├── Codes ├── datacleaning1.py ├── datacleaning2.py ├── decisiontree.py ├── knnprediction.py ├── logisticregression.py └── naivebayesprediction.py ├── Dataset ├── NoPCOS.csv ├── OnlyPCOS.csv ├── allData.csv ├── clean_data.csv └── results.xlsx ├── Final Model - Best Accuracy └── final_decisiontree.py └── README.md /Codes/datacleaning1.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """DataCleaning.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/12Zq_ZASPLAj3oOl3AnQPU1gl7jb_lsnt 8 | """ 9 | 10 | import pandas as pd 11 | import numpy as np 12 | 13 | from google.colab import drive 14 | drive.mount('/content/drive') 15 | 16 | copied_path = "drive/My Drive/Academics SPIT/PROJECTS/SEM 4 - PCOS Prediction Research Project/Codes/results.csv" 17 | data = pd.read_csv(copied_path) 18 | 19 | data.head() 20 | 21 | data.drop('Timestamp', inplace=True, axis=1) 22 | data.drop('PCOS tested', inplace=True, axis=1) 23 | data.drop('When do you experience mood swings?', inplace=True, axis=1) 24 | 25 | data.head() 26 | 27 | data["City"] = data["City"].str.lower() # lower all city names 28 | 29 | data.head() 30 | 31 | data = data.rename(columns={'PCOS from age of': 'PCOS_from'}) 32 | 33 | data['PCOS_from'] = data.PCOS_from.str.extract('(\d+)') 34 | 35 | data.head() 36 | 37 | data.to_csv(r'drive/My Drive/Academics SPIT/PROJECTS/SEM 4 - PCOS Prediction Research Project/Codes/allData.csv', index=False) 38 | 39 | PCOS_True.dropna(subset=["PCOS_from"], inplace=True) 40 | 41 | PCOS_True.head() 42 | 43 | PCOS_True.to_csv(r'drive/My Drive/Academics SPIT/PROJECTS/SEM 4 - PCOS Prediction Research Project/Codes/OnlyPCOS.csv', index=False) 44 | -------------------------------------------------------------------------------- /Codes/datacleaning2.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """Copy of Copy of PCOS_DataCleaning.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1Tv0oEBigTE2sb41Dzga8aNjDmKXnmPkT 8 | """ 9 | 10 | from google.colab import files 11 | import numpy as np 12 | import pandas as pd 13 | from google.colab import drive 14 | drive.mount('/content/drive') 15 | 16 | 17 | path = "/content/drive/My Drive/SEM 4 - PCOS Prediction Research Project/Codes/allData.csv" 18 | data = pd.read_csv(path) 19 | 20 | data.head() 21 | 22 | 23 | def label5(row): 24 | if row['Hair growth on Chin'] == 'normal': 25 | return 0 26 | elif row['Hair growth on Chin'] == 'moderate': 27 | return 1 28 | else: 29 | return 2 30 | 31 | 32 | data['Hair growth on Chin'] = data.apply(lambda row: label5(row), axis=1) 33 | data.head() 34 | 35 | 36 | def label16(row): 37 | if row['relocated city'] == 'Yes': 38 | return 1 39 | else: 40 | return 0 41 | 42 | 43 | data['relocated city'] = data.apply(lambda row: label16(row), axis=1) 44 | data.head() 45 | 46 | data.to_csv('data.csv') 47 | files.download("data.csv") 48 | 49 | 50 | def label17(row): 51 | if row['Period Length'] == '2-3 days': 52 | return 3 53 | elif row['Period Length'] == '4-5 days': 54 | return 5 55 | elif row['Period Length'] == '6-7 days': 56 | return 7 57 | else: 58 | return 9 59 | 60 | 61 | data['Period Length'] = data.apply(lambda row: label17(row), axis=1) 62 | data.head() 63 | 64 | 65 | def label18(row): 66 | if row['Cycle Length'] == '20-24 days': 67 | return 22 68 | elif row['Cycle Length'] == '20-28 days': 69 | return 25 70 | elif row['Cycle Length'] == '25-28': 71 | return 27 72 | elif row['Cycle Length'] == '29-35 days': 73 | return 32 74 | elif row['Cycle Length'] == '36+ days': 75 | return 37 76 | else: 77 | return 'NaN' 78 | 79 | 80 | data['Cycle Length'] = data.apply(lambda row: label18(row), axis=1) 81 | data.head() 82 | 83 | del data['PCOS_from'] 84 | 85 | data 86 | 87 | data.to_csv('data_final.csv') 88 | files.download("data_final.csv") 89 | 90 | path = "/content/drive/My Drive/SEM 4 - PCOS Prediction Research Project/Codes/Decesion Tree/data (1).csv" 91 | data1 = pd.read_csv(path) 92 | data1.head() 93 | 94 | 95 | def label17(row): 96 | if row['Period Length'] == '2-3 days': 97 | return 3 98 | elif row['Period Length'] == '4-5 days': 99 | return 5 100 | elif row['Period Length'] == '6-7 days': 101 | return 7 102 | else: 103 | return 9 104 | 105 | 106 | data1['Period Length'] = data.apply(lambda row: label17(row), axis=1) 107 | data1.head() 108 | 109 | 110 | def label18(row): 111 | if row['Cycle Length'] == '20-24 days': 112 | return 1 113 | elif row['Cycle Length'] == '20-28 days': 114 | return 2 115 | elif row['Cycle Length'] == '25-28': 116 | return 3 117 | elif row['Cycle Length'] == '29-35 days': 118 | return 4 119 | elif row['Cycle Length'] == '36+ days': 120 | return 5 121 | else: 122 | return 6 123 | 124 | 125 | data1['Cycle Length'] = data.apply(lambda row: label18(row), axis=1) 126 | data1.head() 127 | 128 | 129 | def label19(row): 130 | if row['Age'] == 'Below 18': 131 | return 1 132 | elif row['Age'] == '18-25': 133 | return 2 134 | elif row['Age'] == '26-30': 135 | return 3 136 | elif row['Age'] == '31-35': 137 | return 4 138 | elif row['Age'] == '36-40': 139 | return 5 140 | elif row['Age'] == '41-45': 141 | return 6 142 | else: 143 | return 7 144 | 145 | 146 | data1['Age'] = data.apply(lambda row: label19(row), axis=1) 147 | data1.head() 148 | 149 | data1.to_csv('data_cleaned.csv') 150 | files.download("data_cleaned.csv") 151 | -------------------------------------------------------------------------------- /Codes/decisiontree.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """DecisionTree.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1pX87WDq00tHR3HwAEDO4zcb9HClhDNcg 8 | """ 9 | 10 | from IPython.display import Image 11 | from sklearn.externals.six import StringIO 12 | import pydotplus 13 | from sklearn.tree import export_graphviz 14 | from sklearn import tree 15 | import matplotlib.pyplot as plt 16 | from sklearn.model_selection import train_test_split 17 | from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report 18 | from sklearn.tree import DecisionTreeClassifier 19 | import numpy as np 20 | import pandas as pd 21 | from google.colab import drive 22 | drive.mount('/content/drive') 23 | 24 | 25 | path = "/content/drive/My Drive/SEM 4 - PCOS Prediction Research Project/Codes/Decesion Tree/Copy of data_cleaned (1).csv" 26 | data = pd.read_csv(path) 27 | data.head() 28 | 29 | del data['PCOS_from'] 30 | 31 | del data['City'] 32 | 33 | del data['relocated city'] 34 | 35 | del data['Unnamed: 0'] 36 | 37 | data['PCOS_label'] = None 38 | data.head() 39 | 40 | data = data.set_index('PCOS_label') 41 | 42 | data = data.reset_index() 43 | 44 | 45 | def label(row): 46 | if row['PCOS'] == 'Yes': 47 | return 1 48 | else: 49 | return 0 50 | 51 | 52 | data['PCOS_label'] = data.apply(lambda row: label(row), axis=1) 53 | 54 | data.head() 55 | 56 | # Commented out IPython magic to ensure Python compatibility. 57 | # %matplotlib notebook 58 | # from adspy_shared_utilities import plot_decision_tree 59 | 60 | PCOS_check = dict(zip(data.PCOS_label.unique(), data.PCOS.unique())) 61 | PCOS_check 62 | 63 | X = data.drop(['PCOS_label', 'PCOS'], axis=1) 64 | y = data.PCOS_label 65 | 66 | X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) 67 | 68 | X_train.head() 69 | 70 | y_train.head() 71 | 72 | clf = DecisionTreeClassifier(max_depth=6).fit(X_train, y_train) 73 | 74 | tree_predicted = clf.predict(X_test) 75 | confusion = confusion_matrix(y_test, tree_predicted) 76 | print(confusion) 77 | 78 | print('Accuracy: {:.2f}'.format(accuracy_score(y_test, tree_predicted))) 79 | print('Precision: {:.2f}'.format(precision_score(y_test, tree_predicted))) 80 | print('Recall: {:.2f}'.format(recall_score(y_test, tree_predicted))) 81 | print('F1: {:.2f}'.format(f1_score(y_test, tree_predicted))) 82 | 83 | print(classification_report( 84 | y_test, tree_predicted, target_names=['No', 'Yes'])) 85 | 86 | print('Accuracy on training set: {:.2f}'.format(clf.score(X_train, y_train))) 87 | 88 | print('Accuracy on test set: {:.2f}'.format(clf.score(X_test, y_test))) 89 | 90 | feature_cols = ['Period Length', 'Cycle Length', 'Age', 'Overweight', 'loss weight gain / weight loss', 'irregular or missed periods', 'Difficulty in conceiving', 'Hair growth on Chin', 'Hair growth on Cheeks', 'Hair growth Between breasts', 91 | 'Hair growth on Upper lips ', 'Hair growth in Arms', 'Hair growth on Inner thighs', 'Acne or skin tags', 'Hair thinning or hair loss ', 'Dark patches', 'always tired', 'more Mood Swings', 'exercise per week', 'eat outside per week', 'canned food often'] 92 | 93 | 94 | # Create DOT data 95 | dot_data = StringIO() 96 | 97 | export_graphviz(clf, out_file=dot_data, filled=True, rounded=True, 98 | special_characters=True, feature_names=feature_cols) 99 | # Draw graph 100 | graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) 101 | 102 | # show graph 103 | Image(graph.create_png()) 104 | 105 | pcos_prediction = clf.predict(X_test) 106 | PCOS_check[pcos_prediction[0]] 107 | 108 | pcos1 = clf.predict( 109 | [[5, 6, 2, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 7, 0]]) 110 | PCOS_check[pcos1[0]] 111 | 112 | pcos2 = clf.predict( 113 | [[5, 1, 2, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 3, 3, 0]]) 114 | PCOS_check[pcos2[0]] 115 | -------------------------------------------------------------------------------- /Codes/knnprediction.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """KNNPrediction.