fabiochiu/medium-articles huggingface dataset:
Linear Regression in Python
Tags: Linear Regression, Accuracy, Regression, Python, Sklearn

Linear Regression in Python

What is regression in machine learning?

Regression is a subfield of supervised machine learning. It aims from model to predict the continuous target variable/ dependent variable (Y). Example : salary, price, temperature etc.

What is linear regression ?

Linear regression is a type of supervised learning algorithm, commonly used for predictive analysis .It is widely used in biological, behavioral and social sciences to describe possible relationships between a dependent variable/ target variable(Y) and one or more independent variable/ feature variables(X) using a best fit straight line (also known as regression line).

Types of linear regression

There are two types of linear regression:

Simple linear regression- It is the simplest case where we have only one dependent variable(Y) and one independent variable(X).

Equation Y = b0 + b1* X

2. Multi linear regression-It is a generalization of simple linear regression to the case of more than one independent variables(X).

Equation Y = b0 + b1*X1 + b2*X2 + b3*X3...

Without data you’re just another person with an opinion.

Now we will learn how to build simple linear regression model using data. You can download the data from here.

Workflow of linear regression model

Import all necessary library

Importing python’s library

2. Now we are going to read the data using pandas read_csv function.(use pandas.read_json to read json data)

Reading data using pandas

3. Now we are going to check null values from our data.

Null values

Their is a null value in y column(target variable) .So we are going to replace null values with the medium of y columns data. fillna function is used here to replace null value with median .

4. Now we are going to divide our train and test dataset in ratio of 70:30 , using sklearn library import train_test_split.

Here X is the independent variable/feature variable and Y is the dependent variable/target variable

Model Selection

5. Predict the values of test data.

Accuracy

Predicting the test data using the regression model .

The equation made here is a simple linear equation (Y = b0 + b1* X , b0 is intercept and b1 is the slope/coefficient of the equation.)

Accuracy of model is 99% .

U can find the code link here.

Thank you for the reading..

This is my first blog .I would love to have your feedbacks.
If the above mentioned question popped up in your mind ever, this blog might help to answer this question. Mathematically, ANOVA might be considered as a special case of linear regression analysis in…
By: Lokesh Sharma
Tags: Data Science, Regression, Statistics, Data, Machine Learning

Ever wondered why Regression and ANOVA are studied separately while mathematical construct of both looks similar?

If the above mentioned question popped up in your mind ever, this blog might help to answer this question. Mathematically, ANOVA might be considered as a special case of linear regression analysis in which all explanatory variables are categorical.

From the application point of view, there is a lot of difference between ANOVA and Regression. ANOVA is oftenly used to compare the dependent variable outcome against various categories of explanatory variables. Example - comparing sales of a product at different price categories. On the other hand, regression is used to predict the dependent variable with the help of explanatory variables. Example - predicting sales with the help of explanatory variables like price of the good, price of substitutory good, taste and preference of the consumer etcetera.
Hands-on practice on machine learning
Tags: Classification, Data Preprocessing, Regression, Scikit Learn, Machine Learning

Part 2.Supervised Learning

Supervised learning is the learning of the model with an input variable and an output variable and algorithm map the input to the output.

Supervised learning classified into two categories of algorithms:

Classification : A classification problem is when the output variable is a category, such as “disease” or “No disease”.

: A classification problem is when the output variable is a category, such as “disease” or “No disease”. Regression: A regression problem is when the output variable is a real value, such as “Price”.

2.1 Classification

There is a wide variety of classification applications from Healthcare to Marketing.

Learn how to implement the following classification models:

2.2 Regression

The regression technique varies from Linear Regression to Random Forest.
Zomato, Restaurant Analysis
By: Sucheta Maitra
Tags: Zomato, Regression

Introduction

Bangalore(officially known as Bengaluru) is the capital and largest city of the Indian state of Karnataka.

With a population of over 15 million, Bangalore is the third largest city in India and 27th largest city in the world.

Bangalore has a unique food culture. Restaurants from all over the world can be found here, with various kind of cuisines.

Some might even say that Bangalore is the best place for foodies.

The food industry is always at a rise in Bangalore, with 12,000 plus restaurants currently active in the city, the number is still increasing.

The growing number of restaurants and dishes in Bangalore is what attracts me to inspect the data to get some insights, some interesting facts and figures.

So, in this article I will be analyzing the Zomato restaurant data for the city, Bangalore.

Breakdown of this notebook:

Loading the dataset: Load the data and import the libraries. Data Cleaning:

Deleting redundant columns.

Renaming the columns.

Dropping duplicates.

Cleaning individual columns.

