Monty Boehm

Monty Boehm

1678783260

DietPi: Lightweight Justice for Your Single-board Computer!

DietPi

Lightweight justice for your single-board computer! 

optimised • simplified • for everyone 

Ready to run optimised software choices with dietpi-software 
Feature-rich configuration tool for your device with dietpi-config.

Introduction

DietPi is an extremely lightweight Debian-based OS. It is highly optimised for minimal CPU and RAM resource usage, ensuring your SBC always runs at its maximum potential.

The dietpi programs use lightweight whiptail menus. You'll spend more time enjoying DietPi and applications you need and less time staring at the command line.

Use dietpi-software to quick and easy install Ready to Run & Optimised applications for your system. DietPi will do all the necessary configurations, including starting the services. Few highlights: Desktop Environments, Remote Desktop Access, Media Systems & Players, BitTorrent & Downloading, Cloud & Backup, Gaming & Emulation, Social & Search, Camera & Surveillance, Networking, System Stats & Management, Home Automation, Hardware & Voice Projects, Webserver Stacks, DNS Servers / Pi-hole, File Servers, Printing and much more.

Use dietpi-services to control which installed software has higher or lower priority levels (nice, affinity, policy scheduler).

dietpi-update automatically checks for updates and informs you when they are available. Update instantly, without having to write a new image. DietPi automation allows you to completely automate a DietPi installation with no user input, simply by configuring dietpi.txt before powering on.

The DietPi Project Team

The full list of code contributors can be viewed here.

Contributors

Micha

Joined Q3 2017

Project lead (20/02/2019 and onwards), source code contributor, bug fixes, software improvements, DietPi forum administrator.

Daniel Knight

Project founder and previous project lead (19/02/2019 and previous), source code contributor and tester.

JohnVick

Joined 2016-06-08

DietPi forum co-administrator, management, support, testing and valuable feedback.

sal666

Joined 2017-07-26

Creator and maintainer of the first Clonezilla based installer images for x86_64 UEFI systems.

Joulinar

Joined Q4 2019

DietPi forum moderator, support, testing, bug reports + investigation and valuable feedback.

StephanStS

Joined Q4 2019

NanoPi image creator, tester and bug reports.

Petru

Joined 2020-05-31

DietPi documentation author, product manager, SEO and DietPi visibility recommendations.

ravenclaw900

Joined 2020-10-11

Source code contributor, creator of the DietPi-Dashboard and many software implementations.

yumiris

Joined 2018-04-16

Creator and maintainer of the first DietPi Hyper-V images.

Collaborations

DietPi + Amiberry

Since 2016-09-02

Joint venture to bring you the ultimate Amiga experience on your SBC, running lightweight and optimised DietPi at its core: https://github.com/MichaIng/DietPi/issues/474

Hall of Fame

K-Plan

Joined 2016-01-01

Contributions to the DietPi in general, in-depth testing, bug finding and valuable feedback, forum moderator.

ZombieVirus

Joined 2016-03-20

DietPi forum moderator and version history maintainer on forums.

Rhkean

Joined 2018-03-01

Contributions to the DietPi in general, including source code, testing, new devices, forum moderator.

Pilovali

Joined 2015-10-10

Provided dietpi.com web hosting for 1 year until April 17th 2016. Additionally: forum moderator, testing, bug reporting.

Xenfomation

Joined 2016-04-01

Contributions to the DietPi in general, including source code and VirtualBox image creation/conversion.

AWL29

Joined 2016-10-01

Created the first DietPi image for NanoPi M3/T3.

Contributing

Git coders, please use the active development branch: dev

Are you able to:

  • Provide feedback and/or test areas of DietPi, to improve the user experience?
  • Report bugs?
  • Improve/add more features to the DietPi website or documentation?
  • Compile software for our supported SBCs?
  • Contribute to DietPi with programming on GitHub?
  • Suggest new software that we can add to the dietpi-software install system?

