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tqgincorrectquotes · 2 years

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Townes: Imagine if someone handed you a box full of all the items you have lost throughout your life.

Cleo: It would be nice to get my sense of purpose back.

Jolene: Oh wow, my childhood innocence! Thank you for finding this.

Benny: My will to live! I haven't seen this in ten years!

Harry: I knew I lost that potential somewhere!

Beth: Mental stability, my old friend!

Townes: ...

Townes: Guys, could you lighten up a little?

#the queen's gambit #the queen's gambit incorrect quotes #tqg incorrect quotes #d.l. townes #cleo #jolene dewitt #benny watts #harry beltik #beth harmon #source: tumblr

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crookshanksshipsdramione · 1 month

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fluffy dramione story told through newspaper articles

Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7, Part 8

this is part 9/ 10

Daily Prophet

Society

London (November, 2019). Despite strong protests to the contrary rumor still has it that Hermione Granger and Draco Malfoy are an item. Several eyewitnesses told this newspaper that they saw Miss Granger out shopping in the Diagon Alley with none other than little Scorpius Malfoy. “I know what I saw!”, a middle-aged man told us “It was him! He looks like his father duplicated himself”. “They met up with the Weasley-Potters at WWW and Scorpius seemed to be close with their kids”, a young witch confirmed. “I overheard Ron Weasley calling him tiny Draco while ruffling the boy’s hair”. Mr. Malfoy could not be reached for comment as to why his son was in not-Mr.Malfoy’s-girlfriend-Granger’s care.

Personal jeweler to Narcissa Malfoy seen entering her son’s London town house.

Is there a Malfoy engagement on the horizon?

The plot is thickening: this author has learned that very recently, Hugh Prince, who has been a personal jeweler to Narcissa Malfoy for years, entered her son Mr. Draco Malfoy’s town house in London. He stayed for about two hours and left with a rather pleased expression on his face. Whatever that might mean? Maybe Draco was in the mood for some new crown jewels.

Forthcoming Marriages March 2022:

Mr. D.L. Malfoy and Miss H.J. Granger. The engagement is announced between Draco, son of Lucius and Narcissa Malfoy of London and Hermione, daughter of Jean and Norbert Granger of London.”

#dramione #dramione fluff #dramione fanfic #newspaper part 9 #newspaper story

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meret118 · 9 months

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Wolfsong — TJ Klune (Tor Books)

Oxnard Matheson was twelve when his father taught him a lesson: Ox wasn’t worth anything and people would never understand him. Then his father left. Ox was sixteen when the energetic Bennett family moved in next door, harboring a secret that would change him forever. The Bennetts are shapeshifters. They can transform into wolves at will. Drawn to their magic, loyalty, and enduring friendships, Ox feels a gulf between this extraordinary new world and the quiet life he’s known, but he finds an ally in Joe, the youngest Bennett boy. Ox was twenty-three when murder came to town and tore a hole in his heart. Violence flared, tragedy split the pack, and Joe left town, leaving Ox behind. Three years later, the boy is back. Except now he’s a man—charming, handsome, but haunted—and Ox can no longer ignore the song that howls between them.

Thief Liar Lady — D.L. Soria (Del Rey)

I’m not who you think I am. My transformation from a poor, orphaned scullery maid into the enchantingly mysterious lady who snagged the heart of the prince did not happen—as the rumors insisted—in a magical metamorphosis of pumpkins and glass slippers. On the first evening of the ball, I didn’t meekly help my “evil” stepmother and stepsisters primp and preen or watch forlornly out the window as their carriage rolled off toward the palace. I had other preparations to make. My stepsisters and I had been trained for this—to be the cleverest in the room, to be quick with our hands and quicker with our lies. We were taught how to get everything we wanted in this world, everything men always kept for themselves: power, wealth, and prestige. And with a touchingly tragic past and the help of some highly illegal spells, I would become a princess, secure our fortunes, and we would all live happily ever after. But there’s always more to the story. With my magic running out, war looming, and a handsome hostage prince—the wrong prince—distracting me from my true purpose with his magnetic charm and forbidden flirtations, I’m in danger of losing control of the delicate balance I’ve created… and that could prove fatal. There’s so much more riding on this than a crown.