Remove the NaN values from the dataset

Some Transformations

3. Regression Analysis

Linear Regression

Decision Tree Regression

Random Forest Regression

4. Data Visualization: Using plots to find relations between the features.

Restaurants delivering Online or not

Restaurants allowing table booking or not

Table booking Rate vs Rate

Best Location

Relation between Location and Rating

Restaurant Type

Gaussian Rest type and Rating

Types of Services

Relation between Type and Rating

Cost of Restuarant

No. of restaurants in a Location

Restaurant type

Most famous restaurant chains in Bengaluru

Loading the Dataset

Reading the dataset

Removing the Duplicates

Remove the NaN values from the dataset

Changing the Columns Names

Some Transformations

Removing ‘/5’ from Rates

Adjust the column names

Encode the input Variables

Get Correlation between different variables

Defining the independent variables and dependent variables

Regression Analysis

Data Visualization

Restaurants delivering Online or not

Location

Location and Rating

Restaurant Type

Gaussian Rest type and Rating

Cost of Restaurant

No. of restaurants in a Location

Restaurant Type

Most famous restaurant chains in Bengaluru
MACHINE LEARNING MODELS AND BABIES: WHAT IS THE CONNECTION?
By: Abhigya Verma
Tags: Ml Model, Regression, Machine Learning

Machine Learning is one of the most advancing technical fields that is grabbing a lot of attention everywhere. From college going students, to research scholars, from small start-ups to big tech giants, all want to bring their best foot forward and make the best models and bring out the best features using ML.

More and more students and researchers are now trying to learn about Machine Learning but it is really important that one should get correct and precise information. Many people give really difficult meanings and definitions of what a machine learning model exactly is and how it works. The bookish definitions are definitely correct but is this correct easy to understand and explain? My answer is: NO.

When someone asks me what an ML model is, I simply say it’s a human child. Did you get it? No, I’ll explain.

Just like a small human child observes the people around and slowly learns from them. Along with this, there are some functions called reflexes that have already fed information in a human mind as to how to react in a situation of panic. In a very similar way, a machine learning model has some inbuilt functions and then it learns the other things from the information that is fed inside.

Let’s understand using one of the simplest ML models — linear regression. Linear regression attempts to model the relationship between two variables by setting a linear equation to the observed data.

This model uses a given set of information to get trained on it and then tries to implement the gathered information and its patterns on test data.

Taking the example in the image, the data containing images of various vehicles with a dependent value that tells if it is a car or not is given to the regression model. The model analyses this given data and then tries to give output on other images telling if they represent a car or not.

I hope that after today even you will say that an ML model is just like a baby. Hence, now you know the connections.

IMAGE REFERENCE:

https://www.futurithmic.com/2019/04/12/why-you-should-study-ai-machine-learning-how-did-it/

https://semiengineering.com/deep-learning-spreads/
Fitting functions with a configurable XGBoost regressor
By: Ettore Messina
Tags: Xgboost, Regression, Fitting

Published on https://computationalmindset.com/en/ the page “Fitting functions with a configurable XGBoost regressor”.

The page deals with the approximation of scalar mathematical functions to one or more real variables using a #xgboost regressor without writing code but only acting on the command line of Python scripts; the goal is to demonstrate that regression with XGBoost achieve low error values with extremely short learning times.

Here the url to the page:

https://computationalmindset.com/en/machine-learning/fitting-with-configurable-xgboost.html

Here is the url to the source code on GitHub:

https://github.com/ettoremessina/function-fitting/tree/master/xgboost/
LightGBM for Quantile Regression
By: Jeremy Zhang
Tags: Machine Learning, Regression, Lightgbm

LightGBM for Quantile Regression

For regression prediction tasks, not all time that we pursue only an absolute accurate prediction, and in fact, our prediction is always inaccurate, so instead of looking for an absolute precision, some times a prediction interval is required, in which cases we need quantile regression — that we predict an interval estimation of our target.

Loss Function

Fortunately, the powerful lightGBM has made quantile prediction possible and the major difference of quantile regression against general regression lies in the loss function, which is called pinball loss or quantile loss. There is a good explanation of pinball loss here, it has the formula:

Where y is the actual value, z is the prediction and 𝛕 is the targeted quantile. So the first sight of the loss function, we can see that besides when quantile equals to 0.5, the loss function is unsymmetrical. Let’s have a visual on it:

The implementation can be found on my Git Repo. In the graph, three different quantiles were plotted, take quantile 0.8 as an example, when the error is positive ( z > y — predicted value is higher than the actual value), the loss is less than that when error is negative. In another world, higher error is less punished, this makes sense in that for high quantile prediction, the loss function encourages higher prediction value, and vice versa for low quantile prediction.

Generate Sample Dataset

Now let’s generate some data for lightGBM prediction.

Here we use a sin(x) function with some additional noise as training set.

LightGBM Prediction

Initiate LGMRegressor :

Notice that different from general regression, the objective and metric are both quantile , and alpha is the quantile we need to predict ( details can check my Repo ).

Prediction Visualisation

Now let’s check out quantile prediction result:

We can see that most noisy dots are located in the prediction range, where the green line is the upper bound of 0.9 quantile and blue is the 0.1 quantile.

This post is originally inspired by this, which is a great entry point quantile regression starter.
Mathematical Notations for Machine Learning (Markdown)
By: Krishna K
Tags: Markdown, Regression, Mathematics, Machine Learning

Learning to write Mathematical notations is critical, when you are taking a note in your Machine Learning classes or building a custom ML algorithm.