If so, let us know! We are always looking for talented people who believe in the DietPi project, and, wish to contribute in any way you can.

Also read our contribute page for an overview of way to support DietPi.

License

DietPi Copyright (C) 2022 Contributors

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/

Links

DietPi Source

DietPi Files

  • All files located in (recursively):
    • /var/lib/dietpi/
    • /var/tmp/dietpi/
    • /boot/dietpi/
  • /boot/dietpi.txt
  • /boot/config.txt (RPi)
  • /boot/boot.ini (Odroid)
  • All files prefixed with: dietpi-

The above GPLv2 documentation also applies to all mentioned files!

3rd Party Sources/Credits

Links to hardware and software manufacturers, sources and build instructions used in DietPi:

Website • Downloads • Documentation • Forum • Blog

Download Details:

Author: MichaIng
Source Code: https://github.com/MichaIng/DietPi 
License: GPL-2.0 license

#shell #bash #lightweight #debian #optimization #raspberrypi 

What is GEEK

Buddha Community

DietPi: Lightweight Justice for Your Single-board Computer!
Monty Boehm

Monty Boehm

1678783260

DietPi: Lightweight Justice for Your Single-board Computer!

DietPi

Lightweight justice for your single-board computer! 

optimised • simplified • for everyone 

Ready to run optimised software choices with dietpi-software 
Feature-rich configuration tool for your device with dietpi-config.

Introduction

DietPi is an extremely lightweight Debian-based OS. It is highly optimised for minimal CPU and RAM resource usage, ensuring your SBC always runs at its maximum potential.

The dietpi programs use lightweight whiptail menus. You'll spend more time enjoying DietPi and applications you need and less time staring at the command line.

Use dietpi-software to quick and easy install Ready to Run & Optimised applications for your system. DietPi will do all the necessary configurations, including starting the services. Few highlights: Desktop Environments, Remote Desktop Access, Media Systems & Players, BitTorrent & Downloading, Cloud & Backup, Gaming & Emulation, Social & Search, Camera & Surveillance, Networking, System Stats & Management, Home Automation, Hardware & Voice Projects, Webserver Stacks, DNS Servers / Pi-hole, File Servers, Printing and much more.

Use dietpi-services to control which installed software has higher or lower priority levels (nice, affinity, policy scheduler).

dietpi-update automatically checks for updates and informs you when they are available. Update instantly, without having to write a new image. DietPi automation allows you to completely automate a DietPi installation with no user input, simply by configuring dietpi.txt before powering on.

The DietPi Project Team

The full list of code contributors can be viewed here.

Contributors

Micha

Joined Q3 2017

Project lead (20/02/2019 and onwards), source code contributor, bug fixes, software improvements, DietPi forum administrator.

Daniel Knight

Project founder and previous project lead (19/02/2019 and previous), source code contributor and tester.

JohnVick

Joined 2016-06-08

DietPi forum co-administrator, management, support, testing and valuable feedback.

sal666

Joined 2017-07-26

Creator and maintainer of the first Clonezilla based installer images for x86_64 UEFI systems.

Joulinar

Joined Q4 2019

DietPi forum moderator, support, testing, bug reports + investigation and valuable feedback.

StephanStS

Joined Q4 2019

NanoPi image creator, tester and bug reports.

Petru

Joined 2020-05-31

DietPi documentation author, product manager, SEO and DietPi visibility recommendations.

ravenclaw900

Joined 2020-10-11

Source code contributor, creator of the DietPi-Dashboard and many software implementations.

yumiris

Joined 2018-04-16

Creator and maintainer of the first DietPi Hyper-V images.

Collaborations

DietPi + Amiberry

Since 2016-09-02

Joint venture to bring you the ultimate Amiga experience on your SBC, running lightweight and optimised DietPi at its core: https://github.com/MichaIng/DietPi/issues/474

Hall of Fame

K-Plan

Joined 2016-01-01

Contributions to the DietPi in general, in-depth testing, bug finding and valuable feedback, forum moderator.

