1623724552
Paying Attention To Green Computing Is Worthwhile For Environmental Concerns
In this modern age of technological innovations, the requirement to go green is stronger than ever. With the emergence of cloud computing, the internet is becoming a great contributor to carbon emissions due to the utilization of dirty energy. Revolutionary IT companies and search giants have taken this issue under consideration and are redirecting a massive number of their data centers onto renewable energy sources. Be it Facebook, Amazon, Google, or Microsoft, organizations are progressing by leaps and bounds to become as environmentally friendly as possible by setting their ecological company goals.
The latest evolution in cloud computing assists companies to become familiar with the fact that they are attaining more flexibility and scalability by switching to the public cloud. According to the Environmental protection agency, data centers now encounter 1.5% of overall electricity utilization in the United States. Moreover, by the year 2020 carbon emissions have four-footed to 680 million tons per year. Consequently, carbon exhalation and electricity consumption can be minimized by redirecting all of the IT operations to a public cloud provider, known as green computing in layman’s terms.
How Could Computing Be Enhancing Environmental sustainability?
There is no surprise in saying that cloud computing is sustainable yet it comes with a catch. Cloud computing provides a wide range of benefits such as security, mobility, scalability but the highly valuable advantage is its potential to foster environmental friendliness in regular business operations. Let us have an overview of some major benefits that organizations can cherish by acquiring cloud computing.
Prevent 1 Billion Emissions of CO2
It is predicted by International Data Corporation (IDC) that from 2021 to 2024 the continuous acquisition of cloud computing helps to intercept the emission of more than 1 billion metric tons of carbon dioxide (CO2). The prediction utilizes IDC data on server distribution, cloud, and on-premises software utilization in addition to third-party information on data center power utilization, emission analogy of cloud as well as non-cloud data centers, and CO2 emission per kilowatt-hour.
1
Increases The Utilization of Renewable Energy Sources
Cloud data centers mainly rely upon renewable energy resources and making them environmentally friendly. Solar powers and winds are substituting fossil fuel-based electricity. The utilization of green energy instead of fossil fuels is becoming the most common sustainability strategy. For instance, Agile IT and Arcadia Power, partnered so that their headquarters and data centers currently operate under wind power. Thanks to the leveraging technology due to which cloud centers are running in a more energy-efficient manner as compared to physical data centers.
**Decreases Automobile Emission **
The way cloud computing facilitates remote work, which decreases commutes which in return decreases automobile emission is effortless. Saving the utilization of gasoline and auto emissions also results in less air pollution, providing it an additional environmental advantage that is integrated into our society. Remote employees assist companies to work more accurately and efficiently. A minimal number of employees means minimal utilization of resources, also utilizing less heat and energy.
Cloud Providers Are Going Green
The global environmental ecosystem is more secure and stronger because of cloud computing. Let us have a deep analysis of this enthusiastic fact that how the world’s most popular cloud providers are acquiring green computing to leave a positive impact on the environment.
**Google Cloud **
Recently Google Cloud announced that by the year 2030, it is projecting its goal to run its business on carbon-free energy 24/7 all across the globe. Google Cloud encounters how much carbon it has consumed every year. In 2007, Google successfully achieved carbon neutrality. This search giant has purchased enough solar and wind energy to tone with 100% of its global electricity consumption since 2017. The next step in resulting in a carbon-free future and providing its customer with the most enhanced form of cloud computing services is completely decarbonizing its data center electricity supply chain.
Each Google Cloud region will be supplied with the emergence of more and more carbon-free energy and very minimal fossil-based energy, the company is continuously striving to achieve new and innovative sustainability goals.
#environmental-technology #green-energy #green-computing #google-cloud-services #amazon-web-services
What is GEEK
Buddha Community
1623724552
Paying Attention To Green Computing Is Worthwhile For Environmental Concerns
In this modern age of technological innovations, the requirement to go green is stronger than ever. With the emergence of cloud computing, the internet is becoming a great contributor to carbon emissions due to the utilization of dirty energy. Revolutionary IT companies and search giants have taken this issue under consideration and are redirecting a massive number of their data centers onto renewable energy sources. Be it Facebook, Amazon, Google, or Microsoft, organizations are progressing by leaps and bounds to become as environmentally friendly as possible by setting their ecological company goals.
