Aditya1001001

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
% matplotlib inline
from sklearn.linear_model import LinearRegression

n_samples = 100
X = np.linspace(0, 10, 100)
y = X ** 3 + np.random.randn(n_samples) * 100 + 100

Aditya1001001

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
% matplotlib inline
from sklearn.preprocessing import PolynomialFeatures

n_samples = 100
X = np.linspace(0, 10, 100)
y = X ** 3 + np.random.randn(n_samples) * 100 + 100

Aditya1001001

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import numpy as np

fig = plt.figure()
ax = fig.gca(projection='3d')   # Create the axes

# Data
X = np.linspace(-8, 8, 100)

Aditya1001001

import seaborn as sns
sns.set(rc={'figure.figsize':(12,8)})


df = sns.load_dataset('iris')
  
sns.regplot(x = "sepal_length",
            y = "petal_length",
            data = df,
           color="r")

Aditya1001001

class Vector():
    def __new__(cls, x, y):
        print("__new__ was invoked")
        instance = object.__new__(cls)
        return instance

    def __init__(self, x, y):
        print("__init__ was invoked")
        self.x = x
        self.y = y