Advantage of Markdown approach: you may use any IDE to write Markdown.

This article is focused on how to write mathematical notations for ML. So, I won’t be covering details of each algorithm. you will find most commonly used notations and how to write them below

Simple Linear Regression:

Using one variable to predict a value of other variable

$\hat{y}$ = $\theta_0 + \theta_1 x_1$

output

Mean Squared Error:

Mean of all Residual Errors shows how poorly the line fits with the data set

MSE = $\frac{1}{n} \Sigma_{i=1}^n({y}-\hat{y})^2$

output

Logistic Regression & Thresholds:

$\Theta^T$ X = $\theta_0$ + $\theta_1$ * $$x_1$$ Threshold: (Step function)

- $\hat{y}$ = 0 if $\Theta^T$ X < 0.5

- $\hat{y}$ = 1 if $\Theta^T$ X $\ge$ 0.5

output

Sigmoid function:

$\Sigma(\Theta^TX)$ = $$\frac{1} {1 + e^-\Theta^Tx }$$

$\hat{y}$ = $\Sigma(\Theta^T X)$

output

Cost function:

$Cost(\hat{y}$, y) = $\frac{1}{2}(\Sigma(\theta^TX)-y)^2$

output

Mean squared error for cost function:

J($\theta$) = $\frac{1}{m} \Sigma_{i=1}^{m} Cost(\hat{y}, y)$

output

Logistic Regression Cost function:

$J(\Theta)$ = -$\frac{1}{m} \Sigma_{i=1}^my^ilog(\hat{y}^i) + (1-y^i)log(1-\hat{y}^i)$

output

Finally, once you get used to writing mathematical notation for markdown it is much fun to work on Machine Learning algorithms

Happy Coding

For further learning — please checkout resources below:
A Simple FOREX Model
By: Christensen Daniel
Tags: Regression, Forex, Stock Market, Currency, Modeling

TDLR: There is an arbitrage opportunity between XAU, GBP, and USD.

I used the Bank of England’s currency exchange data from 2015 to create a model between the price of gold (XAU) in GBP (XAU GBP) and XAU in USD (XAU USD). Graphing both, we can see that it most likely has a linear relationship.

With XAU USD as the regressor against XAU GBP, the model returned an R² of 88.53% with σ² = 185.05. In a quick and rough summary, we see the exchange rate should be β_1, which is .8853, the exchange rate of USD GBP.

To improve the model, we can take the GBP USD price and add it as a regressor. We get an R² of .9990 and σ² = 1.61. This is a near-perfect model. When graphing the residuals plot, we see hardly any outliers and scattered data points scattered without clustering.

This model was frustrating to me on the surface since there is an economic theory saying that a currency will have the same value anywhere — parity. A way to think of this is if two pawn shops are next to each other if one offers widgets for sale at $1 and the one next door will but them at $2, then it would be a good idea to but as much, sell next door, and keep up the process until the prices are equal. The same applies to the currency market. Since the model is not perfect, we see a minimal opportunity for arbitrage; however, since it is not equal, and the model’s R² is not a perfect 1.000, money can be made in thoery.

At the time this is written I do not have any financial postions. I am not offering this article as investment advice. I am not a fincial advisor. There is a risk involved. Every investment has risk that I do not assume for you. It should not be assumed that the information provided will be profitable or that it will not result in losses. Previous information shared is not an indicative of future returns. You are responsible for making your own investment research and decisions.
R-Squared Vs Adjusted R-Squared
Tags: Python, Regression, Artificial Intelligence, Data Science, Machine Learning

While checking the performance of regression models, the fundamental methods are r-squared and adjusted r-squared.

This question is ubiquitous in data scientist interviews too. The adjusted r-squared has an added advantage over the r-squared.

So learn the key differences between the r-squared and adjusted r-squared with advertising vs sales growth case study.

Read more…!
Machine Learning Applications
By: Raj Upadhyay
Tags: Recommendation System, Classification, Regression, Clustering, Machine Learning

Recommendation Classification Regression Clustering Anomaly Detection

Let’s talk about each one

Recommendation

Recommender systems are the systems that are designed to recommend things to the utilizer predicated on many different factors. These systems presage the most likely product that the users are most liable to purchase and are of interest to. Companies like Netflix, Amazon, etc. utilize recommender systems to avail their users to identify the correct product or movies for them.

In simple words, we can describe a recommendation system as

“Give your users the items they may be most interested in.”

For example, Netflix’s recommendation system to suggest what to watch based on your preferences, genre watch time, ratings, location, etc.

Classification

Used in Supervised Learning algorithms.

Classification is a process of categorizing a given set of data into classes.

A classification model attempts to draw some conclusions from observed values. Given one or more inputs a classification model will try to predict the value of one or more outcomes.

Classification assigns its input to a predefined class.

In simple words, we can describe Classification as:

Determine which category does an item belongs to

Example: filtering emails “spam” or “not spam”

Regression

Regression is used when we want to predict real or continuous values as output.