ZombieVirus

Joined 2016-03-20

DietPi forum moderator and version history maintainer on forums.

Rhkean

Joined 2018-03-01

Contributions to the DietPi in general, including source code, testing, new devices, forum moderator.

Pilovali

Joined 2015-10-10

Provided dietpi.com web hosting for 1 year until April 17th 2016. Additionally: forum moderator, testing, bug reporting.

Xenfomation

Joined 2016-04-01

Contributions to the DietPi in general, including source code and VirtualBox image creation/conversion.

AWL29

Joined 2016-10-01

Created the first DietPi image for NanoPi M3/T3.

Contributing

Git coders, please use the active development branch: dev

Are you able to:

  • Provide feedback and/or test areas of DietPi, to improve the user experience?
  • Report bugs?
  • Improve/add more features to the DietPi website or documentation?
  • Compile software for our supported SBCs?
  • Contribute to DietPi with programming on GitHub?
  • Suggest new software that we can add to the dietpi-software install system?

If so, let us know! We are always looking for talented people who believe in the DietPi project, and, wish to contribute in any way you can.

Also read our contribute page for an overview of way to support DietPi.

License

DietPi Copyright (C) 2022 Contributors

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/

Links

DietPi Source

DietPi Files

  • All files located in (recursively):
    • /var/lib/dietpi/
    • /var/tmp/dietpi/
    • /boot/dietpi/
  • /boot/dietpi.txt
  • /boot/config.txt (RPi)
  • /boot/boot.ini (Odroid)
  • All files prefixed with: dietpi-

The above GPLv2 documentation also applies to all mentioned files!

3rd Party Sources/Credits

Links to hardware and software manufacturers, sources and build instructions used in DietPi:

Website • Downloads • Documentation • Forum • Blog

Download Details:

Author: MichaIng
Source Code: https://github.com/MichaIng/DietPi 
License: GPL-2.0 license

#shell #bash #lightweight #debian #optimization #raspberrypi 

Jones Brianna

Jones Brianna

1602133570

Why You Should Design Your Own Board Game App?

https://www.mobiwebtech.com/why-you-should-design-your-own-board-game-app/

The dice, decks and game board are built with pixel art and you can design any game imaginable with your creativity. You can design a brand-new board game app or innovate your favourite game. You can design crazy games or role-playing maps with your art. You can play it online with your family and friends in your private game room in real-time.

#board game app development #board game app development company #board game app developers #board game app development services #board game app development usa #board game website development

Scraping Intelligence

Scraping Intelligence

1641805837

How to Predict Housing Prices with Linear Regression?

How-to-Predict-Housing-Prices-with-Linear-Regression

The final objective is to estimate the cost of a certain house in a Boston suburb. In 1970, the Boston Standard Metropolitan Statistical Area provided the information. To examine and modify the data, we will use several techniques such as data pre-processing and feature engineering. After that, we'll apply a statistical model like regression model to anticipate and monitor the real estate market.

Project Outline:

  • EDA
  • Feature Engineering
  • Pick and Train a Model
  • Interpret
  • Conclusion

EDA

Before using a statistical model, the EDA is a good step to go through in order to:

  • Recognize the data set
  • Check to see if any information is missing.
  • Find some outliers.
  • To get more out of the data, add, alter, or eliminate some features.

Importing the Libraries

  • Recognize the data set
  • Check to see if any information is missing.
  • Find some outliers.
  • To get more out of the data, add, alter, or eliminate some features.