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1Qks5Os4M6zo_I0dNMBvV7LI92XCmBWRW 8 | """ 9 | 10 | from sklearn.metrics import accuracy_score 11 | from sklearn.metrics import confusion_matrix 12 | from sklearn.metrics import classification_report 13 | import operator 14 | import math 15 | import random 16 | import csv 17 | import pandas as pd 18 | import numpy as np 19 | 20 | from google.colab import drive 21 | drive.mount('/content/drive') 22 | 23 | #copied_path = "drive/My Drive/ML/KNN/data.csv" 24 | copied_path = "/content/drive/My Drive/Academics SPIT/PROJECTS/SEM 4 - PCOS Prediction Research Project/Codes/KNN/data.csv" 25 | data = pd.read_csv(copied_path) 26 | 27 | data.head() 28 | cols = [0] 29 | data.drop(data.columns[cols], inplace=True, axis=1) 30 | 31 | cols = list(data.columns.values) # Make a list of all of the columns in the df 32 | cols.pop(cols.index('City')) # Remove b from list 33 | cols.pop(cols.index('PCOS')) # Remove x from list 34 | cols.pop(cols.index('PCOS_from')) 35 | data = data[cols+['City', 'PCOS_from', 'PCOS']] 36 | 37 | data['PCOS'] = data['PCOS'].map(dict(Yes=1, No=0)) 38 | 39 | data.head() 40 | 41 | 42 | def loadDataset(data, split, trainingSet=[], testSet=[]): 43 | dataset = data.values.tolist() 44 | for x in range(len(dataset)-1): 45 | for y in range(22): 46 | dataset[x][y] = int(dataset[x][y]) 47 | if random.random() < split: 48 | trainingSet.append(dataset[x]) 49 | else: 50 | testSet.append(dataset[x]) 51 | 52 | 53 | def euclideanDistance(instance1, instance2, length): 54 | distance = 0 55 | for x in range(length): 56 | distance += pow((instance1[x] - instance2[x]), 2) 57 | return math.sqrt(distance) 58 | 59 | 60 | def getNeighbors(trainingSet, testInstance, k): 61 | distances = [] 62 | length = len(testInstance)-1 63 | for x in range(len(trainingSet)): 64 | dist = euclideanDistance(testInstance, trainingSet[x], length) 65 | distances.append((trainingSet[x], dist)) 66 | distances.sort(key=operator.itemgetter(1)) 67 | neighbors = [] 68 | 69 | for x in range(k): 70 | neighbors.append(distances[x][0]) 71 | 72 | return neighbors 73 | 74 | 75 | def getResponse(neighbors): 76 | classVotes = {} 77 | for x in range(len(neighbors)): 78 | response = neighbors[x][-1] 79 | 80 | if response in classVotes: 81 | classVotes[response] += 1 82 | else: 83 | classVotes[response] = 1 84 | sortedVotes = sorted(classVotes.items(), 85 | key=operator.itemgetter(1), reverse=True) 86 | 87 | return sortedVotes[0][0] 88 | 89 | 90 | def getAccuracy(testSet, predictions): 91 | correct = 0 92 | 93 | for x in range(len(testSet)): 94 | if testSet[x][-1] == predictions[x]: 95 | correct += 1 96 | 97 | return (correct/float(len(testSet))) 98 | 99 | 100 | # Python script for confusion matrix creation. 101 | 102 | 103 | def main(): # prepare data 104 | trainingSet = [] 105 | testSet = [] 106 | split = 0.67 107 | 108 | loadDataset(data, split, trainingSet, testSet) 109 | 110 | print('Train set: ' + repr(len(trainingSet))) 111 | 112 | print('Test set: ' + repr(len(testSet))) 113 | 114 | # generate predictions 115 | predictions = [] 116 | k = 3 117 | 118 | for x in range(len(trainingSet)): 119 | neighbors = getNeighbors(trainingSet, trainingSet[x], k) 120 | result = getResponse(neighbors) 121 | predictions.append(result) 122 | 123 | #print('> predicted=' + repr(result) + ', actual=' + repr(trainingSet[x][-1])) 124 | accuracy = getAccuracy(trainingSet, predictions) 125 | print('Accuracy Training: ' + repr(accuracy) + '%') 126 | 127 | predictions.clear() 128 | for x in range(len(testSet)): 129 | neighbors = getNeighbors(trainingSet, testSet[x], k) 130 | result = getResponse(neighbors) 131 | predictions.append(result) 132 | 133 | #print('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1])) 134 | accuracy = getAccuracy(testSet, predictions) 135 | print('Accuracy Test: ' + repr(accuracy) + '%') 136 | 137 | actual = [] 138 | 139 | for x in range(len(testSet)): 140 | actual.append(testSet[x][-1]) 141 | 142 | results = confusion_matrix(actual, predictions) 143 | 144 | print('Confusion Matrix :') 145 | print(results) 146 | print('Accuracy Score :', accuracy_score(actual, predictions)) 147 | print('Report : ') 148 | print(classification_report(actual, predictions)) 149 | 150 | 151 | main() 152 | -------------------------------------------------------------------------------- /Codes/logisticregression.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """LogisticRegression.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1y8Mb0HTo2AqYMcahdeY-qdp2ahXIYeUk 8 | """ 9 | 10 | from sklearn.metrics import accuracy_score 11 | from sklearn.preprocessing import StandardScaler 12 | from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report 13 | from sklearn.model_selection import train_test_split 14 | from sklearn.linear_model import LogisticRegression 15 | import pandas as pd 16 | import numpy as np 17 | 18 | from google.colab import drive 19 | drive.mount('/content/drive') 20 | 21 | data_path = "/content/drive/My Drive/Academics SPIT/PROJECTS/SEM 4 - PCOS Prediction Research Project/Codes/Logistic Regression/data.csv" 22 | data = pd.read_csv(data_path) 23 | 24 | data.head() 25 | 26 | del data['PCOS_from'] 27 | del data['City'] 28 | del data['relocated city'] 29 | del data['Unnamed: 0'] 30 | 31 | data.head() 32 | 33 | data['PCOS_label'] = None 34 | data = data.set_index('PCOS_label') 35 | data = data.reset_index() 36 | data.head() 37 | 38 | 39 | def label(row): 40 | if row['PCOS'] == 'Yes': 41 | return 1 42 | else: 43 | return 0 44 | 45 | 46 | data['PCOS_label'] = data.apply(lambda row: label(row), axis=1) 47 | 48 | data.head() 49 | 50 | 51 | PCOS_check = dict(zip(data.PCOS_label.unique(), data.PCOS.unique())) 52 | PCOS_check 53 | 54 | x = data.drop(['PCOS_label', 'PCOS'], axis=1) 55 | y = data.PCOS_label 56 | 57 | #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state=0) 58 | 59 | xtrain, xtest, ytrain, ytest = train_test_split( 60 | x, y, test_size=0.25, random_state=0) 61 | 62 | sc_x = StandardScaler() 63 | xtrain = sc_x.fit_transform(xtrain) 64 | xtest = sc_x.transform(xtest) 65 | 66 | print(xtrain[0:10, :]) 67 | 68 | classifier = LogisticRegression(random_state=0) 69 | classifier.fit(xtrain, ytrain) 70 | 71 | y_pred = classifier.predict(xtest) 72 | 73 | cm = confusion_matrix(ytest, y_pred) 74 | 75 | print("Confusion Matrix : \n", cm) 76 | 77 | print("Accuracy : ", accuracy_score(ytest, y_pred)) 78 | -------------------------------------------------------------------------------- /Codes/naivebayesprediction.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """NaiveBayesPrediction.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1U3EHSc61enQyf32VQByHsiZ_vCqupRS5 8 | """ 9 | 10 | import pandas as pd 11 | import numpy as np 12 | from sklearn.model_selection import train_test_split 13 | from sklearn.naive_bayes import GaussianNB 14 | from sklearn.metrics import accuracy_score 15 | import matplotlib.pyplot as plt 16 | import seaborn as sns 17 | 18 | from google.colab import drive 19 | drive.mount('/content/drive') 20 | 21 | #copied_path = "drive/My Drive/ML/Naive Bayes/data.csv" 22 | copied_path = "/content/drive/My Drive/ML/Naive Bayes/data.csv" 23 | data = pd.read_csv(copied_path) 24 | 25 | data.head() 26 | cols = [0] 27 | data.drop(data.columns[cols], inplace=True, axis=1) 28 | 29 | cols = list(data.columns.values) #Make a list of all of the columns in the df 30 | cols.pop(cols.index('City')) #Remove b from list 31 | cols.pop(cols.index('PCOS')) #Remove x from list 32 | cols.pop(cols.index('PCOS_from')) 33 | data = data[cols+['PCOS']] 34 | 35 | data['PCOS'] = data['PCOS'].map(dict(Yes = 1, No = 0)) 36 | 37 | data.head() 38 | 39 | x = data.drop('PCOS', axis = 1) 40 | y = data['PCOS'] 41 | 42 | x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) 43 | 44 | model=GaussianNB() 45 | model.fit(x_train, y_train) 46 | 47 | y_pred = model.predict(x_test) 48 | 49 | y_pred 50 | 51 | accuracy = accuracy_score(y_test, y_pred) * 100 52 | 53 | accuracy 54 | 55 | from sklearn.metrics import confusion_matrix 56 | 57 | results = confusion_matrix(y_test, y_pred) 58 | 59 | print('Confusion Matrix :') 60 | print(results) 61 | 62 | # import csv 63 | # import math 64 | # import random 65 | 66 | # def loadDataset(data): 67 | # dataset = data.values.tolist() 68 | # for x in range(len(dataset)-1): 69 | # for y in range(22): 70 | # dataset[x][y] = int(dataset[x][y]) 71 | 72 | # return dataset 73 | 74 | # def splitDataset(dataset, splitRatio): 75 | # trainSize = int(len(dataset) * splitRatio) 76 | # trainSet = [] 77 | # copy = dataset 78 | 79 | # while len(trainSet) < trainSize: 80 | # index = random.randrange(len(copy)) 81 | # trainSet.append(copy.pop(index)) 82 | 83 | # return [trainSet, copy] 84 | 85 | # def separateByClass(dataset): 86 | # separated = {} 87 | 88 | # for i in range(len(dataset)): 89 | # vector = dataset[i] 90 | # if (vector[-1] not in separated): 91 | # separated[vector[-1]] = [] 92 | # separated[vector[-1]].append(vector) 93 | 94 | # return separated 95 | 96 | # def mean(numbers): 97 | # print("mean", numbers) 98 | # return sum(numbers)/float(len(numbers)) 99 | 100 | # def stdev(numbers): 101 | # print(len(numbers)) 102 | # avg = mean(numbers) 103 | # variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers)-1) 104 | 105 | # return math.sqrt(variance) 106 | 107 | # def summarize(dataset): 108 | # print(dataset) 109 | # summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)] 110 | # del summaries[-1] 111 | 112 | # return summaries 113 | 114 | # def summarizeByClass(dataset): 115 | # separated = separateByClass(dataset) 116 | # summaries = {} 117 | 118 | # for classValue, instances in separated.items(): 119 | # summaries[classValue] = summarize(instances) 120 | # print("INS",isinstance) 121 | 122 | # return summaries 123 | 124 | # def calculateProbability(x, mean, stdev): 125 | # exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2)))) 126 | 127 | # return (1/(math.sqrt(2*math.pi)*stdev))*exponent 128 | 129 | # def calculateClassProbabilities(summaries, inputVector): 130 | # probabilities = {} 131 | 132 | # for classValue, classSummaries in summaries.items(): 133 | # probabilities[classValue] = 1 134 | # for i in range(len(classSummaries)): 135 | # mean, stdev = classSummaries[i] 136 | # x = inputVector[i] 137 | # probabilities[classValue] *= calculateProbability(x, mean, stdev) 138 | 139 | # return probabilities 140 | 141 | # def predict(summaries, inputVector): 142 | # probabilities = calculateClassProbabilities(summaries, inputVector) 143 | # bestLabel, bestProb = None, -1 144 | 145 | # for classValue, probability in probabilities.items(): 146 | # if bestLabel is None or probability > bestProb: 147 | # bestProb = probability 148 | # bestLabel = classValue 149 | 150 | # return bestLabel 151 | 152 | # def getPredictions(summaries, testSet): 153 | # predictions = [] 154 | 155 | # for i in range(len(testSet)): 156 | # result = predict(summaries, testSet[i]) 157 | # predictions.append(result) 158 | 159 | # return predictions 160 | 161 | # def getAccuracy(testSet, predictions): 162 | # correct = 0 163 | 164 | # for x in range(len(testSet)): 165 | # if testSet[x][-1] == predictions[x]: 166 | # correct += 1 167 | 168 | # return (correct/float(len(testSet)))*100.0 169 | 170 | # # Python script for confusion matrix creation. 