Regression analysis helps us to understand how the value of the dependent variable is changing corresponding to an independent variable when other independent variables are held fixed.

It predicts continuous/real values such as temperature, age, salary, price, etc

Example: Zillow’s algorithm determines the price of a house giving its characteristics, location, surrounding houses, etc.

Clustering

Used in Unsupervised Learning algorithms.

It is basically a type of unsupervised learning method.

Clustering is a Machine Learning technique that involves the grouping of data points.

It can be defined as “A way of grouping the data points into different clusters, consisting of similar data points. The objects with the possible similarities remain in a group that has less or no similarities with another group.”

Put similar items together
Evaluation metrics for regression techniques
By: Ritik Kesharwani
Tags: Evaluation Metric, Regression, Machine Learning

To be able to choose between different evaluation metrics available out there let’s take a look at “What regression is?”

Regression is a statistical method used to predict a dependent variable based on some other variables which are correlated with our predicting variable.

Let’s take a classic example to understand it in a better way,

Suppose we want to predict the prices of the house based on the available variable which is square feet of the house. We can use regression to predict the prices where our dependent variable is the ‘price’ and the independent variable is ‘square feet’, prediction of the prices can be done using Linear Regression.

Above Fig depicts the formulae, which is used to predict our dependent variable, to predict our dependent variable(y) using the independent variable(x) we need to calculate the Gradient of the line(m) and the y-intercept(c).

In this way, we can use Linear Regression to predict the house price based on the square feet of the house.

Now, we’ve understood “what Regression is?” and also predicted the house prices using it but how can we decide that the predicted prices are correct?

This is the time when evaluation metrics comes into play.

As the name suggests, Evaluation Metrics are used to check whether our predicted prices are correct or not in simple words it is used to validate our findings.

So, there are 3 frequently used metrics which are explained below,

Mean Absolute Error(MAE): The mean absolute error, it calculates the absolute difference between our actual and predicted values.

Good predictions will have lower MAE value since the difference between the actual and predicted values will be less.

2. Root Mean Squared Error(RMSE): The root means squared error gives the average of squared differences between actual and predicted values and finally, the square root is taken.

RMSE is most useful when large errors are particularly undesirable.

3. R-Squared Error: R-squared is the proportion of the variance in the dependent variable that is predictable from the independent variable.

The R-Squared value should not be too small or too large.

Where,

n= No. of training examples

yi= Actual Values

yhati= Predicted values

ybari= Average of values

SSRES= Squared difference of actual and predicted values

SSTOT= Squared difference of actual and average values

NOTE: For a model to be good it should have lower MAE, RMSE and higher R-Squared values.

As they say “A picture is worth a thousand words” So, let’s understand the errors using the below picture.

MAE is the sum of absolute vertical distances of the observed data(yact) and Regression line(ypred) for all the values in training data. RMSE is the sum of root mean squares of the vertical distances between yact and ypred. SSRES is the sum of the squares of the vertical distances of the yact from the Regression line. SSTOT is the sum of the squares of the vertical distances of the yact from the Mean line.

Now, you are fully equipped with regression and evaluation metrics.

Hope this will help.
Some Regression Techniques with Outliers
By: Sandip Dutta
Tags: Regression, Machie Learning, Statistics, Data Science, Outliers

Recall that in a linear regression problem we have an array of y values(values to be predicted) and corresponding x values(values which we will use to predict y). The equation becomes :

y_i = m * x_i + b + ε(Error term) # y_i - true values to be predicted

# m - slope of the regression line

# x_i - the value which will help us predict y_i

# b - intercept of the regression line

# ε - Error term / Random Noise associated with y_i

This data has a linear trend. However, we can see the presence of outliers (Data that one considers to be ‘extreme’ or very erratic). These data usually occur due to several errors.

Linear models are very sensitive to outliers and few outliers can skew our predictions heavily.

How can we deal with such kinds of data (instead of filtering them, which we can do as an option)?

Huber Regression

One method that might come to mind is : We use Mean Squared Error as loss function. What if we used a different loss? Something which penalises values close to the true value heavily, and penalises outliers less. This will ensure that our line does not sway with outliers.

Huber Regression implements this very idea. This regression method minimises the so called Huber Loss:

Huber Loss Function, source below

This penalises values closer to true value , while for values far away, it is linear.

This method is already implemented in sklearn package in python. Below is the code.
Image Classification On Fashion MNIST dataset using Logistic Regression
By: Techtangent Community
Tags: Machine Learning, Classification, Regression, Data Science, Logistics

Logistic Regression

Logistic regression is a statistical method for predicting binary classes. It is an extension of the linear regression for the classification problem approaches.It is named logistic because the function used in the logistic regression is logistic function also known as sigmoid function. This function is developed by statisticians to describe properties of population growth in ecology, rising quickly.