The latest evolution in cloud computing assists companies to become familiar with the fact that they are attaining more flexibility and scalability by switching to the public cloud. According to the Environmental protection agency, data centers now encounter 1.5% of overall electricity utilization in the United States. Moreover, by the year 2020 carbon emissions have four-footed to 680 million tons per year. Consequently, carbon exhalation and electricity consumption can be minimized by redirecting all of the IT operations to a public cloud provider, known as green computing in layman’s terms.
How Could Computing Be Enhancing Environmental sustainability?
There is no surprise in saying that cloud computing is sustainable yet it comes with a catch. Cloud computing provides a wide range of benefits such as security, mobility, scalability but the highly valuable advantage is its potential to foster environmental friendliness in regular business operations. Let us have an overview of some major benefits that organizations can cherish by acquiring cloud computing.
Prevent 1 Billion Emissions of CO2
It is predicted by International Data Corporation (IDC) that from 2021 to 2024 the continuous acquisition of cloud computing helps to intercept the emission of more than 1 billion metric tons of carbon dioxide (CO2). The prediction utilizes IDC data on server distribution, cloud, and on-premises software utilization in addition to third-party information on data center power utilization, emission analogy of cloud as well as non-cloud data centers, and CO2 emission per kilowatt-hour.
1
Increases The Utilization of Renewable Energy Sources
Cloud data centers mainly rely upon renewable energy resources and making them environmentally friendly. Solar powers and winds are substituting fossil fuel-based electricity. The utilization of green energy instead of fossil fuels is becoming the most common sustainability strategy. For instance, Agile IT and Arcadia Power, partnered so that their headquarters and data centers currently operate under wind power. Thanks to the leveraging technology due to which cloud centers are running in a more energy-efficient manner as compared to physical data centers.
**Decreases Automobile Emission **
The way cloud computing facilitates remote work, which decreases commutes which in return decreases automobile emission is effortless. Saving the utilization of gasoline and auto emissions also results in less air pollution, providing it an additional environmental advantage that is integrated into our society. Remote employees assist companies to work more accurately and efficiently. A minimal number of employees means minimal utilization of resources, also utilizing less heat and energy.
Cloud Providers Are Going Green
The global environmental ecosystem is more secure and stronger because of cloud computing. Let us have a deep analysis of this enthusiastic fact that how the world’s most popular cloud providers are acquiring green computing to leave a positive impact on the environment.
**Google Cloud **
Recently Google Cloud announced that by the year 2030, it is projecting its goal to run its business on carbon-free energy 24/7 all across the globe. Google Cloud encounters how much carbon it has consumed every year. In 2007, Google successfully achieved carbon neutrality. This search giant has purchased enough solar and wind energy to tone with 100% of its global electricity consumption since 2017. The next step in resulting in a carbon-free future and providing its customer with the most enhanced form of cloud computing services is completely decarbonizing its data center electricity supply chain.
Each Google Cloud region will be supplied with the emergence of more and more carbon-free energy and very minimal fossil-based energy, the company is continuously striving to achieve new and innovative sustainability goals.
#environmental-technology #green-energy #green-computing #google-cloud-services #amazon-web-services
1612355729
Amazon Pay Clone, Amazon Pay Clone Script, Recharge & Wallet App Solution
Mobile wallet applications have become the new trend in today’s world. Apps like Amazon Pay, Paytm, PayPal are some of the leading apps that are owned and used by millions. Be it paying bills, recharging, or money transactions, everything has turned easier because of these apps. There were days when people used to travel for hours to do these tasks have been totally transformed. Moreover, consumers can use these e-wallet apps while paying in a store, either for shopping or while eating out. Thus, as far as mobile wallets are concerned, they are a convenient way for handling all the tasks involving finance.
As an aspiring entrepreneur, if you wish to succeed in your business, without second thoughts, go for Amazon Pay clone app development. Let’s narrow down your thinking processes for a quicker stride forward by analyzing the types of apps first.
