Added Hierarchical Clustering

Hierarchical_clustering/Mall_Customers.csv

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+CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100)
+0001,Male,19,15,39
+0002,Male,21,15,81
+0003,Female,20,16,6
+0004,Female,23,16,77
+0005,Female,31,17,40
+0006,Female,22,17,76
+0007,Female,35,18,6
+0008,Female,23,18,94
+0009,Male,64,19,3
+0010,Female,30,19,72
+0011,Male,67,19,14
+0012,Female,35,19,99
+0013,Female,58,20,15
+0014,Female,24,20,77
+0015,Male,37,20,13
+0016,Male,22,20,79
+0017,Female,35,21,35
+0018,Male,20,21,66
+0019,Male,52,23,29
+0020,Female,35,23,98
+0021,Male,35,24,35
+0022,Male,25,24,73
+0023,Female,46,25,5
+0024,Male,31,25,73
+0025,Female,54,28,14
+0026,Male,29,28,82
+0027,Female,45,28,32
+0028,Male,35,28,61
+0029,Female,40,29,31
+0030,Female,23,29,87
+0031,Male,60,30,4
+0032,Female,21,30,73
+0033,Male,53,33,4
+0034,Male,18,33,92
+0035,Female,49,33,14
+0036,Female,21,33,81
+0037,Female,42,34,17
+0038,Female,30,34,73
+0039,Female,36,37,26
+0040,Female,20,37,75
+0041,Female,65,38,35
+0042,Male,24,38,92
+0043,Male,48,39,36
+0044,Female,31,39,61
+0045,Female,49,39,28
+0046,Female,24,39,65
+0047,Female,50,40,55
+0048,Female,27,40,47
+0049,Female,29,40,42
+0050,Female,31,40,42
+0051,Female,49,42,52
+0052,Male,33,42,60
+0053,Female,31,43,54
+0054,Male,59,43,60
+0055,Female,50,43,45
+0056,Male,47,43,41
+0057,Female,51,44,50
+0058,Male,69,44,46
+0059,Female,27,46,51
+0060,Male,53,46,46
+0061,Male,70,46,56
+0062,Male,19,46,55
+0063,Female,67,47,52
+0064,Female,54,47,59
+0065,Male,63,48,51
+0066,Male,18,48,59
+0067,Female,43,48,50
+0068,Female,68,48,48
+0069,Male,19,48,59
+0070,Female,32,48,47
+0071,Male,70,49,55
+0072,Female,47,49,42
+0073,Female,60,50,49
+0074,Female,60,50,56
+0075,Male,59,54,47
+0076,Male,26,54,54
+0077,Female,45,54,53
+0078,Male,40,54,48
+0079,Female,23,54,52
+0080,Female,49,54,42
+0081,Male,57,54,51
+0082,Male,38,54,55
+0083,Male,67,54,41
+0084,Female,46,54,44
+0085,Female,21,54,57
+0086,Male,48,54,46
+0087,Female,55,57,58
+0088,Female,22,57,55
+0089,Female,34,58,60
+0090,Female,50,58,46
+0091,Female,68,59,55
+0092,Male,18,59,41
+0093,Male,48,60,49
+0094,Female,40,60,40
+0095,Female,32,60,42
+0096,Male,24,60,52
+0097,Female,47,60,47
+0098,Female,27,60,50
+0099,Male,48,61,42
+0100,Male,20,61,49
+0101,Female,23,62,41
+0102,Female,49,62,48
+0103,Male,67,62,59
+0104,Male,26,62,55
+0105,Male,49,62,56
+0106,Female,21,62,42
+0107,Female,66,63,50
+0108,Male,54,63,46
+0109,Male,68,63,43
+0110,Male,66,63,48
+0111,Male,65,63,52
+0112,Female,19,63,54
+0113,Female,38,64,42
+0114,Male,19,64,46
+0115,Female,18,65,48
+0116,Female,19,65,50
+0117,Female,63,65,43
+0118,Female,49,65,59
+0119,Female,51,67,43
+0120,Female,50,67,57
+0121,Male,27,67,56
+0122,Female,38,67,40
+0123,Female,40,69,58
+0124,Male,39,69,91
+0125,Female,23,70,29
+0126,Female,31,70,77
+0127,Male,43,71,35
+0128,Male,40,71,95
+0129,Male,59,71,11
+0130,Male,38,71,75
+0131,Male,47,71,9
+0132,Male,39,71,75
+0133,Female,25,72,34
+0134,Female,31,72,71
+0135,Male,20,73,5
+0136,Female,29,73,88
+0137,Female,44,73,7
+0138,Male,32,73,73
+0139,Male,19,74,10
+0140,Female,35,74,72
+0141,Female,57,75,5
+0142,Male,32,75,93
+0143,Female,28,76,40
+0144,Female,32,76,87
+0145,Male,25,77,12
+0146,Male,28,77,97
+0147,Male,48,77,36
+0148,Female,32,77,74
+0149,Female,34,78,22
+0150,Male,34,78,90
+0151,Male,43,78,17
+0152,Male,39,78,88
+0153,Female,44,78,20
+0154,Female,38,78,76
+0155,Female,47,78,16
+0156,Female,27,78,89
+0157,Male,37,78,1
+0158,Female,30,78,78
+0159,Male,34,78,1
+0160,Female,30,78,73
+0161,Female,56,79,35
+0162,Female,29,79,83
+0163,Male,19,81,5
+0164,Female,31,81,93
+0165,Male,50,85,26
+0166,Female,36,85,75
+0167,Male,42,86,20
+0168,Female,33,86,95
+0169,Female,36,87,27
+0170,Male,32,87,63
+0171,Male,40,87,13
+0172,Male,28,87,75
+0173,Male,36,87,10
+0174,Male,36,87,92
+0175,Female,52,88,13
+0176,Female,30,88,86
+0177,Male,58,88,15
+0178,Male,27,88,69
+0179,Male,59,93,14
+0180,Male,35,93,90
+0181,Female,37,97,32
+0182,Female,32,97,86
+0183,Male,46,98,15
+0184,Female,29,98,88
+0185,Female,41,99,39
+0186,Male,30,99,97
+0187,Female,54,101,24
+0188,Male,28,101,68
+0189,Female,41,103,17
+0190,Female,36,103,85
+0191,Female,34,103,23
+0192,Female,32,103,69
+0193,Male,33,113,8
+0194,Female,38,113,91
+0195,Female,47,120,16
+0196,Female,35,120,79
+0197,Female,45,126,28
+0198,Male,32,126,74
+0199,Male,32,137,18
+0200,Male,30,137,83