171 | # from sklearn.metrics import confusion_matrix 172 | # from sklearn.metrics import accuracy_score 173 | # from sklearn.metrics import classification_report 174 | 175 | # def main(): 176 | # trainingSet=[] 177 | # testSet=[] 178 | # split = 0.67 179 | 180 | # dataset = loadDataset(data) 181 | 182 | # trainingSet, testSet = splitDataset(dataset, split) 183 | 184 | # print('Split {0} rows into train = {1} and test = {2} rows'.format(len(dataset),len(trainingSet),len(testSet))) 185 | 186 | # #prepare model 187 | 188 | # summaries = summarizeByClass(trainingSet) 189 | 190 | # #train model 191 | # predictions = getPredictions(summaries, testSet) 192 | # accuracy = getAccuracy(testSet, predictions) 193 | # print('Accuracy: {0}%'.format(accuracy)) 194 | 195 | # #test model 196 | # predictions = getPredictions(summaries, testSet) 197 | # accuracy = getAccuracy(testSet, predictions) 198 | # print('Accuracy: {0}%'.format(accuracy)) 199 | 200 | # actual = [] 201 | 202 | # for x in range(len(testSet)): 203 | # actual.append(testSet[x][-1]) 204 | 205 | # results = confusion_matrix(actual, predictions) 206 | 207 | # print('Confusion Matrix :') 208 | # print(results) 209 | # print('Accuracy Score :',accuracy_score(actual, predictions) ) 210 | # print('Report : ') 211 | # print(classification_report(actual, predictions) ) 212 | 213 | # main() -------------------------------------------------------------------------------- /Dataset/NoPCOS.csv: -------------------------------------------------------------------------------- 1 | ,Period Length,Cycle Length,Age,City,PCOS,PCOS_from,Overweight,loss weight gain / weight loss,irregular or missed periods,Difficulty in conceiving,Hair growth on Chin,Hair growth on Cheeks,Hair growth Between breasts,Hair growth on Upper lips ,Hair growth in Arms,Hair growth on Inner thighs,Acne or skin tags,Hair thinning or hair loss ,Dark patches,always tired,more Mood Swings,exercise per week,eat outside per week,canned food often,relocated city 2 | 0,5,6,2,mumbai,No,,0,0,1,0,1,0,0,1,0,0,1,1,0,0,0,0,4,0,0 3 | 1,5,2,2,mumbai,No,,0,1,0,0,1,1,1,2,2,1,0,1,0,1,1,1,1,0,0 4 | 2,5,4,2,mumbai,Yes,20.0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,1,0 5 | 3,5,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,1,0,0 6 | 4,5,2,2,thane,No,,0,0,1,0,1,0,1,1,1,0,1,0,1,1,1,0,4,0,0 7 | 5,5,4,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0 8 | 6,5,2,2,mumbai,No,,0,0,0,0,0,0,0,1,0,1,1,1,0,0,1,0,4,0,1 9 | 7,5,4,2,mumbai,No,,0,0,0,0,0,1,0,1,2,2,0,0,1,1,1,0,2,0,1 10 | 8,7,2,2,mumbai,Yes,20.0,0,0,0,0,1,0,0,0,1,1,1,1,0,0,1,3,2,0,0 11 | 9,3,4,2,mumbai,No,,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,0,4,0,1 12 | 10,5,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,3,4,0,0 13 | 11,7,6,2,mumbai,Yes,17.0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,1,2,7,0,1 14 | 12,5,4,2,mumbai,No,,0,1,1,0,0,0,0,0,1,0,1,1,1,1,1,0,4,0,0 15 | 13,5,6,4,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0 16 | 14,5,3,2,mumbai,Yes,21.0,1,1,1,0,0,0,0,0,0,0,1,1,0,0,1,4,1,0,0 17 | 15,5,3,2,thane,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0 18 | 16,7,4,2,pune,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,4,0,1 19 | 17,5,4,2,mumbai,No,,0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,4,0,0 20 | 18,5,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,1 21 | 19,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,2,2,0,0 22 | 20,3,4,2,mumbai,No,,0,0,0,0,0,0,0,1,1,1,1,1,1,0,1,0,3,0,0 23 | 21,7,3,1,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,2,0,0 24 | 22,5,6,2,thane,Yes,21.0,0,1,1,0,1,1,2,2,2,1,1,1,1,1,1,0,3,0,0 25 | 23,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,5,0,1 26 | 24,5,4,2,mumbai,Yes,20.0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,4,0,0 27 | 25,5,3,2,kalyan,No,,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,4,0,0 28 | 26,5,4,2,thane,No,,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,2,0,0 29 | 27,5,6,2,mumbai,Yes,17.0,0,0,1,0,0,0,1,0,1,1,0,1,0,1,1,3,4,0,0 30 | 28,5,4,2,thane,No,,0,1,1,0,2,2,2,2,2,2,1,1,1,1,1,4,3,0,0 31 | 29,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,7,0,0 32 | 30,3,3,3,jhajha,No,,0,0,1,0,0,0,0,0,1,0,1,1,0,1,1,3,0,0,0 33 | 31,5,1,2,mumbai,No,,0,0,0,0,0,0,0,1,1,0,0,0,1,1,1,3,3,0,0 34 | 32,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,3,1,0,0 35 | 33,5,3,7,mumbai,No,,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0 36 | 34,5,1,2,mumbai,No,,0,0,0,0,0,0,1,1,1,0,1,1,1,1,1,1,5,1,0 37 | 35,5,4,7,mumbai,Yes,14.0,1,1,1,0,0,1,0,1,0,0,1,0,1,1,1,0,1,0,0 38 | 36,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,3,0,0 39 | 37,9,5,3,patna,Yes,,0,1,1,0,0,0,1,0,0,0,0,1,0,1,1,0,2,0,1 40 | 38,9,5,3,patna,Yes,,0,1,1,0,0,0,1,0,0,0,0,1,0,1,1,0,2,0,1 41 | 39,7,6,2,mumbai,No,,1,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,5,0,0 42 | 40,5,4,1,mumbai,No,,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,3,1,0,0 43 | 41,7,3,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,5,0,0 44 | 42,3,3,2,dombivli,No,,0,1,1,0,0,0,0,0,1,0,1,1,1,1,1,0,2,0,0 45 | 43,5,4,2,mumbai,No,,0,0,0,0,0,1,0,1,1,0,0,1,0,1,1,3,2,0,0 46 | 44,3,3,7,mumbai,No,,0,1,0,0,0,0,0,1,1,1,0,1,0,1,1,5,6,0,0 47 | 45,5,4,2,kalyan,No,,0,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0 48 | 46,5,6,2,mumbai,No,,0,1,1,0,2,2,0,1,2,0,1,1,0,1,1,1,1,0,0 49 | 47,3,4,7,mumbai,No,,0,1,1,0,1,0,0,1,1,0,1,0,1,1,1,3,1,0,0 50 | 48,3,4,5,mumbai,No,36.0,1,1,1,1,0,0,0,0,0,0,1,1,0,1,1,3,0,0,0 51 | 49,5,6,3,navi mumbai,Yes,18.0,1,1,1,0,0,0,0,1,0,0,1,1,0,1,1,5,4,0,0 52 | 50,5,3,7,mumbai,No,,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,7,1,0,0 53 | 51,3,1,3,mumbai,No,,0,0,0,0,1,1,0,0,1,0,0,1,1,0,1,1,1,0,0 54 | 52,5,5,3,mumbai,Yes,18.0,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,0,2,0,0 55 | 53,5,1,7,mumbai,No,,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0 56 | 54,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0 57 | 55,7,4,7,mumbai,Yes,37.0,1,1,0,0,1,2,0,1,1,2,1,1,0,1,1,0,2,0,0 58 | 56,5,4,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0 59 | 57,3,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0 60 | 58,5,3,7,mumbai,No,,0,0,0,0,1,1,0,1,1,0,1,1,1,1,0,0,1,0,0 61 | 59,3,4,4,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0 62 | 60,5,3,2,bhopal,No,,0,1,0,0,0,0,0,1,1,0,0,1,1,1,1,0,2,0,0 63 | 61,3,3,2,patna,No,,0,0,0,0,0,0,1,0,1,0,1,1,1,0,1,0,7,1,1 64 | 62,3,3,2,patna,No,,0,0,0,0,0,0,1,0,1,0,1,1,1,0,1,0,7,1,1 65 | 63,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0 66 | 64,5,5,2,pune,No,,1,1,1,1,0,1,2,1,1,2,1,1,0,1,1,0,5,1,1 67 | 65,5,5,2,pune,No,,1,1,1,1,0,1,2,1,1,2,1,1,0,1,1,0,5,1,1 68 | 66,5,3,7,mumbai,No,,1,1,0,0,0,1,0,0,0,0,1,1,1,1,1,0,0,0,1 69 | 67,5,3,7,mumbai,No,,0,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,1 70 | 68,3,4,7,mumbai,No,,0,0,0,1,1,0,0,1,1,0,0,0,1,1,1,4,1,0,1 71 | 69,3,5,2,pune,Yes,20.0,1,1,1,1,2,2,1,0,2,2,1,1,1,1,1,0,3,0,1 72 | 70,5,6,7,mumbai,No,,0,1,1,0,1,0,0,0,0,0,0,1,1,0,1,2,2,0,0 73 | 71,9,6,2,mumbai,Yes,16.0,1,1,1,0,2,2,2,2,2,2,1,1,1,1,0,0,7,0,0 74 | 72,3,4,4,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0 75 | 73,5,1,2,thane,No,,0,0,0,0,1,0,0,1,1,0,1,0,1,0,0,3,3,0,0 76 | 74,3,3,2,mumbai,Yes,18.0,0,1,0,0,0,0,0,0,0,0,1,1,0,1,1,3,7,1,0 77 | 75,5,3,3,mumbai,No,,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,3,0,0 78 | 76,3,4,4,mumbai,Yes,30.0,1,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,1,0,0 79 | 77,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,2,0,0 80 | 78,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,2,1,0 81 | 79,3,3,3,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,1,1,0,0 82 | 80,5,6,3,mumbai,Yes,17.0,1,1,1,1,0,0,0,0,0,0,0,1,0,1,1,2,0,0,0 83 | 81,5,3,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,7,0,1 84 | 82,3,4,2,mumbai,Yes,22.0,0,1,1,0,0,0,0,0,0,1,1,0,0,1,0,5,4,0,0 85 | 83,3,4,7,"manama, bahrain",No,,0,1,1,0,0,1,0,1,0,0,0,1,0,0,0,2,2,0,0 86 | 84,5,3,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,5,2,0,0 87 | 85,7,3,2,mumbai,No,,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,4,2,0,0 88 | 86,5,3,1,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,2,0,0 89 | 87,5,1,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,1,0 90 | 88,5,4,2,mumbai,No,,0,1,0,0,0,0,0,1,1,1,1,1,1,0,1,0,3,0,0 91 | 89,3,4,2,mumbai,Yes,19.0,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,2,2,0,0 92 | 90,3,4,2,mumbai,Yes,19.0,1,1,1,0,0,0,0,0,0,0,1,1,1,1,0,2,2,0,0 93 | 91,7,1,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,1,1,0,0,3,0,0 94 | 92,5,3,4,bangalore,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,2,0,0 95 | 93,7,1,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0 96 | 94,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,6,0,0 97 | 95,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0 98 | 96,7,5,2,mumbai,No,,0,1,1,0,0,0,0,0,1,0,1,1,0,1,1,0,3,0,0 99 | 97,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,4,0,0 100 | 98,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,4,0,0 101 | 99,7,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 102 | 100,7,4,7,mumbai,No,,0,1,1,0,0,0,0,0,0,0,0,1,0,0,1,4,2,0,0 103 | 101,5,4,2,mumbai,No,,0,0,0,0,0,0,0,1,1,0,1,1,0,1,0,2,1,0,0 104 | 102,5,4,2,mumbai,No,,0,1,1,1,0,0,0,0,1,1,1,1,0,0,1,3,2,0,1 105 | 103,5,6,2,mumbai,Yes,23.0,1,1,1,1,1,0,0,1,0,0,1,1,1,1,1,0,7,0,1 106 | 104,5,6,4,mumbai,No,,0,1,1,0,0,0,0,0,0,0,0,1,0,1,1,2,2,1,0 107 | 105,3,3,7,mumbai,No,,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0 108 | 106,5,3,2,thane,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,2,0,0 109 | 107,5,4,7,mumbai,No,,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,5,1,0,0 110 | 108,7,1,7,pune,No,,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,2,1,0,1 111 | 109,5,4,7,dibai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0 112 | 110,3,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0 113 | 111,5,3,7,mumbai,Yes,,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 114 | 112,3,3,7,thane,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0 115 | 113,3,6,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0 116 | 114,5,3,3,mumbai,No,,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,0,3,0,0 117 | 115,5,3,2,panvel,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,4,0,0 118 | 116,5,6,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,5,0,1 119 | 117,5,3,3,canada,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,1,0,1 120 | 118,5,4,2,mumbai,No,,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0 121 | 119,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0 122 | 120,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,6,0,0 123 | 121,7,6,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,1,1,0,2,1,0,0 124 | 122,5,3,4,mumbai,Yes,32.0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,3,2,0,0 125 | 123,3,6,2,mumbai,Yes,21.0,1,1,1,0,2,2,0,2,2,2,1,1,0,1,1,3,1,0,1 126 | 124,7,4,4,mumbai,Yes,,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,0 127 | 125,3,5,2,thane,No,,0,0,1,0,1,0,1,1,1,0,1,1,0,0,1,3,1,0,0 128 | 126,5,1,3,mumbai,Yes,23.0,1,1,1,0,2,2,0,2,2,2,0,1,1,1,1,4,4,0,0 129 | 127,3,3,4,mumbai,No,,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 130 | 128,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,2,2,0,0 131 | 129,5,4,2,mumbai,No,,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0 132 | 130,5,4,3,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,2,0,0 133 | 131,5,3,2,mumbai,No,,0,0,1,0,0,0,0,1,1,1,1,1,1,0,0,0,2,0,0 134 | 132,7,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0 135 | 133,3,4,2,mumbai,Yes,,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,5,3,0,0 136 | 134,5,4,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,3,0,0 137 | 135,3,3,2,mumbai,No,,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,3,4,0,0 138 | 136,7,4,2,bangalore,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0 139 | 137,7,4,2,mumbai,No,,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,3,5,0,1 140 | 138,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,4,4,0,0 141 | 139,3,6,2,mumbai,Yes,20.0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,3,2,0,0 142 | 140,5,4,2,mumbai,No,,0,1,1,0,0,0,0,1,1,0,0,1,1,0,0,3,3,0,0 143 | 141,5,6,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,7,0,0 144 | 142,5,3,2,mumbai,No,,1,1,0,0,0,0,0,1,0,1,1,1,1,0,0,0,5,0,0 145 | 143,3,4,3,mumbai,No,,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,2,1,0,0 146 | 144,3,6,5,mumbai,No,,0,1,1,0,1,1,1,1,1,1,1,1,0,1,1,0,7,0,0 147 | 145,3,6,5,jaipur,Yes,28.0,1,1,1,0,2,1,0,2,1,1,0,1,1,1,1,0,1,0,1 148 | 146,5,6,2,mumbai,Yes,,0,0,1,0,0,0,1,0,1,0,1,1,0,1,0,4,3,0,0 149 | 147,5,6,2,mumbai,Yes,21.0,0,0,1,0,0,0,0,0,1,1,1,0,0,1,1,0,1,0,0 150 | 148,5,6,2,mumbai,No,,0,0,1,0,0,0,1,2,0,1,0,1,1,1,1,0,3,0,0 151 | 149,5,6,2,navi mumbai,Yes,18.0,0,1,1,0,1,1,1,2,2,2,1,1,1,1,1,1,6,0,0 152 | 150,5,6,2,mumbai,No,,0,1,1,0,1,1,1,1,1,1,0,0,1,0,0,2,0,0,0 153 | 151,3,3,7,maharashtra,No,,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,4,0,0 154 | 152,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,3,3,0,0 155 | 153,3,4,2,mumbai,No,,0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,5,0,0 156 | 154,5,4,2,mumbai,Yes,15.0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,1 157 | 155,5,1,4,mumbai,No,,0,1,0,0,0,0,0,0,0,0,1,1,1,1,1,0,1,0,0 158 | 156,5,3,2,mumbai,Yes,14.0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1 159 | 157,5,6,2,bangalore,Yes,17.0,1,1,1,0,0,0,0,1,0,0,0,1,0,0,0,2,6,0,1 160 | 158,3,4,2,mumbai,Yes,14.0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,2,0,0 161 | 159,5,4,2,mumbai,Yes,14.0,1,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,3,1,0 162 | 160,5,4,2,mumbai,No,,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,1,7,0,0 163 | 161,7,4,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0 164 | 162,5,1,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0 165 | 163,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6,1,1 166 | 164,3,1,7,mumbai,Yes,12.0,0,1,1,0,0,0,0,0,0,0,1,1,1,1,0,3,1,0,0 167 | 165,5,3,1,mumbai,Yes,19.0,1,1,1,0,0,0,0,0,0,0,1,1,1,0,0,0,4,0,1 168 | 166,5,3,3,mumbai,No,,0,0,0,0,0,0,0,1,1,1,0,1,0,1,1,0,1,0,0 169 | 167,5,6,3,bangalore,No,26.0,1,1,1,0,2,0,0,0,0,0,0,1,1,1,1,4,2,0,1 170 | 168,5,1,2,mumbai,No,,0,1,1,1,0,0,0,0,0,1,0,1,0,0,1,1,3,0,0 171 | 169,5,4,3,hyderabad,No,,1,1,1,0,1,1,0,2,0,0,0,1,0,1,1,0,2,0,1 172 | 170,5,3,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0 173 | 171,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0 174 | 172,5,4,2,mumbai,No,,0,0,1,0,0,0,0,0,2,2,1,1,1,1,0,0,1,0,0 175 | 173,7,3,2,thane,No,,0,1,0,0,0,0,2,2,2,0,1,0,0,0,0,3,6,0,1 176 | 174,5,1,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0 177 | 175,5,4,2,mumbai,No,,0,1,1,0,0,0,0,0,1,1,0,1,1,1,0,0,1,0,1 178 | 176,5,3,2,dombivli,No,,0,0,0,0,0,0,0,0,1,0,1,1,0,1,1,0,3,0,0 179 | 177,5,4,2,kalyan,No,,0,0,0,0,0,0,0,0,2,2,1,1,1,1,1,0,3,0,0 180 | 178,5,4,2,kolkata,Yes,15.0,1,1,1,0,0,0,1,1,0,0,0,0,0,0,0,3,2,0,0 181 | 179,5,1,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0 182 | 180,9,5,7,nagpur,Yes,12.0,0,1,0,1,2,1,2,2,0,0,1,1,1,1,1,0,0,0,0 183 | 181,5,6,2,navi mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,5,1,0,0 184 | 182,3,1,2,nagpur,No,,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0 185 | 183,5,6,2,mumbai,No,,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,1,7,0,1 186 | 184,3,4,2,bangalore,No,,0,1,1,0,0,0,0,0,1,0,0,1,0,0,1,0,2,0,0 187 | 185,3,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0 188 | 186,3,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0 189 | 187,5,4,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0 190 | 188,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,4,4,0,0 191 | 189,5,4,2,thane,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0 192 | 190,5,3,2,thane,No,,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,3,0,0 193 | 191,7,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,3,4,0,0 194 | 192,5,6,2,mumbai,No,,0,0,1,0,1,0,0,1,1,0,0,0,1,0,1,1,4,0,0 195 | 193,5,4,2,amravati,Yes,19.0,0,1,1,0,1,0,0,1,1,1,1,1,0,1,1,0,5,0,0 196 | 194,3,3,2,bangalore,No,,0,1,0,0,0,0,0,0,1,0,1,1,1,1,1,0,4,0,1 197 | 195,3,6,2,mumbai,Yes,17.0,1,1,1,0,0,0,0,1,1,0,1,1,1,1,1,4,5,0,0 198 | 196,5,3,2,mumbai,No,,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,5,4,0,0 199 | 197,5,6,5,palghar,No,,0,1,1,1,0,0,0,0,0,0,0,1,1,1,1,0,2,0,0 200 | 198,5,3,2,mumbai,No,,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,7,1,0,0 201 | 199,5,4,4,mumbai,Yes,,0,1,1,0,1,0,1,1,0,0,0,1,0,0,0,0,2,0,1 202 | 200,7,4,2,mumbai,No,,0,1,1,0,0,0,0,0,0,0,0,0,1,1,0,1,2,0,1 203 | 201,5,3,5,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,4,0,0 204 | 202,7,3,2,mumbai,No,,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,6,2,0,0 205 | 203,5,4,2,mumbai,Yes,16.0,1,1,1,0,0,0,0,0,0,0,1,1,0,1,0,3,5,0,0 206 | 204,5,4,7,mumbai,Yes,38.0,1,1,1,0,0,0,0,0,0,0,1,1,1,0,1,4,1,0,0 207 | 205,7,1,4,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0 208 | 206,5,1,2,mumbai,Yes,21.0,1,1,1,0,1,1,0,1,0,0,1,1,1,1,1,0,6,0,1 209 | 207,3,3,2,mumbai,No,,0,0,1,1,0,0,1,2,2,2,1,1,1,1,1,0,2,0,0 210 | 208,3,3,7,mumbai,No,,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,6,1,0,0 211 | 209,3,3,5,mumbai,No,,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,3,1,0,0 212 | 210,5,4,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,0 213 | 211,3,4,5,mumbai,No,,0,1,1,0,0,2,0,2,0,2,0,1,0,0,0,0,4,0,0 214 | 212,5,1,7,mumbai,No,,0,1,1,1,1,0,0,1,0,0,0,0,0,1,1,3,0,0,0 215 | 213,7,1,2,hyderabad,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0 216 | 214,5,3,7,dubai,Yes,40.0,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,0,0 217 | 215,7,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 218 | 216,7,6,3,mumbai,Yes,20.0,1,1,1,0,0,0,0,2,0,0,0,1,1,0,0,0,7,0,0 219 | 217,7,4,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,2,4,0,0 220 | 218,3,4,7,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,1,0,1,4,3,0,0 221 | 219,7,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0 222 | 220,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,5,0,0 223 | 221,5,1,7,mumbai,No,,0,1,1,0,0,0,0,0,1,1,1,0,0,0,0,3,1,0,0 224 | 222,5,6,7,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,5,1,0,0 225 | 223,5,6,7,mumbai,No,,1,1,1,0,0,0,0,0,0,0,1,1,0,1,0,2,1,0,0 226 | 224,7,1,1,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0 227 | 225,7,3,5,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0 228 | 226,3,3,7,mumbai,No,,0,1,1,0,0,2,1,0,0,1,1,1,0,1,1,2,0,0,0 229 | 227,5,6,2,kalyan,Yes,20.0,0,1,1,0,0,0,0,1,0,0,1,1,0,0,1,0,4,0,0 230 | 228,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0 231 | 229,3,6,2,hyderabad,No,,0,1,1,0,1,0,0,0,0,0,1,1,0,1,1,0,5,0,0 232 | 230,5,1,7,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,1,1,1,2,0,0 233 | 231,7,4,2,mumbai,No,,0,0,1,0,0,0,0,1,1,0,0,1,1,0,0,2,2,0,0 234 | 232,5,1,2,mumbai,No,,0,0,0,0,0,0,0,0,1,1,0,1,1,0,1,2,4,0,0 235 | 233,5,3,7,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,7,2,0,0 236 | 234,5,3,4,"thane, maharashtra",No,,0,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,3,0,0 237 | 235,5,6,2,mumba,No,,0,1,0,0,0,0,0,1,1,0,1,1,1,1,1,0,2,0,0 238 | 236,9,5,4,navi mumbai,Yes,26.0,0,1,1,1,2,0,0,2,1,1,1,1,1,1,1,2,0,0,0 239 | 237,7,3,2,thane,No,,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,5,0,0 240 | 238,5,3,3,mumbai,No,,0,0,0,0,0,0,1,1,1,0,0,1,1,0,0,4,4,0,0 241 | 239,3,5,2,thane,Yes,20.0,0,1,1,0,2,0,0,2,0,0,1,1,1,1,1,0,2,1,0 242 | 240,3,3,7,kharghar,No,,0,1,0,0,0,0,0,0,0,0,1,1,1,0,1,3,1,0,0 243 | 