Types of e-wallet apps you could develop:
Retail application: An app like Amazon is considered the retail app because it has a mobile wallet in it. It has all the basic functionalities, which helps users to redeem coupons and reward points. All the payment modes are accessible through the app, including net banking.
Dedicated app: The app allows P2P money transactions by storing a variety of cards. You could also make international money transfers using this app. Example: PayPal, Apple Pay, and Amazon Pay.
PoS payments: The PoS payment wallet apps are found at the stores. It is exclusively used by the users to make contactless payments without having to stand in a long queue.
Wrapping up,
Choose the best type of e-wallet app you want to develop and join forces with our Appdupe. Grab the cutting-edge Amazon Pay Clone script and launch an app in a week!
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1641805837
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)
| CRIM | ZN | INDUS | CHAS | NOX | RM | AGE | DIS | RAD | TAX | PTRATIO | B | LSTAT | MEDV | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.00632 | 18.0 | 2.31 | 0 | 0.538 | 6.575 | 65.2 | 4.0900 | 1 | 296.0 | 15.3 | 396.90 | 4.98 | 24.0 |
| 1 | 0.02731 | 0.0 | 7.07 | 0 | 0.469 | 6.421 | 78.9 | 4.9671 | 2 | 242.0 | 17.8 | 396.90 | 9.14 | 21.6 |
| 2 | 0.02729 | 0.0 | 7.07 | 0 | 0.469 | 7.185 | 61.1 | 4.9671 | 2 | 242.0 | 17.8 | 392.83 | 4.03 | 34.7 |
| 3 | 0.03237 | 0.0 | 2.18 | 0 | 0.458 | 6.998 | 45.8 | 6.0622 | 3 | 222.0 | 18.7 | 394.63 | 2.94 | 33.4 |
| 4 | 0.06905 | 0.0 | 2.18 | 0 | 0.458 | 7.147 | 54.2 | 6.0622 | 3 | 222.0 | 18.7 | 396.90 | 5.33 | 36.2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 501 | 0.06263 | 0.0 | 11.93 | 0 | 0.573 | 6.593 | 69.1 | 2.4786 | 1 | 273.0 | 21.0 | 391.99 | 9.67 | 22.4 |
| 502 | 0.04527 | 0.0 | 11.93 | 0 | 0.573 | 6.120 | 76.7 | 2.2875 | 1 | 273.0 | 21.0 | 396.90 | 9.08 | 20.6 |
| 503 | 0.06076 | 0.0 | 11.93 | 0 | 0.573 | 6.976 | 91.0 | 2.1675 | 1 | 273.0 | 21.0 | 396.90 | 5.64 | 23.9 |
| 504 | 0.10959 | 0.0 | 11.93 | 0 | 0.573 | 6.794 | 89.3 | 2.3889 | 1 | 273.0 | 21.0 | 393.45 | 6.48 | 22.0 |
| 505 | 0.04741 | 0.0 | 11.93 | 0 | 0.573 | 6.030 | 80.8 | 2.5050 | 1 | 273.0 | 21.0 | 396.90 | 7.88 | 11.9 |
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()
| CRIM | ZN | INDUS | CHAS | NOX | RM | AGE | DIS | RAD | TAX | PTRATIO | B | LSTAT | MEDV | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 |
| mean | 3.613524 | 11.363636 | 11.136779 | 0.069170 | 0.554695 | 6.284634 | 68.574901 | 3.795043 | 9.549407 | 408.237154 | 18.455534 | 356.674032 | 12.653063 | 22.532806 |
| std | 8.601545 | 23.322453 | 6.860353 | 0.253994 | 0.115878 | 0.702617 | 28.148861 | 2.105710 | 8.707259 | 168.537116 | 2.164946 | 91.294864 | 7.141062 | 9.197104 |
| min | 0.006320 | 0.000000 | 0.460000 | 0.000000 | 0.385000 | 3.561000 | 2.900000 | 1.129600 | 1.000000 | 187.000000 | 12.600000 | 0.320000 | 1.730000 | 5.000000 |
| 25% | 0.082045 | 0.000000 | 5.190000 | 0.000000 | 0.449000 | 5.885500 | 45.025000 | 2.100175 | 4.000000 | 279.000000 | 17.400000 | 375.377500 | 6.950000 | 17.025000 |
| 50% | 0.256510 | 0.000000 | 9.690000 | 0.000000 | 0.538000 | 6.208500 | 77.500000 | 3.207450 | 5.000000 | 330.000000 | 19.050000 | 391.440000 | 11.360000 | 21.200000 |
| 75% | 3.677083 | 12.500000 | 18.100000 | 0.000000 | 0.624000 | 6.623500 | 94.075000 | 5.188425 | 24.000000 | 666.000000 | 20.200000 | 396.225000 | 16.955000 | 25.000000 |
| max | 88.976200 | 100.000000 | 27.740000 | 1.000000 | 0.871000 | 8.780000 | 100.000000 | 12.126500 | 24.000000 | 711.000000 | 22.000000 | 396.900000 | 37.970000 | 50.000000 |
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
- 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.