Hierarchical_clustering/hc.py

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+# Hierarchical Clustering
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Mall_Customers.csv')
+X = dataset.iloc[:, [3, 4]].values
+# y = dataset.iloc[:, 3].values
+
+# Splitting the dataset into the Training set and Test set
+"""from sklearn.cross_validation import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, 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)
+sc_y = StandardScaler()
+y_train = sc_y.fit_transform(y_train)"""
+
+# Using the dendrogram to find the optimal number of clusters
+import scipy.cluster.hierarchy as sch
+dendrogram = sch.dendrogram(sch.linkage(X, method = 'ward'))
+plt.title('Dendrogram')
+plt.xlabel('Customers')
+plt.ylabel('Euclidean distances')
+plt.show()
+
+# Fitting Hierarchical Clustering to the dataset
+from sklearn.cluster import AgglomerativeClustering
+hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
+y_hc = hc.fit_predict(X)
+
+# Visualising the clusters
+plt.scatter(X[y_hc == 0, 0], X[y_hc == 0, 1], s = 100, c = 'red', label = 'Cluster 1')
+plt.scatter(X[y_hc == 1, 0], X[y_hc == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')
+plt.scatter(X[y_hc == 2, 0], X[y_hc == 2, 1], s = 100, c = 'green', label = 'Cluster 3')
+plt.scatter(X[y_hc == 3, 0], X[y_hc == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')
+plt.scatter(X[y_hc == 4, 0], X[y_hc == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')
+plt.title('Clusters of customers')
+plt.xlabel('Annual Income (k$)')
+plt.ylabel('Spending Score (1-100)')
+plt.legend()
+plt.show()

