Delete jijifi.txt

jijifi.txt

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-# K Means Clustering w elbow plot
-#importing libraries
-from sklearn.cluster import KMeans
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-import matplotlib.pyplot as plt
-#import dataset
-df = pd.read_csv("C:datasets\.csv")
-df.head()
-plt.scatter(df.Age, df.Income)
-plt.xlabel('Age')
-plt.ylabel('Income')
-scaler = MinMaxScaler()
-scaler.fit(df[['Income']])
-df['Income'] = scaler.transform(df[['Income']])
-
-scaler.fit(df[['Age']])
-df['Age'] = scaler.transform(df[['Age']])
-
-#Elbow plot
-sse = []
-k_rng = range(1, 10)
-for k in k_rng:
-    km = KMeans(n_clusters = k)
-    km.fit(df[['Age', 'Income']])
-    sse.append(km.inertia_)
-plt.xlabel('K')
-plt.ylabel('Sum of squared error')
-plt.plot(k_rng,sse)
-km = KMeans(n_clusters = 3)
-y_predicted = km.fit_predict(df[['Age', 'Income']])
-y_predicted
-km.cluster_centers_
-df['cluster']=y_predicted
-df.head()
-df1 = df[df.cluster == 0]
-df2 = df[df.cluster == 1]
-df3 = df[df.cluster == 2]
-
-plt.scatter(df1.Age, df1.Income, color = 'black')
-plt.scatter(df2.Age, df2.Income, color = 'orange')
-plt.scatter(df3.Age, df3.Income, color = 'maroon')
-plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:,1], color = 'purple', marker = '*', label = 'centroid')
-plt.xlabel('Age')
-plt.ylabel('Income in $')
-
-#Association Rule Mining
-#!pip install --no-binary :all: mlxtend
-import pandas as pd
-from mlxtend.preprocessing import TransactionEncoder
-from mlxtend.frequent_patterns import apriori
-dataset = [["Milk", "Cola", "Beer"], 
-          ["Milk", "Pepsi", "Juice"], 
-          ["Milk", "Beer"],
-          ["Cola", "Juice"],
-          ["Milk", "Pepsi", "Beer"]]
-dataset
-var = TransactionEncoder()
-var_arr = var.fit(dataset).transform(dataset)
-df = pd.DataFrame(var_arr, columns = var.columns_)
-df
-#from mlxtend.frequent_patterns import apriori
-fq_is = apriori(df, min_support = 0.5, use_colnames = True)
-fq_is
-from mlxtend.frequent_patterns import association_rules
-rules_ap = association_rules(fq_is, metric = "confidence", min_threshold = 0.01)
-rules_ap
-
-##Decision Tree
-
-import pandas as pd
-from sklearn.preprocessing import LabelEncoder
-from sklearn import tree
-df = pd.read_csv('C:/Users/datasets/lung cancer.csv')
-df.head()
-df.isnull().any()
-le_GENDER = LabelEncoder()
-le_LUNG_CANCER = LabelEncoder()
-
-df['Gender'] = le_GENDER.fit_transform(df['GENDER'])
-df['LungCancer'] = le_LUNG_CANCER.fit_transform(df['LUNG_CANCER'])
-
-df.head()
-df = df.drop(['GENDER','LUNG_CANCER'], axis = 1)
-df.head()
-inputs = df.drop('LungCancer', axis = 1)
-target = df['LungCancer']
-inputs.head()
-print(target)
-from sklearn import tree
-model = tree.DecisionTreeClassifier()
-model.fit(inputs, target)
-model.score(inputs, target)
-print(model.predict([[63,1,2,2,1,2,1,1,2,1,2,1,1,2,2]]))
-tree.plot_tree(model) 
-
-##SVM bank loan
-
-# libraries
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn
-import sklearn
-from sklearn import model_selection
-from sklearn.model_selection import train_test_split
-from sklearn import svm
-from sklearn.svm import SVC
-from sklearn import metrics
-from sklearn.metrics import confusion_matrix
-from sklearn.metrics import classification_report
-from sklearn.metrics import roc_curve, auc 
-bl = pd.read_csv("C:/fifi.csv")
-bl = pd.DataFrame(bl)
-bl_data = bl.drop(['address','ed','debtinc','employ'],1)
-bl_data.head(3)
-x = bl_data.drop('default', axis = 1)
-y = bl_data.default
-x.head()
-y.head()
-x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=0)
-# building an SVM having penalty 10 and gamma auto optimized
-svct = SVC(kernel = 'linear',
-               C = 10, gamma = 'auto', 
