised machine learning models in a low-code environment.

In data science, your codes grow exponentially during the workflow, but using PyCaret you can build powerful solutions in a low code environment. You can build and deploy ML models in seconds using PyCaret.

💻Installation

!pip install pycaret

## for data analysis
!pip install pandas_profiling

Import libraries:

import pandas as pd 
import pandas_profiling as pp

Here we are using this house price prediction dataset to explore PyCaret

train_house=pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')

test_house=pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')

#pycaret #regression #machine-learning #artificial-intelligence #classification

What is GEEK

Buddha Community

House price prediction using PyCaret

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

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.
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Why Use WordPress? What Can You Do With WordPress?

Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?

WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:

1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.

2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.

3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.

4. Shortcodes
Since we gave you a problem in the previous section, we would also give you a perfect solution for it. You might not know to code, but you have shortcodes. Shortcodes help you execute functions without having to code. It is an easy way to build an amazing website, add new features, customize plugins easily. They are short lines of code, and rather than memorizing multiple lines; you can have zero technical knowledge and start building a feature-rich website or application.
There are also plugins like Shortcoder, Shortcodes Ultimate, and the Basics available on WordPress that can be used, and you would not even have to remember the shortcodes.

5. Build Online Stores
If you still think about why to use WordPress, use it to build an online store. You can start selling your goods online and start selling. It is an affordable technology that helps you build a feature-rich eCommerce store with WordPress.
WooCommerce is an extension of WordPress and is one of the most used eCommerce solutions. WooCommerce holds a 28% share of the global market and is one of the best ways to set up an online store. It allows you to build user-friendly and professional online stores and has thousands of free and paid extensions. Moreover as an open-source platform, and you don’t have to pay for the license.
Apart from WooCommerce, there are Easy Digital Downloads, iThemes Exchange, Shopify eCommerce plugin, and so much more available.

6. Security Features
WordPress takes security very seriously. It offers tons of external solutions that help you in safeguarding your WordPress site. While there is no way to ensure 100% security, it provides regular updates with security patches and provides several plugins to help with backups, two-factor authorization, and more.
By choosing hosting providers like WP Engine, you can improve the security of the website. It helps in threat detection, manage patching and updates, and internal security audits for the customers, and so much more.

Read More

#use of wordpress #use wordpress for business website #use wordpress for website #what is use of wordpress #why use wordpress #why use wordpress to build a website

Lisa joly

Lisa joly

1624586400

gZIL Price Prediction Could Hit $1500 in 2021 , Here's Why!! DO NOT MISS!!!

Hey guys in this video I am going to talk about gZil the governance token for Zilliqa and why you should absolutely should be hodling or buying more if it now. I don’t think people understand the real value of gZIL because the price kept dumping because people were earning it by staking and then selling it off.

📺 The video in this post was made by Crypto expat
The origin of the article: https://www.youtube.com/watch?v=H1jaWuS3r2w
🔺 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.
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Rusty  Shanahan

Rusty Shanahan

1595563500

House Prices Prediction Using Deep Learning

In this tutorial, we’re going to create a model to predict House prices🏡 based on various factors across different markets.

Problem Statement

The goal of this statistical analysis is to help us understand the relationship between house features and how these variables are used to predict house price.

Objective

  • Predict the house price
  • Using two different models in terms of minimizing the difference between predicted and actual rating

Data used: Kaggle-kc_house Dataset

GitHub: you can find my source code here


Step 1: Exploratory Data Analysis (EDA)

First, Let’s import the data and have a look to see what kind of data we are dealing with:

#import required libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
#import Data
Data = pd.read_csv('kc_house_data.csv')
Data.head(5).T
#get some information about our Data-Set
Data.info()
Data.describe().transpose()

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5 records of our dataset

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Information about the dataset, what kind of data types are your variables

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Statistical summary of your dataset

Our features are:

✔️**Date:**_ Date house was sold_

✔️**Price:**_ Price is prediction target_

✔️**_Bedrooms: _**Number of Bedrooms/House

✔️**Bathrooms:**_ Number of bathrooms/House_

✔️**Sqft_Living:**_ square footage of the home_

✔️**Sqft_Lot:**_ square footage of the lot_

✔️**Floors:**_ Total floors (levels) in house_

✔️**Waterfront:**_ House which has a view to a waterfront_

✔️**View:**_ Has been viewed_

✔️**Condition:**_ How good the condition is ( Overall )_

✔️**Grade:**_ grade given to the housing unit, based on King County grading system_

✔️**Sqft_Above:**_ square footage of house apart from basement_

✔️**Sqft_Basement:**_ square footage of the basement_

✔️**Yr_Built:**_ Built Year_

✔️**Yr_Renovated:**_ Year when house was renovated_

✔️**Zipcode:**_ Zip_

✔️**Lat:**_ Latitude coordinate_

✔️**_Long: _**Longitude coordinate

✔️**Sqft_Living15:**_ Living room area in 2015(implies — some renovations)_

✔️**Sqft_Lot15:**_ lotSize area in 2015(implies — some renovations)_


Let’s plot couple of features to get a better feel of the data

#visualizing house prices
fig = plt.figure(figsize=(10,7))
fig.add_subplot(2,1,1)
sns.distplot(Data['price'])
fig.add_subplot(2,1,2)
sns.boxplot(Data['price'])
plt.tight_layout()
#visualizing square footage of (home,lot,above and basement)
fig = plt.figure(figsize=(16,5))
fig.add_subplot(2,2,1)
sns.scatterplot(Data['sqft_above'], Data['price'])
fig.add_subplot(2,2,2)
sns.scatterplot(Data['sqft_lot'],Data['price'])
fig.add_subplot(2,2,3)
sns.scatterplot(Data['sqft_living'],Data['price'])
fig.add_subplot(2,2,4)
sns.scatterplot(Data['sqft_basement'],Data['price'])
#visualizing bedrooms,bathrooms,floors,grade
fig = plt.figure(figsize=(15,7))
fig.add_subplot(2,2,1)
sns.countplot(Data['bedrooms'])
fig.add_subplot(2,2,2)
sns.countplot(Data['floors'])
fig.add_subplot(2,2,3)
sns.countplot(Data['bathrooms'])
fig.add_subplot(2,2,4)
sns.countplot(Data['grade'])
plt.tight_layout()

With distribution plot of price, we can visualize that most of the prices are between 0 and around 1M with few outliers close to 8 million (fancy houses😉). It would make sense to drop those outliers in our analysis.

#linear-regression #machine-learning #python #house-price-prediction #deep-learning #deep learning