241,5,3,2,mumbai,Yes,20.0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0 244 | 242,3,4,4,thane,No,,0,0,0,0,0,0,0,1,1,1,0,1,0,1,1,0,1,0,0 245 | 243,5,3,7,mumbai,No,,1,1,1,0,0,0,0,0,1,1,0,1,1,1,1,5,3,0,0 246 | 244,3,3,3,mumbai,No,,0,1,1,0,1,1,0,1,1,1,1,1,0,1,1,0,3,0,0 247 | 245,3,1,3,mumbai,No,,0,1,0,0,0,0,0,0,0,0,1,1,1,1,1,0,3,0,0 248 | 246,5,4,1,mumbai,No,,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,2,0,0 249 | 247,3,4,7,mumbai,Yes,30.0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,2,2,0,0 250 | 248,9,6,2,mumbai,Yes,15.0,1,1,1,0,0,0,0,0,1,2,0,1,1,1,1,0,4,0,0 251 | 249,3,3,7,mumbai,No,,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0 252 | 250,7,3,3,mumbai,No,,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,5,1,0,0 253 | 251,3,3,2,dharwad,No,,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,3,0,1 254 | 252,5,4,4,ahmedabad,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,3,1,0,0 255 | 253,3,1,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,2,0,0 256 | 254,7,6,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0 257 | 255,5,4,3,mumbai,Yes,16.0,0,1,1,0,0,0,0,0,0,0,1,1,1,1,1,2,1,0,0 258 | 256,3,6,7,mumbai,Yes,28.0,0,0,1,1,1,1,1,1,1,1,0,1,0,1,0,2,0,0,0 259 | 257,5,3,7,thane,No,,0,1,0,0,0,0,0,0,0,1,1,1,1,1,1,0,2,0,0 260 | 258,7,3,7,mumbai,Yes,40.0,0,0,1,0,1,0,1,1,0,0,1,1,0,1,0,7,1,0,0 261 | 259,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,4,1,0,0 262 | 260,7,6,1,mumbai,Yes,14.0,1,1,1,0,0,0,0,0,1,0,1,0,1,1,1,0,2,0,0 263 | 261,3,3,2,mumbai,No,,0,1,0,0,0,0,1,0,1,2,1,1,1,1,1,0,3,0,0 264 | 262,7,3,5,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0 265 | 263,7,4,2,delhi,No,,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,1,6,0,0 266 | 264,5,3,2,udaipur,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0 267 | 265,7,1,2,mumbai,No,,0,0,0,0,1,1,1,2,2,2,1,1,0,1,0,5,4,1,1 268 | 266,5,3,4,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,3,1,0,0 269 | -------------------------------------------------------------------------------- /Dataset/OnlyPCOS.csv: -------------------------------------------------------------------------------- 1 | Period Length,Cycle Length,Age,City,PCOS,PCOS_from,Overweight,loss weight gain / weight loss,irregular or missed periods,Difficulty in conceiving,Hair growth on Chin,Hair growth on Cheeks,Hair growth Between breasts,Hair growth on Upper lips ,Hair growth in Arms,Hair growth on Inner thighs,Acne or skin tags,Hair thinning or hair loss ,Dark patches,always tired,more Mood Swings,exercise per week,eat outside per week,canned food often,relocated city 2 | 4-5 days,29-35 days,18-25,mumbai,Yes,20,Yes,Yes,Yes,Yes,moderate,moderate,moderate,moderate,moderate,moderate,Yes,Yes,Yes,Yes,Yes,1,4,Yes,No 3 | 6-7 days,20-28 days,18-25,mumbai,Yes,20,No,No,No,Not Applicable,moderate,normal,normal,normal,moderate,moderate,Yes,Yes,No,No,Yes,3,2,No,No 4 | 6-7 days,Keeps Variating,18-25,mumbai,Yes,17,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,No,Yes,No,Yes,2,7,No,Yes 5 | 4-5 days,25-28,18-25,mumbai,Yes,21,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,Yes,4,1,No,No 6 | 4-5 days,Keeps Variating,18-25,thane,Yes,21,Maybe,Yes,Yes,Not Applicable,moderate,moderate,excessive,excessive,excessive,moderate,Yes,Yes,Yes,Yes,Yes,0,3,No,No 7 | 4-5 days,29-35 days,18-25,mumbai,Yes,20,Yes,Yes,Yes,Not Applicable,moderate,moderate,moderate,moderate,moderate,moderate,Yes,Yes,Yes,Yes,Yes,0,4,No,No 8 | 4-5 days,Keeps Variating,18-25,mumbai,Yes,17,No,No,Yes,Not Applicable,normal,normal,moderate,normal,moderate,moderate,No,Yes,No,Yes,Yes,3,4,No,No 9 | 4-5 days,29-35 days,40-45,mumbai,Yes,14,Yes,Yes,Yes,Not Applicable,normal,moderate,normal,moderate,normal,normal,Yes,No,Yes,Yes,Yes,0,1,No,No 10 | 2-3 days,29-35 days,36-40,mumbai,No,36,Yes,Yes,Yes,Yes,normal,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,Yes,3,0,No,No 11 | 4-5 days,Keeps Variating,26-30,navi mumbai,Yes,18,Yes,Yes,Yes,Not Applicable,normal,normal,normal,moderate,normal,normal,Yes,Yes,No,Yes,Yes,5,4,No,No 12 | 4-5 days,36+ days,26-30,mumbai,Yes,18,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,Yes,Yes,Yes,0,2,No,No 13 | 6-7 days,29-35 days,Above 45,mumbai,Yes,37,Yes,Yes,Not Applicable,Not Applicable,moderate,excessive,normal,moderate,moderate,excessive,Yes,Yes,No,Yes,Yes,0,2,No,No 14 | 2-3 days,36+ days,18-25,pune,Yes,20,Yes,Yes,Yes,Yes,excessive,excessive,moderate,normal,excessive,excessive,Yes,Yes,Yes,Yes,Yes,0,3,No,Yes 15 | More than 7 days,Keeps Variating,18-25,mumbai,Yes,16,Yes,Yes,Yes,Not Applicable,excessive,excessive,excessive,excessive,excessive,excessive,Yes,Yes,Yes,Yes,No,0,7,No,No 16 | 2-3 days,25-28,18-25,mumbai,Yes,18,No,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,Yes,3,7,Yes,No 17 | 2-3 days,29-35 days,31-35,mumbai,Yes,30,Yes,Yes,Yes,Yes,normal,normal,normal,moderate,moderate,moderate,No,No,No,No,Yes,1,1,No,No 18 | 4-5 days,Keeps Variating,26-30,mumbai,Yes,17,Yes,Yes,Yes,Yes,normal,normal,normal,normal,normal,normal,No,Yes,No,Yes,Yes,2,0,No,No 19 | 2-3 days,29-35 days,18-25,mumbai,Yes,22,Maybe,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,moderate,Yes,No,No,Yes,No,5,4,No,No 20 | 2-3 days,29-35 days,18-25,mumbai,Yes,19,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,2,2,No,No 21 | 2-3 days,29-35 days,18-25,mumbai,Yes,19,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,No,2,2,No,No 22 | 4-5 days,Keeps Variating,18-25,mumbai,Yes,23,Yes,Yes,Yes,Yes,moderate,normal,normal,moderate,normal,normal,Yes,Yes,Yes,Yes,Yes,0,7,No,Yes 23 | 4-5 days,25-28,31-35,mumbai,Yes,32,Yes,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,Yes,Yes,3,2,No,No 24 | 2-3 days,Keeps Variating,18-25,mumbai,Yes,21,Yes,Yes,Yes,Not Applicable,excessive,excessive,normal,excessive,excessive,excessive,Yes,Yes,No,Yes,Yes,3,1,No,Yes 25 | 4-5 days,20-24 days,26-30,mumbai,Yes,23,Yes,Yes,Yes,Not Applicable,excessive,excessive,normal,excessive,excessive,excessive,No,Yes,Yes,Yes,Yes,4,4,No,No 26 | 2-3 days,Keeps Variating,18-25,mumbai,Yes,20,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,Yes,Yes,Yes,3,2,No,No 27 | 2-3 days,Keeps Variating,36-40,jaipur,Yes,28,Yes,Yes,Yes,No,excessive,moderate,normal,excessive,moderate,moderate,No,Yes,Yes,Yes,Yes,0,1,No,Yes 28 | 4-5 days,Keeps Variating,18-25,mumbai,Yes,21,No,No,Yes,Not Applicable,normal,normal,normal,normal,moderate,moderate,Yes,No,No,Yes,Yes,0,1,No,No 29 | 4-5 days,Keeps Variating,18-25,navi mumbai,Yes,18,No,Yes,Yes,Not Applicable,moderate,moderate,moderate,excessive,excessive,excessive,Yes,Yes,Yes,Yes,Yes,1,6,No,No 30 | 4-5 days,29-35 days,18-25,mumbai,Yes,15,No,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,0,2,No,Yes 31 | 4-5 days,25-28,18-25,mumbai,Yes,14,No,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,Yes,No,0,2,No,Yes 32 | 4-5 days,Keeps Variating,18-25,bangalore,Yes,17,Yes,Yes,Yes,Not Applicable,normal,normal,normal,moderate,normal,normal,No,Yes,No,No,No,2,6,No,Yes 33 | 2-3 days,29-35 days,18-25,mumbai,Yes,14,Yes,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,Yes,Yes,Yes,0,2,No,No 34 | 4-5 days,29-35 days,18-25,mumbai,Yes,14,Yes,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,0,3,Yes,No 35 | 2-3 days,20-24 days,Above 45,mumbai,Yes,12,No,Yes,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,No,3,1,No,No 36 | 4-5 days,25-28,Below 18,mumbai,Yes,19,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,No,No,0,4,No,Yes 37 | 4-5 days,Keeps Variating,26-30,bangalore,No,26,Yes,Yes,Yes,Not Applicable,excessive,normal,normal,normal,normal,normal,No,Yes,Yes,Yes,Yes,4,2,No,Yes 38 | 4-5 days,29-35 days,18-25,kolkata,Yes,15,Yes,Yes,Yes,Not Applicable,normal,normal,moderate,moderate,normal,normal,No,No,No,No,No,3,2,No,No 39 | More than 7 days,36+ days,Above 45,nagpur,Yes,12,Maybe,Yes,No,Yes,excessive,moderate,excessive,excessive,normal,normal,Yes,Yes,Yes,Yes,Yes,0,0,No,No 40 | 4-5 days,29-35 days,18-25,amravati,Yes,19,Maybe,Yes,Yes,Not Applicable,moderate,normal,normal,moderate,moderate,moderate,Yes,Yes,No,Yes,Yes,0,5,No,No 41 | 2-3 days,Keeps Variating,18-25,mumbai,Yes,17,Yes,Yes,Yes,Not Applicable,normal,normal,normal,moderate,moderate,normal,Yes,Yes,Yes,Yes,Yes,4,5,No,No 42 | 4-5 days,29-35 days,18-25,mumbai,Yes,16,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,No,3,5,No,No 43 | 4-5 days,29-35 days,40-45,mumbai,Yes,38,Yes,Yes,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,No,Yes,4,1,No,No 44 | 4-5 days,20-24 days,18-25,mumbai,Yes,21,Yes,Yes,Yes,Not Applicable,moderate,moderate,normal,moderate,normal,normal,Yes,Yes,Yes,Yes,Yes,0,6,No,Yes 45 | 4-5 days,25-28,40-45,dubai,Yes,40,Yes,Yes,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,0,1,No,No 46 | 6-7 days,Keeps Variating,26-30,mumbai,Yes,20,Yes,Yes,Yes,Not Applicable,normal,normal,normal,excessive,normal,normal,No,Yes,Yes,No,No,0,7,No,No 47 | 4-5 days,Keeps Variating,18-25,kalyan,Yes,20,No,Yes,Yes,Not Applicable,normal,normal,normal,moderate,normal,normal,Yes,Yes,No,No,Yes,0,4,No,No 48 | More than 7 days,36+ days,31-35,navi mumbai,Yes,26,Maybe,Yes,Yes,Yes,excessive,normal,normal,excessive,moderate,moderate,Yes,Yes,Yes,Yes,Yes,2,0,No,No 49 | 2-3 days,36+ days,18-25,thane,Yes,20,No,Yes,Yes,No,excessive,normal,normal,excessive,normal,normal,Yes,Yes,Yes,Yes,Yes,0,2,Yes,No 50 | 4-5 days,25-28,18-25,mumbai,Yes,20,No,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,No,Yes,No,No,1,1,No,No 51 | 2-3 days,29-35 days,40-45,mumbai,Yes,30,Yes,Yes,No,Not Applicable,normal,normal,normal,normal,normal,moderate,Yes,Yes,No,Yes,No,2,2,No,No 52 | More than 7 days,Keeps Variating,18-25,mumbai,Yes,15,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,moderate,excessive,No,Yes,Yes,Yes,Yes,0,4,No,No 53 | 4-5 days,29-35 days,26-30,mumbai,Yes,16,No,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,2,1,No,No 54 | 2-3 days,Keeps Variating,40-45,mumbai,Yes,28,Maybe,No,Yes,Yes,moderate,moderate,moderate,moderate,moderate,moderate,No,Yes,No,Yes,No,2,0,No,No 55 | 6-7 days,25-28,Above 45,mumbai,Yes,40,No,No,Yes,Not Applicable,moderate,normal,moderate,moderate,normal,normal,Yes,Yes,No,Yes,No,7,1,No,No 