# Import the libraries #Dataframe/Numerical libraries import pandas as pd import numpy as np #Data visualization import plotly.express as px import matplotlib import matplotlib.pyplot as plt import seaborn as sns #Machine learning model from sklearn.linear_model import LinearRegression

Reading the Dataset with Pandas

#Reading the data path='./housing.csv' housing_df=pd.read_csv(path,header=None,delim_whitespace=True)

 CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
00.0063218.02.3100.5386.57565.24.09001296.015.3396.904.9824.0
10.027310.07.0700.4696.42178.94.96712242.017.8396.909.1421.6
20.027290.07.0700.4697.18561.14.96712242.017.8392.834.0334.7
30.032370.02.1800.4586.99845.86.06223222.018.7394.632.9433.4
40.069050.02.1800.4587.14754.26.06223222.018.7396.905.3336.2
.............................................
5010.062630.011.9300.5736.59369.12.47861273.021.0391.999.6722.4
5020.045270.011.9300.5736.12076.72.28751273.021.0396.909.0820.6
5030.060760.011.9300.5736.97691.02.16751273.021.0396.905.6423.9
5040.109590.011.9300.5736.79489.32.38891273.021.0393.456.4822.0
5050.047410.011.9300.5736.03080.82.50501273.021.0396.907.8811.9

Have a Look at the Columns

Crime: It refers to a town's per capita crime rate.

ZN: It is the percentage of residential land allocated for 25,000 square feet.

Indus: The amount of non-retail business lands per town is referred to as the indus.

CHAS: CHAS denotes whether or not the land is surrounded by a river.

NOX: The NOX stands for nitric oxide content (part per 10m)

RM: The average number of rooms per home is referred to as RM.

AGE: The percentage of owner-occupied housing built before 1940 is referred to as AGE.

DIS: Weighted distance to five Boston employment centers are referred to as dis.

RAD: Accessibility to radial highways index

TAX: The TAX columns denote the rate of full-value property taxes per $10,000 dollars.

B: B=1000(Bk — 0.63)2 is the outcome of the equation, where Bk is the proportion of blacks in each town.

PTRATIO: It refers to the student-to-teacher ratio in each community.

LSTAT: It refers to the population's lower socioeconomic status.

MEDV: It refers to the 1000-dollar median value of owner-occupied residences.

Data Preprocessing

# Check if there is any missing values. housing_df.isna().sum() CRIM       0 ZN         0 INDUS      0 CHAS       0 NOX        0 RM         0 AGE        0 DIS        0 RAD        0 TAX        0 PTRATIO    0 B          0 LSTAT      0 MEDV       0 dtype: int64

No missing values are found

We examine our data's mean, standard deviation, and percentiles.

housing_df.describe()

Graph Data

 CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
count506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000
mean3.61352411.36363611.1367790.0691700.5546956.28463468.5749013.7950439.549407408.23715418.455534356.67403212.65306322.532806
std8.60154523.3224536.8603530.2539940.1158780.70261728.1488612.1057108.707259168.5371162.16494691.2948647.1410629.197104
min0.0063200.0000000.4600000.0000000.3850003.5610002.9000001.1296001.000000187.00000012.6000000.3200001.7300005.000000
25%0.0820450.0000005.1900000.0000000.4490005.88550045.0250002.1001754.000000279.00000017.400000375.3775006.95000017.025000
50%0.2565100.0000009.6900000.0000000.5380006.20850077.5000003.2074505.000000330.00000019.050000391.44000011.36000021.200000
75%3.67708312.50000018.1000000.0000000.6240006.62350094.0750005.18842524.000000666.00000020.200000396.22500016.95500025.000000
max88.976200100.00000027.7400001.0000000.8710008.780000100.00000012.12650024.000000711.00000022.000000396.90000037.97000050.000000

The crime, area, sector, nitric oxides, 'B' appear to have multiple outliers at first look because the minimum and maximum values are so far apart. In the Age columns, the mean and the Q2(50 percentile) do not match.

We might double-check it by examining the distribution of each column.