#Books

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therealimintobooks · 5 months

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PRICK by D.L. Hammons

My Thoughts I loved reading “Prick”! It’s such a thrilling and suspenseful mystery that kept me glued to the pages. The small-town setting of New Haven was so fascinating, with all its secrets waiting to be uncovered. The author’s writing was superb, as he masterfully woven the story together and created well-rounded characters that I couldn’t help but fall in love with. Watching the plot…

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#Suspense #Thriller #Young Adult

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yhwhrulz777 · 9 months

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Worthy Brief - July 25, 2023

Put out the fire!

2 Corinthians 1:20 For all the promises of God in him are yea, and in him Amen, unto the glory of God by us.

In the midst of a serious heat wave, wildfires are spreading across Europe causing thousands to evacuate. This reminded me of a story I once read.

When a power plant in a tiny village in Alaska caught fire one evening, a local fire truck stood, unused, in perfect working condition as the plant burnt to the ground. Damage to the plant was great. And why did this state of the art firetruck go unused? So sad… simply nobody in the town knew how to operate it.

This is what I realized. Amidst the everyday fires we face in our lives, God has given us precious, perfectly good working, state of the art promises. Unfortunately, it seems that either we are ignorant of them or fail to know how to operate them!

D.L. Moody says that many of the promises God has given to His people, to us, "seem to be pretty pictures of an ideal peace and rest, but are not appropriated as practical helps in daily life. And not one of these promises is more neglected that the assurance of salvation. An open Bible places them within reach of all, and we may appropriate the blessing which such a knowledge brings.”

Are you burning to put God's promises to work in your life today?? I know I am! Let's get into God's Word, get to know the depth of His commitment to us, and put His wonderful promises to action!

Your family in the Lord with much agape love,

George, Baht Rivka, Obadiah and Elianna (Dallas, TX) (Baltimore, MD)

Editor's Note:Just wanted to share an exciting article written about my wife and our ministry. Also a link to her recent single that seems to be going viral. It is extremely timely and relevant for the day in which we are living. If you are fighting a battle, or you know some people who are, send it to them and sing it with us! God will have the victory!!

Editor's Note: We are planning our Fall Tour so if you would like us to minister at your congregation, home fellowship, or Israel focused event, be sure to let us know ASAP. You can send an email to george [ @ ] worthyministries.com for more information.

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feed-my-reads · 1 year

Link

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incomenotify2 · 2 years

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Identity Theft Explained

As outrageous as this may sound, you require to do a criminal background check at least as soon as a year. Identity wrongdoers can develop false documents of your information such as D.L or Recognition cards, passports, and birth certificates. They utilize your personal details during criminal activities and pass your information on as their own to police, to prevent utilizing their real identity from adding or having a record to it if they already have one. Secure antivirus scan help identity theft . If you're going to be out of town, have the post office hold your mail or ask someone to select it up. Location outbound mail in an official mailbox, not your own. So they have your date of birth and access to your e-mail account, all they need to do is login, find the receipt for the table you bought a week back and BAM they have your address. And all they have actually had to do is ask a few concerns, ones that are asked every day, extremely innocuous. Financial identity theft. You can not blame the people when they think of credit reports and draining pipes checking account as quickly as they hear the words 'identity theft.' This is so because of big scale data breaches like that of TJ Maxx and Heartland Payment Systems which has actually impacted millions of charge card users. While individuals still trust differed banks, it can not be denied that these trusts are currently shaken. Some individuals are even considering keeping their cash under the mattresses once again.

youtube

All of us see those amusing commercials on television with the male speaking as a female and so on. It is not so amusing when you or somebody you understand becomes a victim. Id theft will constantly worsen each year. The following week Medicare called inquiring about specific tests for another patient. She didn't understand the patient. Fear swallowed her body as she restored order on her cluttered desk and left the workplace in a daze, questioning was occurring. After arriving home from her medical facility rounds she called her attorney. He would check out the situation, however felt there was little he might do to assist. If there are warrants out there for your arrest, although you can't keep in mind when you did any criminal offense punishable by law, then you can be sure that somebody else is doing it for you. Of course, you can't anticipate every police offer to think you say, 'it wasn't me' or 'somebody else did it.' They hear that everyday. It is therefore important that you keep your individual info out of the general public reach so you won't fall victims to any of these identity thefts. Again, another concern comes to mind: which files should I shred and which should remain? There are some basic steps we can all require to decrease the threat. The very first of which I would suggest would be two have two separate e-mail addresses, one for your monetary organization, and the other for whatever else.

#identity theft scan #get identity theft scan #buy identity theft scan #legit identity theft scan #trusted identity theft scan #online identity theft scan

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mldataanlysis · 2 years

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Week3-Lasso Regression

@author-Bikramjit

TO DO:

We would require to run a run a lasso regression. We would need to run a lasso regression analysis using k-fold cross validation to identify a subset of predictors from a larger pool of predictor variables that best predicts a quantitative response variable.