-               probability = True).fit(x_train, y_train)
-
-print(svct)
-y_pred = svct.predict(x_test)
-y_pred
-cnf = confusion_matrix(y_test, y_pred)
-cnf
-# ROC Curve with AUC Plotting
-# finding probabilities for 0 and 1 classses
-preds1 = svct.predict_proba(x_test)[:,1]
-print(preds1)
-from sklearn.metrics import roc_curve, auc 
-fpr1, tpr1, thresholds1 = metrics.roc_curve(y_test, preds1)
-fpr1
-tpr1
-thresholds1	
-df1 = pd.DataFrame(dict(fpr = fpr1, tpr = tpr1))
-auc = metrics.auc(fpr1, tpr1)
-# plotting
-plt.figure()
-plt.plot(fpr1, tpr1, color = 'darkorange', label = 'ROC CURVE(area = %0.2f)' % auc)
-plt.plot([0,1], [0,1], color = 'navy')
-plt.xlim([0.0, 1.0])
-plt.ylim([0.0, 1.05])
-plt.xlabel('False Positive Rate')
-plt.ylabel('True Positive Rate')
-plt.title('ROC example')
-plt.legend(loc = 'lower right')
-plt.show()
-
-##SVM Iris
-from sklearn import svm
-import pandas as pd
-df = pd.read_csv("C:datasets/iris_df.csv")
-df.head(5)
-df.columns = ['X4','X3','X1','X2','Y']
-df = df.drop(['X4','X3'],1)
-df.head()
-from sklearn.model_selection import train_test_split
-from sklearn import svm
-x = df.drop("Y", axis = 1)
-y = df.Y
-
-support = svm.SVC()
-
-
-trainx, testx, trainy, testy = train_test_split(x,y, test_size = 0.3)
-support.fit(trainx, trainy)
-print('Accuracy:/n', support.score(testx, testy))
-
-pred = support.predict(testx)
-import pandas as pd
-import numpy as np
-from sklearn import linear_model
-import matplotlib.pyplot as plt
-df = pd.read_csv("C:datasets/house_price.csv")
-df.head()
-plt.xlabel('area')
-plt.ylabel('price')
-plt.scatter(df.area,df.price,color = 'blue')
-reg = linear_model.LinearRegression()
-reg.fit(df[['area']], df.price)
-# prediction
-reg.predict([[3100]])
-reg.coef_
-reg.intercept_
-plt.xlabel('area')
-plt.ylabel('price')
-plt.scatter(df.area,df.price,color='green')
-plt.plot(df.area,reg.predict(df[['area']]), color = 'red')
-
-##Linear Regression Multiple Variable
-# pandas numpy
-from sklearn import linear_model
-df = pd.read_csv("C:/house_pricemodel.csv")
-df.head()
-df.isnull()
-df.bedrooms.median()
-df.bedrooms = df.bedrooms.fillna(df.bedrooms.median())
-df
-reg = linear_model.LinearRegression()
-reg.fit(df.drop('price', axis = 1), df.price)
-reg.coef_
-reg.predict([[3200,3,10]])
-##SImple Logistic Regression
-# pandas, matplotlib
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-df = pd.read_csv("C:/Users//Datasets/insurance_age.csv")
-df.head()
-plt.scatter(df.age,df.bought_insurance, marker ='*', color = 'purple')
-x_train, x_test, y_train, y_test = train_test_split(df[['age']], df.bought_insurance, train_size=0.8)
-model = LogisticRegression()
-model.fit(x_train, y_train)
-y_predicted = model.predict(x_test)
-model.predict_proba(x_test)
-model.score(x_test, y_test)
-y_predicted
-model.predict([[45]])
-##Gradient descent simple
-import numpy as np
-import matplotlib.pyplot as plt
-def gradient_descent(x,y):
-    m_curr = b_curr = 0
-    iterations = 10000
-    n = len(x)     # length of x
-    learning_rate = 0.001
-    #learning rate = .00001
-    
-    
-    
-    for i in range(iterations):
-        y_predicted = m_curr * x + b_curr
-        cost = (1/n) * sum([val**2 for val in (y-y_predicted)])
-        md = -(2/n)*sum(x*(y-y_predicted))     # m derivative
-        bd = -(2/n)*sum(y-y_predicted)         # b derivative
-        m_curr = m_curr - learning_rate * md
-        b_curr = b_curr - learning_rate * bd
-        #print ("m {}, b {}, iteration {}".format(m_curr,b_curr, i))
-        print ("m {}, b {}, cost {} iteration {}".format(m_curr,b_curr,cost, i))    # each iteration to print the values
-x = np.array([1,2,3,4,5])
-y = np.array([5,7,9,11,13])
-gradient_descent(x,y)