গাঁজার নৌকা পাহাড়তলি যায়…
গাঁজা খাব আঁটি আঁটি, মদ খাব বাটি বাটি;
ফেন্সি খেলে, ফেন্সি খেলে, ফেন্সি খেলে টাস্কি খেয়ে যাই! ও মিরাবাই!
আফিম খেলে মাথা ধরে, কোকেনে বুক ধরফর করে;
হিরু খেলে, হিরু খেলে, হিরু খেলে টাস্কি খেয়ে যাই! ও মিরাবাই!
খাবোনা আর গাঁজা আমি, যদি পাশে থাক তুমি;
তোমায় পেলে, তোমায় পেলে, তোমায় পেলে নেশা ভুলে যাই, ও মিরাবাই
জানি আসবেনা তুমি, আঁধারে এই কানাকানি,
জানি আসবেনা তুমি, বাতাসে এই কথা শুনি
তোমায় ভুলে, তোমায় ভুলে, তোমায় ভুলে গাঁজার নৌকাই বাই, ও মিরাবাই !!!
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ReplyDeletebangle coding
ReplyDeletepd_frame=pd.DataFrame(panda_file)
ReplyDeleteprint(pd_frame.iloc[:,1])
print(panda_file[panda_file.domestic==1])
print(panda_file[['animal_name','class_type']])
print(panda_file['animal_name'])
examinee = {'names': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew',
'Laura', 'Kevin', 'Jonas'],
'scores': [12.5, 9, 16.5, 2.3, 9, 20, 14.5, 4.5, 8, 19],
'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
'qualified': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}
pd_1=pd.DataFrame.from_dict(examinee)
q={'yes':1,'no':0}
pd_1['qualified']=pd_1['qualified'].map(q)
print(pd_1)
animal_name hair feathers eggs milk airborne aquatic predator toothed backbone breathes venomous fins legs tail domestic catsize class_type 1 1 0 0 1 1 0 1 1 0 0 2 1 0 1 2
ReplyDeletepheasant 0 1 1 0 1 0 0 0 1 1 0 0 2 1 0 0 2
pike 0 0 1 0 0 1 1 1 1 0 0 1 0 1 0 1 4
piranha 0 0 1 0 0 1 1 1 1 0 0 1 0 1 0 0 4
pitviper 0 0 1 0 0 0 1 1 1 1 1 0 0 1 0 0 3
platypus 1 0 1 1 0 1 1 0 1 1 0 0 4 1 0 1 1
polecat 1 0 0 1 0 0 1 1 1 1 0 0 4 1 0 1 1
pony 1 0 0 1 0 0 0 1 1 1 0 0 4 1 1 1 1
porpoise 0 0 0 1 0 1 1 1 1 1 0 1 0 1 0 1 1
puma 1 0 0 1 0 0 1 1 1 1 0 0 4 1 0 1 1
pussycat 1 0 0 1 0 0 1 1 1 1 0 0 4 1 1 1 1
raccoon 1 0 0 1 0 0 1 1 1 1 0 0 4 1 0 1 1
reindeer 1 0 0 1 0 0 0 1 1 1 0 0 4 1 1 1 1
rhea 0 1 1 0 0 0 1 0 1 1 0 0 2 1 0 1 2
scorpion 0 0 0 0 0 0 1 0 0 1 1 0 8 1 0 0 7
seahorse 0 0 1 0 0 1 0 1 1 0 0 1 0 1 0 0 4
seal 1 0 0 1 0 1 1 1 1 1 0 1 0 0 0 1 1
sealion 1 0 0 1 0 1 1 1 1 1 0 1 2 1 0 1 1
seasnake 0 0 0 0 0 1 1 1 1 0 1 0 0 1 0 0 3
seawasp 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 7
skimmer 0 1 1 0 1 1 1 0 1 1 0 0 2 1 0 0 2
skua 0 1 1 0 1 1 1 0 1 1 0 0 2 1 0 0 2
slowworm 0 0 1 0 0 0 1 1 1 1 0 0 0 1 0 0 3
slug 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 7
sole 0 0 1 0 0 1 0 1 1 0 0 1 0 1 0 0 4
sparrow 0 1 1 0 1 0 0 0 1 1 0 0 2 1 0 0 2
squirrel 1 0 0 1 0 0 0 1 1 1 0 0 2 1 0 0 1
starfish 0 0 1 0 0 1 1 0 0 0 0 0 5 0 0 0 7
stingray 0 0 1 0 0 1 1 1 1 0 1 1 0 1 0 1 4
swan 0 1 1 0 1 1 0 0 1 1 0 0 2 1 0 1 2
termite 0 0 1 0 0 0 0 0 0 1 0 0 6 0 0 0 6
toad 0 0 1 0 0 1 0 1 1 1 0 0 4 0 0 0 5
tortoise 0 0 1 0 0 0 0 0 1 1 0 0 4 1 0 1 3
tuatara 0 0 1 0 0 0 1 1 1 1 0 0 4 1 0 0 3
tuna 0 0 1 0 0 1 1 1 1 0 0 1 0 1 0 1 4
vampire 1 0 0 1 1 0 0 1 1 1 0 0 2 1 0 0 1
vole 1 0 0 1 0 0 0 1 1 1 0 0 4 1 0 0 1
vulture 0 1 1 0 1 0 1 0 1 1 0 0 2 1 0 1 2
wallaby 1 0 0 1 0 0 0 1 1 1 0 0 2 1 0 1 1
wasp 1 0 1 0 1 0 0 0 0 1 1 0 6 0 0 0 6
wolf 1 0 0 1 0 0 1 1 1 1 0 0 4 1 0 1 1
worm 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 7
wren 0 1 1 0 1 0 0 0 1 1 0 0 2 1 0 0 2
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Deleteimport matplotlib.pyplot as plt
ReplyDeletefrom sklearn.linear_model import LinearRegression
from sklearn.cross_validation import train_test_split
from sklearn.datasets import make_regression
# synthetic dataset for simple regression
plt.figure()
plt.title('Sample regression problem with one input variable')
X_R1, y_R1 = make_regression(n_samples = 100, n_features=1,
n_informative=1, bias = 150.0,
noise = 30, random_state=1)
plt.scatter(X_R1, y_R1, marker= 'o', s=50)
plt.show()
X_train, X_test, y_train, y_test = train_test_split(X_R1, y_R1,
random_state = 0)
linreg = LinearRegression().fit(X_train, y_train)
print('Output of Least Squares Logistic Regression')
#Task 1:
#print('linear model coeff (w): {}'
# .format(linreg.coef_))
#print('linear model intercept (b): {:.3f}'
# .format(linreg.intercept_))
print('R-squared score (training): {:.3f}'
.format(linreg.score(X_train, y_train)))
print('R-squared score (test): {:.3f}'
.format(linreg.score(X_test, y_test)))
#Plot
plt.figure(figsize=(5,4))
plt.scatter(X_R1, y_R1, marker= 'o', s=10, alpha=0.8)
plt.plot(X_R1, linreg.coef_ * X_R1 + linreg.intercept_, 'r-')
plt.title('Least-squares linear regression')
plt.xlabel('Feature value (x)')
plt.ylabel('Target value (y)')
plt.show()
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