Difference between Linear Regression and Logistic regression

Linear regression model can work for regression but fails for classification. A linear model does not output probabilities, but it treats the classes are numbers (0 and 1) and fits the best hyperplane that minimizes the distances between with this approach. Linear regression is best for predicting the value on the scale 0–100. Linear regression predictions are continuous while in Logistic regression helps in prediction of the data that is in binary form.

Advantages of Logistic regression:

1. It is easier to implement.

2. It is very fast in classifying unknown records.

3. It can overfit in high dimensional datasets then we can use regularization technique to avoid overfitting.

4. It has good accuracy and performs well when the data is linearly separable.

Disadvantages of logistic regression:

1. It does work well when data is correlated.

2. Complex relationships cannot be obtained by logistic regression

3. Nonlinear problems cannot be solved by it.S

Types of logistic regression:

1) Binary Logistic regression : The data having two types of possible output example 0,1.

2) Multinomial logistic regression Having more than 2 categories of output.

3) Ordinal Logistic regression Having more than 2 categories but in ordering like movie rating on the scale of 1 to 10.
House price prediction using PyCaret
By: Harsh Bathia
Tags: Classification, Regression, Artificial Intelligence, Pycaret, Machine Learning

💻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')

📊EDA using Pandas profiling report

The pandas_profiling library in Python includes a method named ProfileReport() which generates a basic report on the input DataFrame.

The report consist of the following:

DataFrame overview,

Each attribute on which DataFrame is defined,

Correlations between attributes (Pearson Correlation and Spearman Correlation), and

A sample of DataFrame.

This single line of code will give you a detailed report of the dataset
Linear Regression, the basic Supervised Learning Algorithm
By: Santosh Thapa
Tags: Linear Regression, Python, Regression, Data Science, Machine Learning

Let’s make it easy..:)

Linear Regression is one of the fundamental machine learning algorithms used to find the value of the dependent variable using the relationship between the independent and dependent variables.

In machine learning lingo, linear regression is a supervised learning algorithm used to predict the value of continuous output using the continuous dependent variable. In short, linear regression maps continuous x to a continuous y.

Linear regression is specifically used in predicting the continuous or real output. For instance, say we want to predict the selling price of the car for our company using the previous dataset, in this case, we can predict such data using the regression problem.

In fact, the main thing to keep inside the mind is that simple linear regression means only one dependent variable associated with one independent variable. The main aim of the regression problem is to find the best fit line, by best fit line I mean to say that the line in which the square sum of error is minimum as possible. Such a line, which dissects our datapoints is considered as the best fitting line in the regression problem.

Let's see it mathematically in a simple way,

Here as shown in the figure, the main goal is to reduce the error distance between predicted points (which falls on the straight line) and actual points.

The best fit line is the which gives the least error among all the lines.

Here in the second figure, you can see that there is a positive slope since there is a positive correlation between the data points. It means that as the value of x increases(in our case it is speed), the value of y (distance also increases).

Similarly, in this figure, we can see that there is a negative correlation between x and y and hence, the slope is also negative.

The main thing to calculate in the linear regression is the coefficients, in our case, it is intercept (x) and slope(m). As mentioned above we can calculate the value of slope and intercept.

Hence, we can finally calculate the predicted points using above mathematical measures. But wait….

I have to tell some more here, so all we did till now is using the classical mathematical approach, all these things can be done using the built-in model in the python sci-kit learn the framework. However, it is very important that you understand the mathematics behind every algorithm else it will be meaningless. I will show that also. Are you ready:)

Here you go…

In this way, we can calculate a linear regression model. I tried my best to give my explanation and I might have missed some points sorry for that.

Thank you, everyone. I will come with a new story related to Machine Learning next time. Peace!!!
ENEM — Math score predictor. A model that predicts the score of math…
By: Diórgenes Eugênio
Tags: Udacity, Regression, Data Science, Enem

Questions to answer

1) Is it possible to predict the score of the math exam with good reliability?

If you are in a hurry, I already say that the answer is yes. But if you’re not in such a hurry, I’ll explain how I built my model to do this. The first step in answering this question was to understand the context of the problem. In other words, I had to analyze my objective within the dataset to define the best strategy.

Analyzing the data, I noticed that it would be a regression problem, not a classification one. I wanted to predict a score ranging from 0 to 1000, an infinite number of possibilities. With the defined approach, I started the data cleaning process to remove null values and treat other values ​​to add to its functionality.

After the cleaning process and the data exploration process, the time has come to tinker with the algorithms. To develop this work, I decided to buy two algorithms very applied for regression problems. The first was LinearRegression and the second RandomForestRegressor.

For each one, I executed the RFECV, a module within scikit-learn to perform the selection of features, that is, choose which columns in the database most influence my target (math note). With the RFECV in place, it was time to submit the two algorithms for testing. For the level of comparison, I chose to use r² as an evaluation metric.

The result was quite close for both, but the linear regression model obtained a better result reaching a coefficient of 0.9186. So, I believe it is possible to have a good predictor model for ENEM grades. This model can certainly be extended to other algorithms or even to other applications.

2) What are the critical information to the score of the exam?

