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John Ehrlichman
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Fine Art Storehouse Photo Prints and Wall Art
John Ehrlichman
(Original Caption) 6/1/1986- Washington, DC: John Ehrlichman says during a interview, it's not that he despises Henry Kissinger and Alexander Haig, but he felt it necessary to be historically accurate when he makes them sound so sleazy in his new novel. PH:Ron Bennett. Unleash your creativity and transform your space into a visual masterpiece.
Bettmann
Media ID 39288521
Gesturing John Ehrlichman Public Speaking Talking Writer
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EDITORS COMMENTS
John Ehrlichman sits in conversation during an interview at his desk in Washington D.C., conveying a nuanced perspective on those he portrays in his novel, including Henry Kissinger and Alexander Haig, as historical figures rather than personal adversaries.
Framed Prints of John Ehrlichman
Capture the essence of American politics with our iconic print featuring John Ehrlichman. This striking image by Bettmann from Fine Art Storehouse, taken in 1986, showcases Ehrlichman's candid moment during an interview where he shares his thoughts on Henry Kissinger and Alexander Haig. A unique addition to any home or office decor that will spark conversation and inspire curiosity about the past.
Jigsaw Puzzles of John Ehrlichman
import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score # Load the dataset into DataFrame df. df = pd.read_csv('your_data.csv') # Define features X and target y. Here we use columns 0-4 as features (X) and column 5 as our target variable (y). X = df.iloc[:, :-1] y = df['target_column'] # Split the data into training set X_train, y_train and test set X_test, y_test. X_train, X_test,y_train ,y_test= train_test_split(X,y,test_size=0.2) model = LinearRegression() model.fit(X_train, y_train) predictions = model.predict(X_test) mse = mean_squared_error(ytest,predictions) r2=r2_score (ytest,predictions)
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