Engineering; Computer Science; Computer Science questions and answers; In [1]: import matplotlib.pyplot as plt plt.style.use('ggplot') %matplotlib inline In [2]: import numpy as np import pandas as pd import seaborn as sns plt.rcParams['figure.figsize'] = (12, 8) In [3]: data - pd.read_csv('bike_sharing_data.txt'); ET In [4]: data.head() Out[4]: Population Profit 0 6.1101 …

Question: import matplotlib.pyplot as plt import numpy as np def f(t,x,c): m=20 k=20 return np.array([x[1]. (-k*x[@]-c+x[1]}/m]) h=.1 for c in (5,40,200): t=np.arange ...

In [1]: import numpy as np import matplotlib.pyplot as plt %matplotlib inline %autosave Autosave disabled Linear Regression Exercises Exercise: toy data Create one-dimensional regression data using scikit-learn's make_regression. Use linear regression to create a model and plot the resulting fitted line.

Question: : matplotlib inline import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from ipywidgets import interact, interactive import itertools import hashlib from scipy.stats import poisson, norm, gamma import statsmodels.api as sm sns.set(style="dark") plt.style.use("ggplot") try: from pymc3 import * import pymc3 as pm

Question: In [1] import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline import seaborn as sns import sklearn from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import accuracy_score In [3]: df = pd.read_csv("iris-data(1).csv") df.head() Out [3]: sepal_length_cm

import matplotlib.pyplot as plt import numpy as np import scipy from scipy import stats import pickle Standard Normal Random Variate Generation In the lecture we stated the following fact: this fact. Below implement function, Normal (n : int )−> tuple with an argument n, and returning a tuple of lists where the first list comes from Z1 and ...

Question: In [1]: import pandas as pd import matplotlib.pyplot as plt import numpy as np import scipy.stats as st # use gamma distribution to randomly generate 5e observations. shape, scale = 1.95, 2.5 tpcp = 100 np.random.gamma(shape, scale, see) * pandas Library can be used to convert the array into a dataframe of rounded figures with the column name TPCP. tpcp_df =

In [ ]; import matplotlib.pyplot as plt import numpy as np import seaborn as sns Problem 1 Data is provided as follows: In [ ]: x = np.linspace(-np.pi, np.pi, 256, endpoint=True) #Return evenly spaced numbers over a specified interval. y = np.cos(x) y1 = np.sin(x) Given the data x, y, and y1 above, complete the code and create the plot as instructed.

Question: import numpy as np import matplotlib.pyplot as plt from matplotlib.cm import get_cmap import pandas as pd from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix, classification_report from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing

In [ ]: import matplotlib.pyplot as plt import numpy as np import seaborn as sns Problem 1 Data is provided as follows: In [ ]: x = np.linspace( - np.pi, np.pi, 256, endpoint=True) #Return evenly spaced numbers over a specified interval. y = np.cos(x) y1 = np.sin(x) Given the data x, y, and y1 above, complete the code and create the plot as instructed.