In addition to developing the model for predicting math scores, I decided to run the SelectKBest algorithm, which selects the K best features for a given target. But what for? I wanted to find out what are the factors that most influence the student’s grade. Well, some returns were quite obvious, and others not so much. The first fields that returned were the other test scores, which makes total sense because generally, those who do well in one test tend to do well in another.

After the grades, another piece of information that greatly influenced the grades was the student’s age, which also makes sense, as older people are usually out of school for some time, and therefore are not as well prepared as young students. However, from here on, I started to surprise myself, actually not so much, but seeing this explained by the data was a little shocking. Another factor that inferred the student’s score is the relationship with work.

I noticed an inverse relationship between working and having a high score in the test. This highlights a series of structural problems within Brazilian education, from the time that the poorest young people have available to study. They have to reconcile the time between studies and work to help the family, even in teaching quality in Brazilian public schools.
The Supreme Court Defies Trump
By: Caren White
Tags: Government, Supreme Court, Election 2020, Politics, Secession

Politics

The Supreme Court Defies Trump

Photo by Sandra Grünewald on Unsplash

The Supreme Court refused on Friday to consider a lawsuit filed by the state of Texas and supported by Trump that would have thrown out the votes of millions of voters in four states that were won by Biden. The court said that Texas had no standing to interfere with the elections in other states. There were only two dissents, by Justices Alito and Thomas, who said that the lawsuit should have been considered but no relief given, meaning that Texas should have been allowed to argue the case but would ultimately have lost.

This decision was in direct defiance of Trump’s wishes. When he nominated Justice Barrett in October, he expressly said it was because he knew the 2020 election would be decided by the Supreme Court and that he needed a conservative majority on the court to vote in his favor and grant him a second term.

The court decided to follow the law and not the whims of a would-be dictator.

Trump’s tantrums in the White House must be epic. He expects total loyalty from everyone that he nominates to office. He has been rage tweeting claiming the court has let him down and lacks courage and wisdom.
I *may* have used that term before.
By: Pepper Mcgowan
Tags: Sexuality, Supreme Court, LGBTQ, Gay Marriage, Gay Rights

I *may* have used that term before. I may have said “sexual preference" but if I did, I would have only done so to clarify that MY sexual preference is good. I prefer that if I am going to have sex with someone, I don’t want to have any bad sex, but I gather that is everyone’s sexual preference regardless of their orientation!
N.C. Supreme Court Rejects Gov. Cooper’s Attempt to Control Appropriations
By: Senator Berger Press Shop
Tags: Roy Cooper, Supreme Court, North Carolina, Federal Block Grants, Ncga

Raleigh, N.C. — The N.C. Supreme Court, in a 6–1 decision, upheld every lower court’s decision to reject Gov. Roy Cooper’s attempt to obtain some control over state appropriations. The near-unanimous decision means that ten judges have now reaffirmed the longheld principle that the Constitution clearly and unambiguously vests the power of the purse with the legislature, not the executive branch.

Justice Anita Earls was the lone dissenter. Attorney General Josh Stein filed an amicus brief supporting the losing unconstitutional arguments to expand his and the Governor’s powers.

Sen. Warren Daniel (R-Burke), who co-chairs the Senate Judiciary Committee, said, “The N.C. Supreme Court’s well-reasoned decision in this matter was firmly grounded in the text of the Constitution and years of precedent.

“It’s unfortunate that Attorney General Stein valued expansion of executive power over the rule of law. Most first-year law students could look at the plain text of the Constitution and previous rulings from the Supreme Court and conclude that the Attorney General’s preferred outcome would be a grave violation of basic legal principles. The law, not politics, should come first in our state’s top law enforcement office.”

The case decided today involves whether the legislative or executive branch has the legal authority to appropriate three obscure federal block grants.

All funds controlled by the State of North Carolina, including federal block grants, go through the State treasury. The N.C. Constitution says, “No money shall be drawn from the State treasury but in consequence of appropriations made by law.” The Constitution provides that only the legislature can enact laws.

The N.C. Supreme Court previously ruled that the Constitution “state[s] in language no man can misunderstand that the legislative power is supreme over the public purse” (State v. Davis).

This case was heard at the trial level by a judge who ruled against Gov. Cooper.

The case was then heard at the N.C. Court of Appeals by a three-judge panel. The three-judge panel unanimously ruled against Gov. Cooper and Attorney General Stein, and the decision was written by a Democratic judge.

The Democrat-dominated state Supreme Court then took the case and five of the six Democrats on the court ruled against Gov. Cooper and Attorney General Stein.

The court wrote in its decision, “After a careful review of the relevant legal authorities, we have been unable to find any provision of the North Carolina State Constitution that creates a category of money that might possibly include the federal block grant monies that lies outside the State treasury or the General Assembly’s appropriation authority.”
Why the Supreme Court won’t take orders from a President.
By: T Guard
Tags: Supreme Court, Elections, Trump

If Trump picked Justices, why won’t they help him?

