Data Science Experiment - Milk Adulteration

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	import pandas as pd
	from sklearn import tree
	from sklearn.cross_validation import train_test_split
	from sklearn.ensemble import RandomForestClassifier
	import sys
	from mlxtend.classifier import EnsembleVoteClassifier
	from sklearn import svm
	from imblearn.over_sampling import SMOTE
	verbose = False
	ratio = 'auto'
	print (sys.version)
	input_file = "TrainDataBinaryClassification.xls"
	df = pd.read_csv(input_file,header=0,sep=",")
	print(df.head())
	print(df.head(5))
	#Remove insignificant id column
	df.drop(['Id'],1,inplace=True)
	#List all column headers
	print(list(df))
	#Fill missing values
	df = df.fillna(-999)
	features = list(df.columns[:-1])
	print(features);
	y1 = df['class']
	x1 = df[features]
	#Option 1
	#SENN = SMOTEENN(ratio=ratio)
	#x, y = SENN.fit_sample(x1, y1)
	#Option #2
	sm = SMOTE(kind='svm')
	x, y = sm.fit_sample(x1, y1)
	pred_train, pred_test, tar_train, tar_test = train_test_split(x,y,test_size=0.3)
	print('Shape of test data')
	rf = RandomForestClassifier(n_estimators=350) # initialize
	classifier2 = rf.fit(x, y) # fit the data to the algorithm
	pred_train, pred_test, tar_train, tar_test = train_test_split(x,y,test_size=0.3)
	print('Shape of test data')
	classifier = tree.DecisionTreeClassifier(criterion="entropy")
	classifier = classifier.fit(x,y)
	classifier3 = RandomForestClassifier(n_jobs=250)
	classifier3 = classifier3.fit(x,y)
	classifier2 = svm.SVC()
	classifier2 = classifier2.fit(x,y)
	clfs = [classifier, classifier2, classifier3]
	clf = EnsembleVoteClassifier(clfs, voting='hard', weights = (4,4,5))
	clf.fit(x, y)
	input_file = "TestDataTwoClass.xls"
	df = pd.read_csv(input_file,header=0,sep=",")
	df2 = pd.read_csv(input_file,header=0,sep=",")
	df.drop(['Id'],1,inplace=True)
	df = df.fillna(-999)
	x = df[features]
	predictions = clf.predict(x)
	print('predictions')
	i = 0
	for i in range(0,len(predictions)):
	print(predictions[i])
	df['class'] = predictions
	df2['class'] = predictions
	print('count',df['class'])
	header = ["Id","class"]
	df2.to_csv("Results_Binary_Class_Adulteration_Sep18_2.csv", sep=',', columns = header,index=False)

view raw Experiment1.py hosted with ❤ by GitHub

	import pandas as pd
	from sklearn import tree
	from sklearn.cross_validation import train_test_split
	import sklearn.metrics
	import sys
	sys.path.append('C:\\Anaconda2\\xgboost')
	import xgboost as xgb
	#2.7.12 |Anaconda 4.0.0 (64-bit)|
	print (sys.version)
	input_file = "TrainDataBinaryClassification.xls"
	df = pd.read_csv(input_file,header=0,sep=",")
	print(df.head())
	print(df.head(5))
	#Remove insignificant id column
	df.drop(['Id'],1,inplace=True)
	#List all column headers
	print(list(df))
	print(df.head())
	#Fill missing values
	df = df.fillna(-99)
	features = list(df.columns[:-1])
	print(features);
	y = df['class']
	x = df[features]
	pred_train, pred_test, tar_train, tar_test = train_test_split(x,y,test_size=0.3)
	print('Shape of test data')
	#classifier = tree.DecisionTreeClassifier(criterion="entropy")
	#classifier = classifier.fit(pred_train,tar_train)
	gbm = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05).fit(x, y)
	#print('acc', classifier.score(x,y))
	#predictions = classifier.predict(pred_test)
	#print(predictions)
	#print(sklearn.metrics.confusion_matrix(tar_test,predictions))
	#print('Classifier Accuracy')
	#print(sklearn.metrics.accuracy_score(tar_test,predictions))
	input_file = "TestDataTwoClassResults.xls"
	df = pd.read_csv(input_file,header=0,sep=",")
	df2 = pd.read_csv(input_file,header=0,sep=",")
	df.drop(['Id'],1,inplace=True)
	df = df.fillna(-99)
	x = df[features]
	predictions = gbm.predict(x)
	print('predictions')
	#print(predictions)
	i = 0
	for i in range(0,len(predictions)):
	print(predictions[i])
	#print('count',len(predictions))
	df2['class'] = predictions
	#df.to_csv("Results_Adulteration.csv", sep=',',index=False)
	header = ["Id","class"]
	df2.to_csv("Results_Adulteration_Sep15.csv", sep=',', columns = header,index=False)

view raw Experiment2.py hosted with ❤ by GitHub

	import pandas as pd
	from sklearn import tree
	from sklearn.cross_validation import train_test_split
	import sklearn.metrics
	import sys
	from sklearn.ensemble import RandomForestClassifier
	#2.7.12 |Anaconda 4.0.0 (64-bit)|
	print (sys.version)
	input_file = "TrainDataMultiClassClassification_Custom_sep18.csv"
	df = pd.read_csv(input_file,header=0,sep=",")
	print(df.head())
	print(df.head(5))
	#Remove insignificant id column
	df.drop(['Id'],1,inplace=True)
	#List all column headers
	print(list(df))
	print(df.head())
	#Fill missing values
	df = df.fillna(-9999)
	features = list(df.columns[:-1])
	print(features);
	y = df['class']
	x = df[features]
	pred_train, pred_test, tar_train, tar_test = train_test_split(x,y,test_size=0.3)
	print('Shape of test data')
	classifier = tree.DecisionTreeClassifier(criterion="entropy")
	classifier = classifier.fit(pred_train,tar_train)
	print('acc', classifier.score(x,y))
	predictions = classifier.predict(pred_test)
	rf = RandomForestClassifier(n_estimators=300) # initialize
	rf.fit(x, y) # fit the data to the algorithm
	input_file = "TestDataMultiClass.xls"
	df = pd.read_csv(input_file,header=0,sep=",")
	df2 = pd.read_csv(input_file,header=0,sep=",")
	df.drop(['Id'],1,inplace=True)
	df = df.fillna(-9999)
	x = df[features]
	predictions = rf.predict(x)
	#predictions = classifier.predict(x)
	print('predictions')
	#print(predictions)
	i = 0
	for i in range(0,len(predictions)):
	print(predictions[i])
	#print('count',len(predictions))
	df['class'] = predictions
	#print('count',len(predictions))
	df2['class'] = predictions
	print('count',df['class'])
	#df.to_csv("Results_Multi_Class_Adulteration.csv", sep=',',index=False)
	header = ["Id","class"]
	df2.to_csv("Results_Multi_Class_Adulteration_2_Sep18_RF.csv", sep=',', columns = header,index=False)

view raw Experiment3.py hosted with ❤ by GitHub

Happy Learning!!!