56 | 6-7 days,Keeps Variating,Below 18,mumbai,Yes,14,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,moderate,normal,Yes,No,Yes,Yes,Yes,0,2,No,No 57 | -------------------------------------------------------------------------------- /Dataset/allData.csv: -------------------------------------------------------------------------------- 1 | Period Length,Cycle Length,Age,City,PCOS,PCOS_from,Overweight,loss weight gain / weight loss,irregular or missed periods,Difficulty in conceiving,Hair growth on Chin,Hair growth on Cheeks,Hair growth Between breasts,Hair growth on Upper lips ,Hair growth in Arms,Hair growth on Inner thighs,Acne or skin tags,Hair thinning or hair loss ,Dark patches,always tired,more Mood Swings,exercise per week,eat outside per week,canned food often,relocated city 2 | 4-5 days,Keeps Variating,18-25,mumbai,No,,,No,Yes,Not Applicable,moderate,normal,normal,moderate,normal,normal,Yes,Yes,No,No,No,0,4,No,No 3 | 4-5 days,20-28 days,18-25,mumbai,No,,No,Yes,No,Not Applicable,moderate,moderate,moderate,excessive,excessive,moderate,No,Yes,No,Yes,Yes,1,1,No,No 4 | 4-5 days,29-35 days,18-25,mumbai,Yes,20,Yes,Yes,Yes,Yes,moderate,moderate,moderate,moderate,moderate,moderate,Yes,Yes,Yes,Yes,Yes,1,4,Yes,No 5 | 4-5 days,29-35 days,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,Yes,No,5,1,No,No 6 | 4-5 days,20-28 days,18-25,thane,No,,,No,Yes,Not Applicable,moderate,normal,moderate,moderate,moderate,normal,Yes,No,Yes,Yes,Yes,0,4,No,No 7 | 4-5 days,29-35 days,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,0,2,No,No 8 | 4-5 days,20-28 days,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,moderate,normal,moderate,Yes,Yes,No,No,Yes,0,4,No,Yes 9 | 4-5 days,29-35 days,18-25,mumbai,No,,No,No,No,Not Applicable,normal,moderate,normal,moderate,excessive,excessive,No,No,Yes,Yes,Yes,0,2,No,Yes 10 | 6-7 days,20-28 days,18-25,mumbai,Yes,20,No,No,No,Not Applicable,moderate,normal,normal,normal,moderate,moderate,Yes,Yes,No,No,Yes,3,2,No,No 11 | 2-3 days,29-35 days,18-25,mumbai,No,,Yes,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,0,4,No,Yes 12 | 4-5 days,29-35 days,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,3,4,No,No 13 | 6-7 days,Keeps Variating,18-25,mumbai,Yes,17,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,No,Yes,No,Yes,2,7,No,Yes 14 | 4-5 days,29-35 days,18-25,mumbai,No,,Maybe,Yes,Yes,Not Applicable,normal,normal,normal,normal,moderate,normal,Yes,Yes,Yes,Yes,Yes,0,4,No,No 15 | 4-5 days,Keeps Variating,31-35,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,2,No,No 16 | 4-5 days,25-28,18-25,mumbai,Yes,21,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,Yes,4,1,No,No 17 | 4-5 days,25-28,18-25,thane,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,0,4,No,No 18 | 6-7 days,29-35 days,18-25,pune,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,3,4,No,Yes 19 | 4-5 days,29-35 days,18-25,mumbai,No,,,Yes,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,No,Yes,0,4,No,No 20 | 4-5 days,29-35 days,18-25,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,Yes,No,No,No,No,0,7,No,Yes 21 | 2-3 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,No,No,2,2,No,No 22 | 2-3 days,29-35 days,18-25,mumbai,No,,No,No,No,No,normal,normal,normal,moderate,moderate,moderate,Yes,Yes,Yes,No,Yes,0,3,No,No 23 | 6-7 days,25-28,Below 18,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,Yes,Yes,0,2,No,No 24 | 4-5 days,Keeps Variating,18-25,thane,Yes,21,Maybe,Yes,Yes,Not Applicable,moderate,moderate,excessive,excessive,excessive,moderate,Yes,Yes,Yes,Yes,Yes,0,3,No,No 25 | 2-3 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,3,5,No,Yes 26 | 4-5 days,29-35 days,18-25,mumbai,Yes,20,Yes,Yes,Yes,Not Applicable,moderate,moderate,moderate,moderate,moderate,moderate,Yes,Yes,Yes,Yes,Yes,0,4,No,No 27 | 4-5 days,25-28,18-25,kalyan,No,,,No,No,Yes,normal,normal,normal,normal,moderate,normal,No,Yes,No,No,No,0,4,No,No 28 | 4-5 days,29-35 days,18-25,thane,No,,No,No,Not Applicable,Not Applicable,normal,normal,normal,normal,moderate,moderate,Yes,Yes,Yes,Yes,Yes,0,2,No,No 29 | 4-5 days,Keeps Variating,18-25,mumbai,Yes,17,No,No,Yes,Not Applicable,normal,normal,moderate,normal,moderate,moderate,No,Yes,No,Yes,Yes,3,4,No,No 30 | 4-5 days,29-35 days,18-25,thane,No,,,Yes,Yes,Not Applicable,excessive,excessive,excessive,excessive,excessive,excessive,Yes,Yes,Yes,Yes,Yes,4,3,No,No 31 | 4-5 days,25-28,18-25,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,Yes,0,7,No,No 32 | 2-3 days,25-28,26-30,jhajha,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,moderate,normal,Yes,Yes,No,Yes,Yes,3,0,No,No 33 | 4-5 days,20-24 days,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,moderate,moderate,normal,No,No,Yes,Yes,Yes,3,3,No,No 34 | 4-5 days,25-28,40-45,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,3,1,No,No 35 | 4-5 days,25-28,40-45,mumbai,No,,Yes,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,0,2,No,No 36 | 4-5 days,20-24 days,18-25,mumbai,No,,Maybe,No,No,Not Applicable,normal,normal,moderate,moderate,moderate,normal,Yes,Yes,Yes,Yes,Yes,1,5,Yes,No 37 | 4-5 days,29-35 days,40-45,mumbai,Yes,14,Yes,Yes,Yes,Not Applicable,normal,moderate,normal,moderate,normal,normal,Yes,No,Yes,Yes,Yes,0,1,No,No 38 | 4-5 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,moderate,normal,No,Yes,Yes,No,No,1,3,No,No 39 | More than 7 days,36+ days,26-30,patna,Yes,,No,Yes,Yes,No,normal,normal,moderate,normal,normal,normal,No,Yes,No,Yes,Yes,0,2,No,Yes 40 | More than 7 days,36+ days,26-30,patna,Yes,,No,Yes,Yes,No,normal,normal,moderate,normal,normal,normal,No,Yes,No,Yes,Yes,0,2,No,Yes 41 | 6-7 days,Keeps Variating,18-25,mumbai,No,,Yes,Yes,Yes,Not Applicable,normal,normal,normal,moderate,normal,normal,No,No,No,No,Yes,0,5,No,No 42 | 4-5 days,29-35 days,Below 18,mumbai,No,,No,No,No,Not Applicable,normal,normal,normal,normal,moderate,normal,No,No,No,Yes,Yes,3,1,No,No 43 | 6-7 days,25-28,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,4,5,No,No 44 | 2-3 days,25-28,18-25,dombivli,No,,,Yes,Yes,Not Applicable,normal,normal,normal,normal,moderate,normal,Yes,Yes,Yes,Yes,Yes,0,2,No,No 45 | 4-5 days,29-35 days,18-25,mumbai,No,,Maybe,No,No,Not Applicable,normal,moderate,normal,moderate,moderate,normal,No,Yes,No,Yes,Yes,3,2,No,No 46 | 2-3 days,25-28,40-45,mumbai,No,,Maybe,Yes,No,No,normal,normal,normal,moderate,moderate,moderate,No,Yes,No,Yes,Yes,5,6,No,No 47 | 4-5 days,29-35 days,18-25,kalyan,No,,,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,Yes,No,0,3,No,No 48 | 4-5 days,Keeps Variating,18-25,mumbai,No,,Maybe,Yes,Yes,Not Applicable,excessive,excessive,normal,moderate,excessive,normal,Yes,Yes,No,Yes,Yes,1,1,No,No 49 | 2-3 days,29-35 days,Above 45,mumbai,No,,,Yes,Yes,No,moderate,normal,normal,moderate,moderate,normal,Yes,No,Yes,Yes,Yes,3,1,No,No 50 | 2-3 days,29-35 days,36-40,mumbai,No,36,Yes,Yes,Yes,Yes,normal,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,Yes,3,0,No,No 51 | 4-5 days,Keeps Variating,26-30,navi mumbai,Yes,18,Yes,Yes,Yes,Not Applicable,normal,normal,normal,moderate,normal,normal,Yes,Yes,No,Yes,Yes,5,4,No,No 52 | 4-5 days,25-28,Above 45,mumbai,No,,No,No,No,No,moderate,normal,normal,normal,normal,normal,No,Yes,No,No,No,7,1,No,No 53 | 2-3 days,20-24 days,26-30,mumbai,No,,,No,No,Not Applicable,moderate,moderate,normal,normal,moderate,normal,No,Yes,Yes,No,Yes,1,1,No,No 54 | 4-5 days,36+ days,26-30,mumbai,Yes,18,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,Yes,Yes,Yes,0,2,No,No 55 | 4-5 days,20-24 days,Above 45,mumbai,No,,Maybe,Yes,No,Yes,normal,normal,normal,normal,normal,normal,No,No,No,No,Yes,3,1,No,No 56 | 4-5 days,25-28,Above 45,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,5,1,No,No 57 | 6-7 days,29-35 days,Above 45,mumbai,Yes,37,Yes,Yes,Not Applicable,Not Applicable,moderate,excessive,normal,moderate,moderate,excessive,Yes,Yes,No,Yes,Yes,0,2,No,No 58 | 4-5 days,29-35 days,Above 45,mumbai,No,,,No,Not Applicable,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,6,1,No,No 59 | 2-3 days,29-35 days,18-25,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,4,No,No 60 | 4-5 days,25-28,Above 45,mumbai,No,,,No,No,Not Applicable,moderate,moderate,normal,moderate,moderate,normal,Yes,Yes,Yes,Yes,No,0,1,No,No 61 | 2-3 days,29-35 days,31-35,mumbai,No,,No,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,No,0,2,No,No 62 | 4-5 days,25-28,18-25,bhopal,No,,No,Yes,Not Applicable,Not Applicable,normal,normal,normal,moderate,moderate,normal,No,Yes,Yes,Yes,Yes,0,2,No,No 63 | 2-3 days,25-28,18-25,patna,No,,No,No,No,No,normal,normal,moderate,normal,moderate,normal,Yes,Yes,Yes,No,Yes,0,7,Yes,Yes 64 | 2-3 days,25-28,18-25,patna,No,,No,No,No,No,normal,normal,moderate,normal,moderate,normal,Yes,Yes,Yes,No,Yes,0,7,Yes,Yes 65 | 4-5 days,25-28,Above 45,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,2,1,No,No 66 | 4-5 days,36+ days,18-25,pune,No,,Yes,Yes,Yes,Yes,normal,moderate,excessive,moderate,moderate,excessive,Yes,Yes,No,Yes,Yes,0,5,Yes,Yes 67 | 4-5 days,36+ days,18-25,pune,No,,Yes,Yes,Yes,Yes,normal,moderate,excessive,moderate,moderate,excessive,Yes,Yes,No,Yes,Yes,0,5,Yes,Yes 68 | 4-5 days,25-28,Above 45,mumbai,No,,Yes,Yes,No,No,normal,moderate,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,0,0,No,Yes 69 | 4-5 days,25-28,Above 45,mumbai,No,,,Yes,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,1,0,No,Yes 70 | 2-3 days,29-35 days,Above 45,mumbai,No,,No,No,No,Yes,moderate,normal,normal,moderate,moderate,normal,No,No,Yes,Yes,Yes,4,1,No,Yes 71 | 2-3 days,36+ days,18-25,pune,Yes,20,Yes,Yes,Yes,Yes,excessive,excessive,moderate,normal,excessive,excessive,Yes,Yes,Yes,Yes,Yes,0,3,No,Yes 72 | 4-5 days,Keeps Variating,Above 45,mumbai,No,,,Yes,Yes,No,moderate,normal,normal,normal,normal,normal,No,Yes,Yes,No,Yes,2,2,No,No 73 | More than 7 days,Keeps Variating,18-25,mumbai,Yes,16,Yes,Yes,Yes,Not Applicable,excessive,excessive,excessive,excessive,excessive,excessive,Yes,Yes,Yes,Yes,No,0,7,No,No 74 | 2-3 days,29-35 days,31-35,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,3,2,No,No 75 | 4-5 days,20-24 