If you are an originalist, the answer is easy. The Framers and the ratifiers considered that a fair and impartial judiciary — one that followed the law and was not biased, partisan, intimidated or seeking preferment — was central to a republican form of government. They believed that judicial independence was critical to fairness and impartiality. They thought of judicial independence in its two facets: the decisional independence of the judge from outside pressures or inducements when deciding a case, and the independence of the judicial branch as a whole, as a separate branch of three.

This Goes Back to The Declaration of Independence

The Declaration of Independence prominently featured the King’s attacks on both the judicial branch and the individual judge in its bill of particulars:

“He has obstructed the Administration of Justice by refusing his Assent to Laws for establishing Judiciary Powers.”

And:

“He has made Judges dependent on his Will alone for the tenure of their offices, and the amount and payment of their salaries.”

Trump is acting as if he were king and his failure to understand history is detrimental to Democracy. His followers tend to take his words as facts. His demeaning Tweets laced with ad hominem are dumbing down citizens and insulting Democracy. Don’t take his word for it just because he says it.

He is not a king. We are a nation of made of rules built on a declaration seeking sovereignty from autocracy.

Know the history or be bound to repeat it.
Supreme Court Says Retweets Equal Endorsements, Views Of Employer
By: Max Barth
Tags: Jokes, Supreme Court, Satire, Comedy, Humor

(WASHINGTON) In a stunning 9–0 decision, the Supreme Court ruled Tuesday that retweets are, in all cases, endorsements. The unanimous opinion, authored by Associate Justice Sonia Sotomayor, further notes that “tweets, retweets and posts of any kind represent the exact and legally binding views of one’s employer, regardless of content.”

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This story is being updated as it develops. For up to the minute coverage, follow our live blog.
Texas AG Ken Paxton Rushed to Trump’s Rescue. Will He Return the Favor?
By: Joel Nihlean
Tags: Supreme Court, Donald Trump, Election 2020, Politics, Texas

But it wasn’t just liberals taking jabs at Paxton.

As more than 100 Republican members of the U.S. House signed on to support Paxton’s ill-fated suit, Rep. Chip Roy, a former Texas Attorney General staffer, called his former boss’s scheme “a dangerous violation of federalism.”

Conservative evangelical force of nature Erick Erickson called Paxton’s antics “performative leg humping by someone desperate to curry favor with President Trump.”

Is Ken Paxton going to be pardoned by President Trump?

Paxton is perhaps desperate to curry favor with the outgoing president, because he’s up to his ears in some deep legal shit of his own.

Paxton has been under indictment for securities fraud for more than five years now. He’s also more recently been accused of corruption, bribery, misusing his office to benefit a wealthy political donor, and other crimes — all by top members of his own staff who then quit in protest. Now the FBI has come knocking.

Thought Paxton dined with the president last week, he denies he’s angling for anything. “I’ve had no discussions with anything about… anything like that,” Paxton said about speculation that he may have discussed a pardon with the Whitehouse.

Rushing to Trump’s aid in hopes that Trump would rescue him sure would go a long way to explaining why an attorney general up to his neck in crimes would take on such a frivolous Hail Mary attempt to overturn a presidential election.

Paxton’s criminal behavior has escalated over the years. He famously tried to steal a $1,000 Montblanc pen from another lawyer, on camera, at the Collin County Courthouse. Now, with the feds on his tail, and a laundry list of state and federal crimes, Paxton needs his executive-level grift to pan out.

President Trump can’t pardon Paxton for state crimes, but the Feds are cracking down already, and soon they’ll be Biden’s Feds. Trump could make those troubles go away.

Courtesy Photo / Ken Paxton

Trump has a reputation hanging around criminals and helping loyalists. Paxton looks to be working hard to demonstrate that he’s both. Trump also likes people who “look the part,” and has a record of disliking losers and people who get caught. Paxton could have problems in that department with our deeply shallow commander in chief.

Paxton has a lazy eye — he nearly lost it in a game of hide-and-seek, and later reinjured it in a basketball game, leaving him with life-long vision problems — and as much as that shouldn’t matter, it very well might for this president.

Trump has granted numerous pardons to aides and allies already during his presidency, and last-minute pardon sprees are something of a presidential pastime.

Trump is no doubt making his list and checking it twice this holiday season, readying a pile of pardons before he skulks out of the White House. It’s far from clear who all is on the list, but Paxton is likely far down a list that starts with his three eldest children, and adopted fail son, Rudy Giuliani, plus at least 20 aides.
Supreme Court Slams The Door in Trump’s Face
By: H. Michael Harvey
Tags: Supreme Court, Election 2020, Trump, Electoral College, Elections

Photo by Claire Anderson on Unsplash

About the only way I can say it: The Supreme Court slammed the door in Trump’s face last week when it decided 9–0 to dismiss a complaint filed by the Attorney General of Texas against the election procedures of four other American states.

The Court shut the door so hard it ended all legal avenues for Trump’s campaign to overturn the people’s will and take the election victory away from Joe Biden and hand-deliver it to Trump.

