Rahuls66

import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet


df = pd.read_csv('data.csv')

x = 

Rahuls66

import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Lasso


df = pd.read_csv('data.csv')

x = 

Rahuls66

import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge


df = pd.read_csv('data.csv')

x = 

Rahuls66

import seaborn as sns

df = sns.load_dataset('penguins')
# df.head()

sns.pairplot(df, dropna = True, hue = 'island');

Rahuls66

import seaborn as sns

df = sns.load_dataset('mpg')
# df.head()

sns.catplot(data=df, x="cylinders", y="displacement", col = 'origin', hue = 'model_year');

Rahuls66

# !pip install mplcyberpunk

import matplotlib.pyplot as plt
import mplcyberpunk

plt.style.use('cyberpunk')

plt.figure(figsize = (20,8))

plt.plot([1,4,6,7,4,1], marker = 'o')

Rahuls66

import numpy as np
import matplotlib.pyplot as plt

data = np.random.randint(20, 100, 6)

plt.pie(data)
circle = plt.Circle( (0,0), 0.7, color='white')
p=plt.gcf()
p.gca().add_artist(circle)

Rahuls66

import matplotlib.pyplot as plt
import numpy as np

x = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
y = np.random.random(1, (np.log(0.2 * np.pi)), 10)

plt.stem(x, y, use_line_collection = True)

plt.show()