Ideally, (Lasso Least Absolute Shrinkage and Selection Operator.) regression is a kind of linear regression which utilizes shrinkage. In simpler terms, we shrunk the data values towards a central point, similar to mean.

DATASET USED:

Here we are using the BOSTON HOUSING DATASET.

print(df.DESCR)

.. _boston_dataset: Boston house prices dataset

---------------------------

**Data Set Characteristics:**

:Number of Instances: 506

:Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.

:Attribute Information (in order): - CRIM per capita crime rate by town - ZN proportion of residential land zoned for lots over 25,000 sq.ft. - INDUS proportion of non-retail business acres per town - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) - NOX nitric oxides concentration (parts per 10 million) - RM average number of rooms per dwelling - AGE proportion of owner-occupied units built prior to 1940 - DIS weighted distances to five Boston employment centres - RAD index of accessibility to radial highways - TAX full-value property-tax rate per $10,000 - PTRATIO pupil-teacher ratio by town - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town - LSTAT % lower status of the population - MEDV Median value of owner-occupied homes in $1000's

:Missing Attribute Values: None

:Creator: Harrison, D. and Rubinfeld, D.L.

This is a copy of UCI ML housing dataset. https://archive.ics.uci.edu/ml/machine-learning-databases/housing/

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 1980. N.B. Various transformations are used in the table on pages 244-261 of the latter.

The Boston house-price data has been used in many machine learning papers that address regression problems.

.. topic:: References

- Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261. - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.

CODE:

#import all the required libraries

import pandas as pd import numpy as np from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.linear_model import LassoCV from sklearn import preprocessing from sklearn.metrics import mean_squared_error import matplotlib.pylab as plt import seaborn as sns %matplotlib inline

#load the dataset

df=load_boston()

print(df.DESCR)

dataset = pd.DataFrame(df.data)

dataset.shape

(506, 13)

#features and target

dataset.columns=df.feature_names

dataset["Price"]=df.target

#load the independent variables in X and dependent variables in y

X=dataset.iloc[:,:-1]

y=dataset.iloc[:,-1]

#splitting the data into 70% training set and 30% testing set

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

At first, we are applying lasso regression on the training set with an arbitrary regularization parameter α(alpha) of 1.

regression = Lasso(alpha=1) regression.fit(X_train, y_train)

To select the best model and find the optimal value of alpha, we are using cross-validation

# Lasso with k=10 fold cross-validation

model = LassoCV(cv=10, random_state=1000, max_iter=10000)

# Fitting the model model.fit(X_train, y_train)

model.alpha_

0.754892367295477

Applying lasso regression with the optimal alpha value

lasso_best_model = Lasso(alpha=model.alpha_) lasso_best_model.fit(X_train, y_train)

#show the model coefficients and names

print(list(zip(lasso_best_model.coef_, X)))

[(-0.07903630751706744, 'CRIM'), (0.048119192704057186, 'ZN'), (-0.0001680987202248114, 'INDUS'), (0.0, 'CHAS'), (-0.0, 'NOX'), (1.6587173804637732, 'RM'), (0.002892581799134004, 'AGE'), (-0.8746046348738787, 'DIS'), (0.20652520434356006, 'RAD'), (-0.014080720078095755, 'TAX'), (-0.8520736879678186, 'PTRATIO'), (0.007121490623308746, 'B'), (-0.6770950039057196, 'LSTAT')]

RESULT AND ANALYSIS:

1.The best value of alpha chosen by cross-validation is 0.754892367295477

2. R-squared analysis for arbitary alpha =1 reached 70.84% for the training set and 61.15% for the test set.

print('R-squared for training set', round(reg.score(X_train, y_train)*100, 2)) print('R squared for test set', round(reg.score(X_test, y_test)*100, 2))

R-squared for training set 70.84 R squared for test set 61.15

3. R-squared analysis for the model using optimal alpha reached 72.65% for the training set and 62.94% for the test set.

R-squared for training set 72.65 R-squared for test set 62.94

4.The mean squared error for the best model reached 30.856352866595522

mean_squared_error(y_test, lasso_best_model.predict(X_test))

The distplot for the target ‘Price’ as per the lasso_best_model

lasso_predict = lasso_best_model.predict(X_test)

sns.distplot(y_test-lasso_predict)