But the Justices, three of whom had been handpicked by Trump, said loud and clear, get that mess out of here. Finally, a branch of the government stood up to Trump. If the judicial branch had cowered to Trump-like congressional Republicans- it would have been the death of American constitutional democracy as the world has known, since 1787.

Photo by visuals on Unsplash

Let’s not think for a minute that we can stick a fork in Trump. While he can not lawfully change the outcome of the November General Election, he undoubtedly will continue to put political pressure on Republican members of congress, first, to reject the vote of the Electoral College when it votes on Monday, December 14.

Next, when the United States Congress meets on January 6, 2021, to certify the electoral college vote expect Trump to resort again to political pressure on Republican legislators to toss out the electoral college vote.

Alabama Representative Mo Brooks said earlier this month that he would challenge the electoral college certification from several states whose voting procedures are different from Alabama’s process. Brooks is using the same flawed rationale that the Supreme Court just struck down.

Hold onto your hats, gentleladies, and gentlemen, the proverbial “fat lady,” at least in Donald Trump’s mind, has not sung.

Photo by David Todd McCarty on Unsplash

After losing on the legal front, and the political battle, Trump has only one last resort. He must turn to the “Proud Boys,” who have been standing ready since September. The 14 days after certification of the electoral college vote likely will spark militant action in the streets of the District of Columbia, unlike anything we had seen in the District since that first week in April 1968 when the nation experienced the assassination of Rev. Dr. Martin Luther King, Jr.

Harold Michael Harvey is the Living Now 2020 Bronze Medal winner for his memoir Freaknik Lawyer: A Memoir on the Craft of Resistance. He is a Past President of the Gate City Bar Association. He is the recipient of Gate City’s R. E. Thomas Civil Rights Award, which he received for his pro bono representation of Black college students arrested during Freaknik celebrations in the mid to late 1990s. Harvey is an engaging public speaker. Contact him at hmharvey@haroldmichaelharvey.com.
Dalton Tanonaka | Tanonaka wins supreme court review in investment case
By: Dalton Tanonaka
Tags: Supreme Court, Dalton Tanonaka, Investment Case

Dalton Tanonaka | Tanonaka wins supreme court review in investment case Dalton Tanonaka ·Dec 13, 2020

Dalton Tanonaka

TANONAKA WINS SUPREME COURT REVIEW IN INVESTMENT CASE

The Supreme Court of Indonesia on October 27, 2020, ruled in favor of Dalton Ichiro Tanonaka in his role as Director of P.T. Melia Media International, doing business as The Indonesia Channel. The Peninjauan Kembali (PK) judicial review reaffirmed a Jakarta High Court decision on May 23, 2018, that said no criminal actions were committed in a business agreement with investor Prem Harjani Ramchand.

“Finally I’m able to focus on my company’s work, which is to help this country tell its story to the world through The Indonesia Channel,” said the former Metro TV and CNN International news anchor. “I ask everyone now to join us in our mission, especially when we enter the post-coronavirus recovery period.”

Tanonaka was represented by attorney Marthen Pongrekun.
Texas Sues NFL in Supreme Court, Requests that Cowboys Be Declared Winner of NFC East
By: William Vaillancourt
Tags: News, Supreme Court, Satire, Politics, Humor

Keith Allison via Wikimedia Commons

Texas Attorney General Ken Paxton on Sunday filed a lawsuit against the National Football League in the Supreme Court, arguing that the Dallas Cowboys should be declared the winner of their division due to “gross abuses” by the league.

In Texas v. Roger Goodell, Paxton wrote that the wins thus far by each of the other three teams in the NFC East should be vacated because of the location of their stadiums.

“These teams play home games in New Jersey, Pennsylvania and Maryland, all of which were won by the Democrat nominee Joe Biden,” the lawsuit read. “This is no mere coincidence. In fact, it means their opponents were unjustly intimidated by the coastal elitism of these venues and felt that there was some funny business going on during games which affected outcomes.”

“Therefour,” it continued, “games played in these locations should not count, except in cases where the Cowboys won or the Giants, Eagles or Washington Football Team lost.”

The Cowboys are currently two games off the lead with four games left to play in the regular season. If the lawsuit’s request is seen through, the team would move up to first in the division. From there, the lawsuit requests that the league end the regular season immediately to allow for an extended Christmas celebration.

“Americans may not be able to celebrate Christmas ever again with Biden as president, so it would be wise to wrap things up now to make the most of it while we still can, and then begin playoffs after the New Year,” the lawsuit argued.

A lawyer claiming to represent the Plano Stampede and the El Paso Tumbleweeds — two non-existent football teams — filed a supporting brief filled with spelling errors, at one point referring to the Giants as the “Ginas.” However, one person involved in the case told the Boast that this individual’s interest lies not in supporting the Cowboys but in simply preventing the team from Washington, D.C. from achieving any sort of success due to its proximity to the “deep state.”

As the court decides whether it will hear the case, which experts say is unlikely, it has been reported that Cowboys owner Jerry Jones has been considering pulling his team out of the NFL to join the XFL.