days,18-25,thane,No,,No,No,No,Not Applicable,moderate,normal,normal,moderate,moderate,normal,Yes,No,Yes,No,No,3,3,No,No 76 | 2-3 days,25-28,18-25,mumbai,Yes,18,No,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,Yes,3,7,Yes,No 77 | 4-5 days,25-28,26-30,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,moderate,normal,Yes,No,No,Yes,Yes,0,3,No,No 78 | 2-3 days,29-35 days,31-35,mumbai,Yes,30,Yes,Yes,Yes,Yes,normal,normal,normal,moderate,moderate,moderate,No,No,No,No,Yes,1,1,No,No 79 | 4-5 days,25-28,18-25,mumbai,No,,No,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,0,2,No,No 80 | 4-5 days,25-28,18-25,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,Yes,0,2,Yes,No 81 | 2-3 days,25-28,26-30,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,No,1,1,No,No 82 | 4-5 days,Keeps Variating,26-30,mumbai,Yes,17,Yes,Yes,Yes,Yes,normal,normal,normal,normal,normal,normal,No,Yes,No,Yes,Yes,2,0,No,No 83 | 4-5 days,25-28,18-25,mumbai,No,,No,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,0,7,No,Yes 84 | 2-3 days,29-35 days,18-25,mumbai,Yes,22,Maybe,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,moderate,Yes,No,No,Yes,No,5,4,No,No 85 | 2-3 days,29-35 days,Above 45,"manama, bahrain",No,,,Yes,Yes,No,normal,moderate,normal,moderate,normal,normal,No,Yes,No,No,No,2,2,No,No 86 | 4-5 days,25-28,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,5,2,No,No 87 | 6-7 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,moderate,moderate,Yes,Yes,No,No,No,4,2,No,No 88 | 4-5 days,25-28,Below 18,mumbai,No,,No,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,No,Yes,0,2,No,No 89 | 4-5 days,20-24 days,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,5,Yes,No 90 | 4-5 days,29-35 days,18-25,mumbai,No,,No,Yes,No,Not Applicable,normal,normal,normal,moderate,moderate,moderate,Yes,Yes,Yes,No,Yes,0,3,No,No 91 | 2-3 days,29-35 days,18-25,mumbai,Yes,19,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,2,2,No,No 92 | 2-3 days,29-35 days,18-25,mumbai,Yes,19,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,No,2,2,No,No 93 | 6-7 days,20-24 days,18-25,mumbai,No,,No,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,No,0,3,No,No 94 | 4-5 days,25-28,31-35,bangalore,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,3,2,No,No 95 | 6-7 days,20-24 days,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,Yes,No,0,0,No,No 96 | 4-5 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,Yes,0,6,No,No 97 | 2-3 days,25-28,18-25,mumbai,No,,,No,Not Applicable,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,5,1,No,No 98 | 6-7 days,36+ days,18-25,mumbai,No,,,Yes,Yes,Not Applicable,normal,normal,normal,normal,moderate,normal,Yes,Yes,No,Yes,Yes,0,3,No,No 99 | 2-3 days,25-28,18-25,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,No,Yes,Yes,No,3,4,No,No 100 | 2-3 days,25-28,18-25,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,No,Yes,Yes,No,3,4,No,No 101 | 6-7 days,25-28,Above 45,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,0,No,No 102 | 6-7 days,29-35 days,Above 45,mumbai,No,,No,Yes,Yes,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,4,2,No,No 103 | 4-5 days,29-35 days,18-25,mumbai,No,,No,No,No,No,normal,normal,normal,moderate,moderate,normal,Yes,Yes,No,Yes,No,2,1,No,No 104 | 4-5 days,29-35 days,18-25,mumbai,No,,,Yes,Yes,Yes,normal,normal,normal,normal,moderate,moderate,Yes,Yes,No,No,Yes,3,2,No,Yes 105 | 4-5 days,Keeps Variating,18-25,mumbai,Yes,23,Yes,Yes,Yes,Yes,moderate,normal,normal,moderate,normal,normal,Yes,Yes,Yes,Yes,Yes,0,7,No,Yes 106 | 4-5 days,Keeps Variating,31-35,mumbai,No,,Maybe,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,Yes,Yes,2,2,Yes,No 107 | 2-3 days,25-28,Above 45,mumbai,No,,,Yes,Not Applicable,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,Yes,Yes,3,0,No,No 108 | 4-5 days,25-28,18-25,thane,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,Yes,5,2,No,No 109 | 4-5 days,29-35 days,Above 45,mumbai,No,,No,Yes,Not Applicable,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,No,No,No,No,5,1,No,No 110 | 6-7 days,20-24 days,Above 45,pune,No,,Maybe,Yes,Yes,No,normal,normal,normal,normal,normal,normal,Yes,No,Yes,No,No,2,1,No,Yes 111 | 4-5 days,29-35 days,Above 45,dibai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,Yes,0,1,No,No 112 | 2-3 days,25-28,Above 45,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,2,2,No,No 113 | 4-5 days,25-28,Above 45,mumbai,Yes,,,Yes,Not Applicable,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,Yes,No,No,1,0,No,No 114 | 2-3 days,25-28,Above 45,thane,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,2,No,No 115 | 2-3 days,Keeps Variating,18-25,mumbai,No,,No,No,Not Applicable,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,2,2,No,No 116 | 4-5 days,25-28,26-30,mumbai,No,,,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,Yes,Yes,0,3,No,No 117 | 4-5 days,25-28,18-25,panvel,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,7,4,No,No 118 | 4-5 days,Keeps Variating,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,Yes,Yes,0,5,No,Yes 119 | 4-5 days,25-28,26-30,canada,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,1,1,No,Yes 120 | 4-5 days,29-35 days,18-25,mumbai,No,,,No,Yes,Yes,normal,normal,normal,normal,normal,normal,No,No,No,No,No,0,3,No,No 121 | 4-5 days,25-28,Above 45,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,5,1,No,No 122 | 4-5 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,3,6,No,No 123 | 6-7 days,Keeps Variating,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,No,2,1,No,No 124 | 4-5 days,25-28,31-35,mumbai,Yes,32,Yes,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,Yes,Yes,3,2,No,No 125 | 2-3 days,Keeps Variating,18-25,mumbai,Yes,21,Yes,Yes,Yes,Not Applicable,excessive,excessive,normal,excessive,excessive,excessive,Yes,Yes,No,Yes,Yes,3,1,No,Yes 126 | 6-7 days,29-35 days,31-35,mumbai,Yes,,No,No,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,Yes,0,1,No,No 127 | 2-3 days,36+ days,18-25,thane,No,,No,No,Yes,Not Applicable,moderate,normal,moderate,moderate,moderate,normal,Yes,Yes,No,No,Yes,3,1,No,No 128 | 4-5 days,20-24 days,26-30,mumbai,Yes,23,Yes,Yes,Yes,Not Applicable,excessive,excessive,normal,excessive,excessive,excessive,No,Yes,Yes,Yes,Yes,4,4,No,No 129 | 2-3 days,25-28,31-35,mumbai,No,,No,No,No,Yes,normal,normal,normal,normal,normal,normal,Yes,No,No,No,No,0,0,No,No 130 | 4-5 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,moderate,Yes,Yes,No,No,No,2,2,No,No 131 | 4-5 days,29-35 days,18-25,mumbai,No,,No,No,Yes,Not Applicable,normal,normal,normal,normal,moderate,normal,No,No,No,No,No,3,1,No,No 132 | 4-5 days,29-35 days,26-30,mumbai,No,,,No,Not Applicable,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,No,No,No,No,3,2,No,No 133 | 4-5 days,25-28,18-25,mumbai,No,,No,No,Yes,Not Applicable,normal,normal,normal,moderate,moderate,moderate,Yes,Yes,Yes,No,No,0,2,No,No 134 | 6-7 days,29-35 days,18-25,mumbai,No,,No,No,Not Applicable,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,No,0,2,No,No 135 | 2-3 days,29-35 days,18-25,mumbai,Yes,,Yes,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,5,3,No,No 136 | 4-5 days,29-35 days,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,Yes,No,No,0,3,No,No 137 | 2-3 days,25-28,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,moderate,normal,normal,No,Yes,No,No,Yes,3,4,No,No 138 | 6-7 days,29-35 days,18-25,bangalore,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,No,0,2,No,No 139 | 6-7 days,29-35 days,18-25,mumbai,No,,Yes,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,Yes,Yes,Yes,3,5,No,Yes 140 | 4-5 days,25-28,18-25,mumbai,No,,No,No,Not Applicable,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,No,4,4,No,No 141 | 2-3 days,Keeps Variating,18-25,mumbai,Yes,20,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,Yes,Yes,Yes,3,2,No,No 142 | 4-5 days,29-35 days,18-25,mumbai,No,,No,Yes,Yes,Not Applicable,normal,normal,normal,moderate,moderate,normal,No,Yes,Yes,No,No,3,3,No,No 143 | 4-5 days,Keeps Variating,18-25,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,0,7,No,No 144 | 4-5 days,25-28,18-25,mumbai,No,,Yes,Yes,No,Not Applicable,normal,normal,normal,moderate,normal,moderate,Yes,Yes,Yes,No,No,0,5,No,No 145 | 2-3 days,29-35 days,26-30,mumbai,No,,No,No,Yes,No,normal,normal,normal,normal,moderate,normal,No,Yes,No,No,No,2,1,No,No 146 | 2-3 days,Keeps Variating,36-40,mumbai,No,,,Yes,Yes,Not Applicable,moderate,moderate,moderate,moderate,moderate,moderate,Yes,Yes,No,Yes,Yes,0,7,No,No 147 | 2-3 days,Keeps Variating,36-40,jaipur,Yes,28,Yes,Yes,Yes,No,excessive,moderate,normal,excessive,moderate,moderate,No,Yes,Yes,Yes,Yes,0,1,No,Yes 148 | 4-5 days,Keeps Variating,18-25,mumbai,Yes,,No,No,Yes,Not Applicable,normal,normal,moderate,normal,moderate,normal,Yes,Yes,No,Yes,No,4,3,No,No 149 | 4-5 days,Keeps Variating,18-25,mumbai,Yes,21,No,No,Yes,Not Applicable,normal,normal,normal,normal,moderate,moderate,Yes,No,No,Yes,Yes,0,1,No,No 150 | 4-5 days,Keeps Variating,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,moderate,excessive,normal,moderate,No,Yes,Yes,Yes,Yes,0,3,No,No 151 | 4-5 days,Keeps Variating,18-25,navi mumbai,Yes,18,No,Yes,Yes,Not Applicable,moderate,moderate,moderate,excessive,excessive,excessive,Yes,Yes,Yes,Yes,Yes,1,6,No,No 152 | 4-5 days,Keeps Variating,18-25,mumbai,No,,,Yes,Yes,Not Applicable,moderate,moderate,moderate,moderate,moderate,moderate,No,No,Yes,No,No,2,0,No,No 153 | 2-3 days,25-28,40-45,maharashtra,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,Yes,No,No,0,4,No,No 154 | 2-3 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,moderate,normal,Yes,Yes,No,No,No,3,3,No,No 155 | 2-3 days,29-35 days,18-25,mumbai,No,,,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,No,Yes,0,5,No,No 156 | 4-5 days,29-35 days,18-25,mumbai,Yes,15,No,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,0,2,No,Yes 157 | 4-5 days,20-24 days,31-35,mumbai,No,,Maybe,Yes,No,No,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,0,1,No,No 158 | 4-5 days,25-28,18-25,mumbai,Yes,14,No,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,Yes,No,0,2,No,Yes 