Inferences

  1. The rate of crime is rather low. The majority of values are in the range of 0 to 25. With a huge value and a value of zero.
  2. The majority of residential land is zoned for less than 25,000 square feet. Land zones larger than 25,000 square feet represent a small portion of the dataset.
  3. The percentage of non-retial commercial acres is mostly split between two ranges: 0-13 and 13-23.
  4. The majority of the properties are bordered by the river, although a tiny portion of the data is not.
  5. The content of nitrite dioxide has been trending lower from.3 to.7, with a little bump towards.8. It is permissible to leave a value in the range of 0.1–1.
  6. The number of rooms tends to cluster around the average.
  7. With time, the proportion of owner-occupied units rises.
  8. As the number of weights grows, the weight distance between 5 employment centers reduces. It could indicate that individuals choose to live in new high-employment areas.
  9. People choose to live in places with limited access to roadways (0-10). We have a 30th percentile outlier.
  10. The majority of dwelling taxes are in the range of $200-450, with large outliers around $700,000.
  11. The percentage of people with lower status tends to cluster around the median. The majority of persons are of lower social standing.

Because the model is overly generic, removing all outliers will underfit it. Keeping all outliers causes the model to overfit and become excessively accurate. The data's noise will be learned.

The approach is to establish a happy medium that prevents the model from becoming overly precise. When faced with a new set of data, however, they generalise well.

We'll keep numbers below 600 because there's a huge anomaly in the TAX column around 600.

new_df=housing_df[housing_df['TAX']<600]

Looking at the Distribution

Looking-at-the-Distribution

The overall distribution, particularly the TAX, PTRATIO, and RAD, has improved slightly.

Correlation

Correlation

Perfect correlation is denoted by the clear values. The medium correlation between the columns is represented by the reds, while the negative correlation is represented by the black.

With a value of 0.89, we can see that 'MEDV', which is the medium price we wish to anticipate, is substantially connected with the number of rooms 'RM'. The proportion of black people in area 'B' with a value of 0.19 is followed by the residential land 'ZN' with a value of 0.32 and the percentage of black people in area 'ZN' with a value of 0.32.

The metrics that are most connected with price will be plotted.

The-metrics-that-are-most-connected

Feature Engineering

Feature Scaling

Gradient descent is aided by feature scaling, which ensures that all features are on the same scale. It makes locating the local optimum much easier.

Mean standardization is one strategy to employ. It substitutes (target-mean) for the target to ensure that the feature has a mean of nearly zero.

def standard(X):    '''Standard makes the feature 'X' have a zero mean'''    mu=np.mean(X) #mean    std=np.std(X) #standard deviation    sta=(X-mu)/std # mean normalization    return mu,std,sta     mu,std,sta=standard(X) X=sta X

 CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
0-0.6091290.092792-1.019125-0.2809760.2586700.2791350.162095-0.167660-2.105767-0.235130-1.1368630.401318-0.933659
1-0.575698-0.598153-0.225291-0.280976-0.4237950.0492520.6482660.250975-1.496334-1.032339-0.0041750.401318-0.219350
2-0.575730-0.598153-0.225291-0.280976-0.4237951.1897080.0165990.250975-1.496334-1.032339-0.0041750.298315-1.096782
3-0.567639-0.598153-1.040806-0.280976-0.5325940.910565-0.5263500.773661-0.886900-1.3276010.4035930.343869-1.283945
4-0.509220-0.598153-1.040806-0.280976-0.5325941.132984-0.2282610.773661-0.886900-1.3276010.4035930.401318-0.873561
..........................................
501-0.519445-0.5981530.585220-0.2809760.6048480.3060040.300494-0.936773-2.105767-0.5746821.4456660.277056-0.128344
502-0.547094-0.5981530.585220-0.2809760.604848-0.4000630.570195-1.027984-2.105767-0.5746821.4456660.401318-0.229652
503-0.522423-0.5981530.585220-0.2809760.6048480.8777251.077657-1.085260-2.105767-0.5746821.4456660.401318-0.820331
504-0.444652-0.5981530.585220-0.2809760.6048480.6060461.017329-0.979587-2.105767-0.5746821.4456660.314006-0.676095
505-0.543685-0.5981530.585220-0.2809760.604848-0.5344100.715691-0.924173-2.105767-0.5746821.4456660.401318-0.435703

Choose and Train the Model

For the sake of the project, we'll apply linear regression.

