def main():
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data[: [2, 3]]
y = iris.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)
nb = NaiveBayes()
nb.fit(X_train, y_train)
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from Perceptron import plot_decision_regions
plot_decision_regions(X, y, classifier=nb)
plt.tittle('Naive Bayes Trial')
plt.Xlabel('Sepal Length[standardized]')
plt.ylabel('Petal Length[Standardized]')
plt.show()
df['labels'] = labels
df.sort_values('labels')
#===Number of unique patterns=====
print(Number of unique patterns)
df['labels'].unique()
#============stocks moving together and stocks are different from each other===
print(stocks moving together and stocks are different from each other)
print(stocks apparently similar in performance)
df_cat0= df.loc[df['labels']==0]
df_cat1= df.loc[df['labels']==1]
df_cat2=df.loc[df['labels']==2]
df_cat2=df.loc[df['labels']==3]
ks = range(1,10)
inertias = []
for k in ks:
model = KMeans(n_clusters = k)
model.fit(df)
inertias.append(model.inertia_)
plt.plot(ks,inertias,'-o')
plt.Xlabel('no of cluster,k')
plt.ylabel('Inertias')
kolon_eksik_deger_toplami = veriler.isnull().sum()
print(kolon_eksik_deger_toplami)
X = veriler.iloc[:, 1:].values
from sklearn.cluster import KMeans
sonuclar = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, random_state=0)
kmeans.fit(X)
sonuclar.append(kmeans.inertia_)
plt.plot(range(1, 11), sonuclar)
plt.title('Küme Sayısı Belirlemek için Dirsek Yöntemi')
plt.Xlabel('Küme Sayısı')
plt.show()
kmeans = KMeans(n_clusters=6, init='k-means++', random_state=0)
Y_tahmin = kmeans.fit_predict(X)
print(Y_tahmin)
for i in range(0, 181):
print(veriler.iloc[i, 0])
print(Y_tahmin[i])
plt.scatter(X[Y_tahmin == 0, 0],
X[Y_tahmin == 0, 1],
s=75,
c='cyan',
label='Küme 1')
plt.scatter(X[Y_tahmin == 1, 0],
plt.show()
h = sns.PairGrid(iris)
h = h.map(plt.scatter)
sns.pairplot(iris)
plt.show(h)
i = sns.JointGrid(x='sepal_length', y='sepal_width', data=iris)
i = i.plot(sns.regplot, sns.distplot)
plt.show(i)
########################### Configuration ######################
g.despine(left=True)
g.set_ylabels('Survived')
g.set_xticklabels(rotation=45)
g.set_axis_labels('Survived', 'Sex')
h.set(xlim=(0, 5), ylim=(0, 5), xticks=[0, 2.5, 5], yticks=[0, 2.5, 5])
plt.title('Title')
plt.ylabel('Y')
plt.Xlabel('X')
plt.ylim(0, 100)
plt.xlim(0, 100)
plt.setp(ax, yticks=[0, 5]) # Setting axis property
from sklearn.linear_model import LinearRegression
lin_reg=LinearRegression()
lin_reg.fit(X,Y)
#fitting polynomial regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg=PolynomialFeatures(degree=4)
X_poly=poly_reg.fit_transform(X)
lin_reg_2=LinearRegression()
lin_reg_2.fit(X_poly,Y)
#visualising thre linear rehgression result
plt.scatter(X,Y,color='red')
plt.plot(X,lin_reg.predict(X),color='blue')
plt.title('Truth of Bluff(Linear Regression)')
plt.Xlabel('Position level')
plt.Ylabel('Salary')
plt.show()
#visualising the polynomial
X_grid=np.arange(min(X),max(X),0.1)
X_grid=X_grid.reshape((len(X_grid),1))
plt.scatter(X,Y,color='red')
plt.plot(X,lin_reg_2.predict(poly_reg.fit_transform(X)),color='blue')
plt.title('Truth of Bluff(Polynomial Regression)')
plt.Xlabel('Position level')
plt.Ylabel('Salary')
plt.show()
#predict a new result with linear regression
lin_reg.predict(6.5)
random_state=0)
# feature scaling
"""from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train=sc_X.fit_transform(X_train)
X_test=sc_X.transform(X_test)"""
#fitting simple linear regression to the training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, Y_train)
#predicting the test set result
Y_pred = regressor.predict(X_test)
# visualizing the training set
plt.scatter(X_train, Y_train, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Experoence(Training set)')
plt.xlabel('Years of Experience')
plt.Ylabel('Salary')
plt.show()
# visualizing the training set
plt.scatter(X_test, Y_test, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Experoence(Test set)')
plt.Xlabel('Years of Experience')
plt.Ylabel('Salary')
plt.show()
df5 = pd.read_excel("METEOROLOGICAL_SUB_DIVISION_WISE_ANNUAL_RAINFALL.xls")
df5
# In[82]:
df5[df5['Sub-division'] == df5['Sub-division'].min()][[
'2010 (in millimetre)', 'Sub-division'
]]
# In[83]:
import matplotlib.pyplot as plt
plt.plot(df5['Sub-division'],
df5['2002 (in millimetre)'],
linestyle="dashed",
marker='*',
color="purple")
x_pos = np.arange(len(df5))
plt.Xlabel("Sub divisions")
plt.ylabel("Rainfall")
plt.legend()
plt.title('rainfall analysis')
plt.show()
# In[69]:
import matpltlib.pyplot as plt
rc('font', weight='bold')
# In[ ]:
import matplotlib.pyplot as plt
import random
n = int(input("Enter no. of student: "))
l = [int(i) for i in range(n)]
m = []
for i in range(n):
x = random.randint(40, 100)
m.append(x)
print(m)
print(l)
plt.plot(l, m)
plt.title("Roll No. VS Marks ")
plt.Xlabel("Roll No.")
plt.Ylabel("Marks")
plt.show()
sc_y = StandardScaler()
X = sc_x.fit_transform(X)
y = sc_y.fit_transform(y)
#fitting regressor
from sklearn.svm import SVR
regressor = SVR(kernel='rbf')
regressor.fit(X, y)
#prediction
y_pred = sc_y.inverse_transform(
regressor.predict(sc_x.transform(np.array([[6.5]]))))
#visualisation
plt.scatter(X, y, color='green')
plt.plot(X, regressor.predict(X), color='red')
plt.title('bluffer detector')
plt.Xlabel('experience')
plt.ylabel('salary')
plt.show()
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color='pink')
plt.plot(X_grid, regressor.predict(X_grid), color='blue')
plt.title('bluffer detector')
plt.Xlabel('experience')
plt.ylabel('salary')
plt.show()
