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()