Typically, we run numerous models and select the best one based on a particular criterion.

Linear regression is a sort of supervised learning model in which the response is continuous, as it relates to machine learning.

Form of Linear Regression

y= θX+θ1 or y= θ1+X1θ2 +X2θ3 + X3θ4

y is the target you will be predicting

0 is the coefficient

x is the input

We will Sklearn to develop and train the model

#Import the libraries to train the model from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression

Allow us to utilise the train/test method to learn a part of the data on one set and predict using another set using the train/test approach.

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) [7.22218258] 24.66379606613584

In this example, you will learn the model using below hypothesis:

Price= 24.85 + 7.18* Room

It is interpreted as:

For a decided price of a house:

A 7.18-unit increase in the price is connected with a growth in the number of rooms.

As a side note, this is an association, not a cause!

Interpretation

You will need a metric to determine whether our hypothesis was right. The RMSE approach will be used.

Root Means Square Error (RMSE) is defined as the square root of the mean of square error. The difference between the true and anticipated numbers called the error. It's popular because it can be expressed in y-units, which is the median price of a home in our scenario.

def rmse(predict,actual):    return np.sqrt(np.mean(np.square(predict - actual))) # Split the Data into train and test set X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) loss=rmse(predictions_test,y_test) print('loss: ',loss) print(model.score(X_test,y_test)) #accuracy [7.43327725] 24.912055881970886 loss: 3.9673165450580714 0.7552661033654667 Loss will be 3.96

This means that y-units refer to the median value of occupied homes with 1000 dollars.

This will be less by 3960 dollars.

While learning the model you will have a high variance when you divide the data. Coefficient and intercept will vary. It's because when we utilized the train/test approach, we choose a set of data at random to place in either the train or test set. As a result, our theory will change each time the dataset is divided.

This problem can be solved using a technique called cross-validation.

Improvisation in the Model

With 'Forward Selection,' we'll iterate through each parameter to assist us choose the numbers characteristics to include in our model.

Forward Selection

  1. Choose the most appropriate variable (in our case based on high correlation)
  2. Add the next best variable to the model
  3. Some predetermined conditions must meet.

We'll use a random state of 1 so that each iteration yields the same outcome.

cols=[] los=[] los_train=[] scor=[] i=0 while i < len(high_corr_var):    cols.append(high_corr_var[i])        # Select inputs variables    X=new_df[cols]        #mean normalization    mu,std,sta=standard(X)    X=sta        # Split the data into training and testing    X_train,X_test,y_train,y_test= train_test_split(X,y,random_state=1)        #fit the model to the training    lnreg=LinearRegression().fit(X_train,y_train)        #make prediction on the training test    prediction_train=lnreg.predict(X_train)        #make prediction on the testing test    prediction=lnreg.predict(X_test)        #compute the loss on train test    loss=rmse(prediction,y_test)    loss_train=rmse(prediction_train,y_train)    los_train.append(loss_train)    los.append(loss)        #compute the score    score=lnreg.score(X_test,y_test)    scor.append(score)        i+=1

We have a big 'loss' with a smaller collection of variables, yet our system will overgeneralize in this scenario. Although we have a reduced 'loss,' we have a large number of variables. However, if the model grows too precise, it may not generalize well to new data.

In order for our model to generalize well with another set of data, we might use 6 or 7 features. The characteristic chosen is descending based on how strong the price correlation is.

high_corr_var ['RM', 'ZN', 'B', 'CHAS', 'RAD', 'DIS', 'CRIM', 'NOX', 'AGE', 'TAX', 'INDUS', 'PTRATIO', 'LSTAT']

With 'RM' having a high price correlation and LSTAT having a negative price correlation.