- 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.
- The percentage of non-retial commercial acres is mostly split between two ranges: 0-13 and 13-23.
- The majority of the properties are bordered by the river, although a tiny portion of the data is not.
- 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.
- The number of rooms tends to cluster around the average.
- With time, the proportion of owner-occupied units rises.
- 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.
- People choose to live in places with limited access to roadways (0-10). We have a 30th percentile outlier.
- The majority of dwelling taxes are in the range of $200-450, with large outliers around $700,000.
- 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
The overall distribution, particularly the TAX, PTRATIO, and RAD, has improved slightly.
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.
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
| CRIM | ZN | INDUS | CHAS | NOX | RM | AGE | DIS | RAD | TAX | PTRATIO | B | LSTAT | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.609129 | 0.092792 | -1.019125 | -0.280976 | 0.258670 | 0.279135 | 0.162095 | -0.167660 | -2.105767 | -0.235130 | -1.136863 | 0.401318 | -0.933659 |
| 1 | -0.575698 | -0.598153 | -0.225291 | -0.280976 | -0.423795 | 0.049252 | 0.648266 | 0.250975 | -1.496334 | -1.032339 | -0.004175 | 0.401318 | -0.219350 |
| 2 | -0.575730 | -0.598153 | -0.225291 | -0.280976 | -0.423795 | 1.189708 | 0.016599 | 0.250975 | -1.496334 | -1.032339 | -0.004175 | 0.298315 | -1.096782 |
| 3 | -0.567639 | -0.598153 | -1.040806 | -0.280976 | -0.532594 | 0.910565 | -0.526350 | 0.773661 | -0.886900 | -1.327601 | 0.403593 | 0.343869 | -1.283945 |
| 4 | -0.509220 | -0.598153 | -1.040806 | -0.280976 | -0.532594 | 1.132984 | -0.228261 | 0.773661 | -0.886900 | -1.327601 | 0.403593 | 0.401318 | -0.873561 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 501 | -0.519445 | -0.598153 | 0.585220 | -0.280976 | 0.604848 | 0.306004 | 0.300494 | -0.936773 | -2.105767 | -0.574682 | 1.445666 | 0.277056 | -0.128344 |
| 502 | -0.547094 | -0.598153 | 0.585220 | -0.280976 | 0.604848 | -0.400063 | 0.570195 | -1.027984 | -2.105767 | -0.574682 | 1.445666 | 0.401318 | -0.229652 |
| 503 | -0.522423 | -0.598153 | 0.585220 | -0.280976 | 0.604848 | 0.877725 | 1.077657 | -1.085260 | -2.105767 | -0.574682 | 1.445666 | 0.401318 | -0.820331 |
| 504 | -0.444652 | -0.598153 | 0.585220 | -0.280976 | 0.604848 | 0.606046 | 1.017329 | -0.979587 | -2.105767 | -0.574682 | 1.445666 | 0.314006 | -0.676095 |
| 505 | -0.543685 | -0.598153 | 0.585220 | -0.280976 | 0.604848 | -0.534410 | 0.715691 | -0.924173 | -2.105767 | -0.574682 | 1.445666 | 0.401318 | -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
- Choose the most appropriate variable (in our case based on high correlation)
- Add the next best variable to the model
- 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
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