NLP/Live-Twitter-Sentiment-Analysis.py

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+### Live Twitter Sentiment Analysis ###
+
+# pip install tweepy
+# pip install textblob
+
+# Creating Account with Twitter API
+
+## Importing the Libraries ##
+from textblob import TextBlob
+import sys
+import tweepy
+import matplotlib.pyplot as plt
+
+## Define a function to calculate the percentage ##
+def percentage(part, whole):
+    return 100 * float(part)/float(whole)
+
+## Connecting with the Twitter API ##
+
+# Importing the keys #
+consumerKey = "copy here your keys"
+consumerSecret = "copy here your keys"
+accessToken = "copy here your keys"
+accessTokenSecret = "copy here your keys"
+
+# Establish the connection with API #
+auth = tweepy.OAuthHandler(consumerKey, consumerSecret)
+auth.set_access_token(accessToken, accessTokenSecret)
+api = tweepy.API(auth)
+
+# Search for the Term and define number of tweets #
+searchTerm = input("Enter Keyword/Tag to search about: ")
+NoOfTerms = int(input("Enter how many tweets to search: "))
+
+# Get no of tweets and searched term together #
+tweets = tweepy.Cursor(api.search, q=searchTerm).items(NoOfTerms)
+
+
+## Iterate and Analyse the tweets ##
+
+# Working of the TextBlob #
+a = TextBlob("I am a bad Cricket player")
+a.sentiment.polarity
+
+b = TextBlob("I am a good Cricket player")
+b.sentiment.polarity
+
+c = TextBlob("I am a Cricket player")
+c.sentiment.polarity
+
+# Create variables to hold the average polarity #
+
+positive = 0
+negative = 0
+neutral = 0
+polarity = 0
+
+for tweet in tweets:
+   analysis = TextBlob(tweet.text) 
+   polarity += analysis.sentiment.polarity
+
+   if(analysis.sentiment.polarity == 0):
+       neutral += 1
+
+   elif(analysis.sentiment.polarity < 0.00):
+       negative += 1
+
+   elif(analysis.sentiment.polarity > 0.00):
+       positive += 1
+
+# Generate the percentages using previously created function percentage #
+positive = percentage(positive, NoOfTerms)
+negative = percentage(negative, NoOfTerms)
+neutral  = percentage(neutral, NoOfTerms)
+polarity  = percentage(polarity, NoOfTerms)
+
+# Limit the decimal upto 2 places #
+positive = format(positive, '.2f')
+negative = format(negative, '.2f')
+neutral = format(neutral, '.2f')
+
+
+## Print the result ##
+
+print("How people are reacting on " + searchTerm + 
+      " By analyzing" + str(NoOfTerms) + " Tweets ")
+
+if (polarity == 0):
+    print("Neutral")
+
+elif (polarity < 0.00):
+    print("Negative")
+
+elif (polarity > 0.00):
+    print("Positive")
+
+## Print the Pie Chart ##
+
+labels = ['Positive ['+str(positive)+'%]', 
+          'Neutral ['+str(neutral)+'%]', 
+          'Negative ['+str(negative)+'%]']
+
+sizes = [positive, neutral, negative]
+colors = ['yellowgreen', 'gold', 'red']
+patches, texts = plt.pie(sizes, colors = colors, startangle = 90)
+plt.legend(patches, labels, loc = "best")
+plt.title('How people are reacting on  '
+          +searchTerm+' by analyzing '
+          +str(NoOfTerms)+' Tweets ')
+plt.axis('equal')
+plt.tight_layout()
+plt.show()