# Create a list of features names feature_cols=['RM','ZN','B','CHAS','RAD','CRIM','DIS','NOX'] #Select inputs variables X=new_df[feature_cols] # Split the data into training and testing sets X_train,X_test,y_train,y_test= train_test_split(X,y, random_state=1) # feature engineering mu,std,sta=standard(X) X=sta # fit the model to the trainning data lnreg=LinearRegression().fit(X_train,y_train) # make prediction on the testing test prediction=lnreg.predict(X_test) # compute the loss loss=rmse(prediction,y_test) print('loss: ',loss) lnreg.score(X_test,y_test) loss: 3.212659865936143 0.8582338376696363

The test set yielded a loss of 3.21 and an accuracy of 85%.

Other factors, such as alpha, the learning rate at which our model learns, could still be tweaked to improve our model. Alternatively, return to the preprocessing section and working to increase the parameter distribution.

For more details regarding scraping real estate data you can contact Scraping Intelligence today

https://www.websitescraper.com/how-to-predict-housing-prices-with-linear-regression.php

Tyrique Littel

Tyrique Littel

1597377600

Micro Frameworks for Single Board Computers

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Photo by Harrison Broadbent on Unsplash

A microframework is essentially any framework, tool, utility, or language that is a trimmed down version of its fully-featured counterpart or designed to work with very limited resources The term “microframework” is very popular among web application development where framework with only basic features exists to keep the code footprint small but offers extensibility through modules.

Such frameworks make an even better use case for single board computers (SBC) which typically operate on very low processing speed, low memory, and even lower power. There are some single-board computers which are quite powerful too, and expensive. While some perform very specific roles, this article will focus on an overview of the most tools.

Let’s dive into the list!

SQLite

Database engines are usually heavy, but not SQLite. This library was written in C and most like is already available in your operating system and language you use. If you need to persist data in a relational style and execute queries, SQLite is your friend.

According to their documentation, SQLite is super small (like few hundred kilobytes), fast, and fully implements SQL features. The good part is, it comes in Python by default which is the go-to language for SBCs. SQLite also supports an in-memory database if you are interested.

Mosquitto MQTT

MQTT is an extremely lightweight publish-subscribe protocol designed way back in 1999 at IBM for communication over unreliable satellite networks. The protocol later gained popularity in IoT space. Mosquitto MQTT is Eclipse’s implementation of this protocol. Mosquitto facilitates real-time machine-to-machine information exchange for bandwidth-constrained devices.

In a MQTT system, a single broker can facilitate many clients to push and listen to one or more channels called topics. MQTT is not only popular among low powered devices and microcontrollers. In fact, a lot of messaging applications use this, including Facebook messenger.

Learn more about Mosquitto here and MQTT here.

Micro Python

MicroPython is a lean port of Python 3 programming language for microcontrollers. Yes, it is unlike other tools here that aim at SBCs. MicroPython is compact enough to fit and run within just 256k of code space and 16k of RAM. There are plenty of boards that support MicroPython. PyBoards and ESP family boards are the most popular among them.

MicroPython is a combination of compiler and runtime environment. On compatible boards, once MicroPython firmware is installed, you can code using the interactive shell through REPL. MicroPython bridges the gap between coding for single board computers and microcontrollers as we are using familiar syntax and libraries.

Learn more from their official website and documentation.

#iot #micro-framework #programming #technology #single-board-computer

Thurman Mills

Thurman Mills

1620874140

Cloud Computing Vs Grid Computing

The similarity between cloud computing and grid computing is uncanny. The underlying concepts that make these two inherently different are actually so similar to one and another, which is responsible for creating a lot of confusion. Both cloud and grid computing aims to provide a similar kind of services to a large user base by sharing assets among an enormous pool of clients.

Both of these technologies are obviously network-based and are capable enough to sport multitasking. The availability of multitasking allows the users of either of the two services to use multiple applications at the same time. You are also not limited to the kind of applications that you can use. You are free to choose any number of applications that can accomplish any tasks that you want. Learn more about cloud computing applications.

#cloud computing #cloud computing vs grid computing #grid computing #cloud