159 | 4-5 days,Keeps Variating,18-25,bangalore,Yes,17,Yes,Yes,Yes,Not Applicable,normal,normal,normal,moderate,normal,normal,No,Yes,No,No,No,2,6,No,Yes 160 | 2-3 days,29-35 days,18-25,mumbai,Yes,14,Yes,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,Yes,Yes,Yes,0,2,No,No 161 | 4-5 days,29-35 days,18-25,mumbai,Yes,14,Yes,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,0,3,Yes,No 162 | 4-5 days,29-35 days,18-25,mumbai,No,,No,No,No,No,normal,normal,moderate,normal,normal,normal,Yes,No,No,Yes,Yes,1,7,No,No 163 | 6-7 days,29-35 days,18-25,mumbai,No,,,No,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,No,0,1,No,No 164 | 4-5 days,20-24 days,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,1,No,No 165 | 4-5 days,25-28,18-25,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,2,6,Yes,Yes 166 | 2-3 days,20-24 days,Above 45,mumbai,Yes,12,No,Yes,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,No,3,1,No,No 167 | 4-5 days,25-28,Below 18,mumbai,Yes,19,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,No,No,0,4,No,Yes 168 | 4-5 days,25-28,26-30,mumbai,No,,,No,No,No,normal,normal,normal,moderate,moderate,moderate,No,Yes,No,Yes,Yes,0,1,No,No 169 | 4-5 days,Keeps Variating,26-30,bangalore,No,26,Yes,Yes,Yes,Not Applicable,excessive,normal,normal,normal,normal,normal,No,Yes,Yes,Yes,Yes,4,2,No,Yes 170 | 4-5 days,20-24 days,18-25,mumbai,No,,Maybe,Yes,Yes,Yes,normal,normal,normal,normal,normal,moderate,No,Yes,No,No,Yes,1,3,No,No 171 | 4-5 days,29-35 days,26-30,hyderabad,No,,Yes,Yes,Yes,Not Applicable,moderate,moderate,normal,excessive,normal,normal,No,Yes,No,Yes,Yes,0,2,No,Yes 172 | 4-5 days,25-28,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,4,No,No 173 | 4-5 days,25-28,Above 45,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,0,0,No,No 174 | 4-5 days,29-35 days,18-25,mumbai,No,,Maybe,No,Yes,Not Applicable,normal,normal,normal,normal,excessive,excessive,Yes,Yes,Yes,Yes,No,0,1,No,No 175 | 6-7 days,25-28,18-25,thane,No,,,Yes,No,Not Applicable,normal,normal,excessive,excessive,excessive,normal,Yes,No,No,No,No,3,6,No,Yes 176 | 4-5 days,20-24 days,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,3,2,No,No 177 | 4-5 days,29-35 days,18-25,mumbai,No,,No,Yes,Yes,No,normal,normal,normal,normal,moderate,moderate,No,Yes,Yes,Yes,No,0,1,No,Yes 178 | 4-5 days,25-28,18-25,dombivli,No,,,No,No,Not Applicable,normal,normal,normal,normal,moderate,normal,Yes,Yes,No,Yes,Yes,0,3,No,No 179 | 4-5 days,29-35 days,18-25,kalyan,No,,No,No,No,No,normal,normal,normal,normal,excessive,excessive,Yes,Yes,Yes,Yes,Yes,0,3,No,No 180 | 4-5 days,29-35 days,18-25,kolkata,Yes,15,Yes,Yes,Yes,Not Applicable,normal,normal,moderate,moderate,normal,normal,No,No,No,No,No,3,2,No,No 181 | 4-5 days,20-24 days,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,5,No,No 182 | More than 7 days,36+ days,Above 45,nagpur,Yes,12,Maybe,Yes,No,Yes,excessive,moderate,excessive,excessive,normal,normal,Yes,Yes,Yes,Yes,Yes,0,0,No,No 183 | 4-5 days,Keeps Variating,18-25,navi mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,5,1,No,No 184 | 2-3 days,20-24 days,18-25,nagpur,No,,,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,No,Yes,Yes,No,0,0,No,No 185 | 4-5 days,Keeps Variating,18-25,mumbai,No,,,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,No,1,7,No,Yes 186 | 2-3 days,29-35 days,18-25,bangalore,No,,,Yes,Yes,Not Applicable,normal,normal,normal,normal,moderate,normal,No,Yes,No,No,Yes,0,2,No,No 187 | 2-3 days,25-28,40-45,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,2,4,No,No 188 | 2-3 days,25-28,40-45,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,2,4,No,No 189 | 4-5 days,29-35 days,18-25,mumbai,No,,No,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,No,0,0,No,No 190 | 2-3 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,No,No,4,4,No,No 191 | 4-5 days,29-35 days,18-25,thane,No,,,No,Not Applicable,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,1,2,No,No 192 | 4-5 days,25-28,18-25,thane,No,,,No,No,No,normal,normal,normal,normal,normal,excessive,Yes,No,Yes,No,No,0,3,No,No 193 | 6-7 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,moderate,Yes,Yes,Yes,Yes,No,3,4,No,No 194 | 4-5 days,Keeps Variating,18-25,mumbai,No,,,No,Yes,Not Applicable,moderate,normal,normal,moderate,moderate,normal,No,No,Yes,No,Yes,1,4,No,No 195 | 4-5 days,29-35 days,18-25,amravati,Yes,19,Maybe,Yes,Yes,Not Applicable,moderate,normal,normal,moderate,moderate,moderate,Yes,Yes,No,Yes,Yes,0,5,No,No 196 | 2-3 days,25-28,18-25,bangalore,No,,Maybe,Yes,Not Applicable,Not Applicable,normal,normal,normal,normal,moderate,normal,Yes,Yes,Yes,Yes,Yes,0,4,No,Yes 197 | 2-3 days,Keeps Variating,18-25,mumbai,Yes,17,Yes,Yes,Yes,Not Applicable,normal,normal,normal,moderate,moderate,normal,Yes,Yes,Yes,Yes,Yes,4,5,No,No 198 | 4-5 days,25-28,18-25,mumbai,No,,,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,5,4,No,No 199 | 4-5 days,Keeps Variating,36-40,palghar,No,,Maybe,Yes,Yes,Yes,normal,normal,normal,normal,normal,normal,No,Yes,Yes,Yes,Yes,0,2,No,No 200 | 4-5 days,25-28,18-25,mumbai,No,,Maybe,Yes,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,7,1,No,No 201 | 4-5 days,29-35 days,31-35,mumbai,Yes,,Maybe,Yes,Yes,Not Applicable,moderate,normal,moderate,moderate,normal,normal,No,Yes,No,No,No,0,2,No,Yes 202 | 6-7 days,29-35 days,18-25,mumbai,No,,No,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,Yes,Yes,No,1,2,No,Yes 203 | 4-5 days,25-28,36-40,mumbai,No,,,No,Not Applicable,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,Yes,Yes,Yes,0,4,No,No 204 | 6-7 days,25-28,18-25,mumbai,No,,,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,No,No,No,Yes,6,2,No,No 205 | 4-5 days,29-35 days,18-25,mumbai,Yes,16,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,No,3,5,No,No 206 | 4-5 days,29-35 days,40-45,mumbai,Yes,38,Yes,Yes,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,No,Yes,4,1,No,No 207 | 6-7 days,20-24 days,31-35,mumbai,No,,No,No,Yes,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,2,No,No 208 | 4-5 days,20-24 days,18-25,mumbai,Yes,21,Yes,Yes,Yes,Not Applicable,moderate,moderate,normal,moderate,normal,normal,Yes,Yes,Yes,Yes,Yes,0,6,No,Yes 209 | 2-3 days,25-28,18-25,mumbai,No,,Maybe,No,Yes,Yes,normal,normal,moderate,excessive,excessive,excessive,Yes,Yes,Yes,Yes,Yes,0,2,No,No 210 | 2-3 days,25-28,Above 45,mumbai,No,,,No,No,No,normal,normal,normal,normal,moderate,moderate,No,No,No,No,No,6,1,No,No 211 | 2-3 days,25-28,36-40,mumbai,No,,,Yes,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,3,1,No,No 212 | 4-5 days,29-35 days,Above 45,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,Yes,No,1,1,No,No 213 | 2-3 days,29-35 days,36-40,mumbai,No,,No,Yes,Yes,Not Applicable,normal,excessive,normal,excessive,normal,excessive,No,Yes,No,No,No,0,4,No,No 214 | 4-5 days,20-24 days,40-45,mumbai,No,,,Yes,Yes,Yes,moderate,normal,normal,moderate,normal,normal,No,No,No,Yes,Yes,3,0,No,No 215 | 6-7 days,20-24 days,18-25,hyderabad,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,0,1,No,No 216 | 4-5 days,25-28,40-45,dubai,Yes,40,Yes,Yes,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,0,1,No,No 217 | 6-7 days,25-28,Above 45,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,0,No,No 218 | 6-7 days,Keeps Variating,26-30,mumbai,Yes,20,Yes,Yes,Yes,Not Applicable,normal,normal,normal,excessive,normal,normal,No,Yes,Yes,No,No,0,7,No,No 219 | 6-7 days,29-35 days,18-25,mumbai,No,,No,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,No,2,4,No,No 220 | 2-3 days,29-35 days,Above 45,mumbai,No,,,No,Yes,No,normal,normal,normal,normal,normal,normal,No,Yes,Yes,No,Yes,4,3,No,No 221 | 6-7 days,29-35 days,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,0,1,No,No 222 | 2-3 days,25-28,18-25,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,moderate,normal,No,Yes,Yes,No,No,0,5,No,No 223 | 4-5 days,20-24 days,Above 45,mumbai,No,,,Yes,Yes,No,normal,normal,normal,normal,moderate,moderate,Yes,No,No,No,No,3,1,No,No 224 | 4-5 days,Keeps Variating,Above 45,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,5,1,No,No 225 | 4-5 days,Keeps Variating,Above 45,mumbai,No,,Yes,Yes,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,No,2,1,No,No 226 | 6-7 days,20-24 days,Below 18,mumbai,No,,,No,Not Applicable,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,3,1,No,No 227 | 6-7 days,25-28,36-40,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,3,1,No,No 228 | 2-3 days,25-28,Above 45,mumbai,No,,,Yes,Yes,Not Applicable,normal,excessive,moderate,normal,normal,moderate,Yes,Yes,No,Yes,Yes,2,0,No,No 229 | 4-5 days,Keeps Variating,18-25,kalyan,Yes,20,No,Yes,Yes,Not Applicable,normal,normal,normal,moderate,normal,normal,Yes,Yes,No,No,Yes,0,4,No,No 230 | 4-5 days,25-28,18-25,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,0,2,No,No 231 | 2-3 days,Keeps Variating,18-25,hyderabad,No,,Maybe,Yes,Yes,Not Applicable,moderate,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,Yes,0,5,No,No 232 | 4-5 days,20-24 days,Above 45,mumbai,No,,,No,Yes,No,normal,normal,normal,normal,normal,normal,No,Yes,No,Yes,Yes,1,2,No,No 233 | 6-7 days,29-35 days,18-25,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,moderate,moderate,normal,No,Yes,Yes,No,No,2,2,No,No 234 | 4-5 days,20-24 days,18-25,mumbai,No,,No,No,No,Not Applicable,normal,normal,normal,normal,moderate,moderate,No,Yes,Yes,No,Yes,2,4,No,No 235 | 4-5 days,25-28,Above 45,mumbai,No,,Maybe,No,Yes,No,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,Yes,7,2,No,No 236 | 4-5 days,25-28,31-35,"thane, maharashtra",No,,No,Yes,No,No,normal,normal,normal,normal,normal,normal,Yes,Yes,No,Yes,No,0,3,No,No 237 | 4-5 days,Keeps Variating,18-25,mumba,No,,Maybe,Yes,No,Not Applicable,normal,normal,normal,moderate,moderate,normal,Yes,Yes,Yes,Yes,Yes,0,2,No,No 238 | More than 7 days,36+ days,31-35,navi mumbai,Yes,26,Maybe,Yes,Yes,Yes,excessive,normal,normal,excessive,moderate,moderate,Yes,Yes,Yes,Yes,Yes,2,0,No,No 239 | 6-7 days,25-28,18-25,thane,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,Yes,No,No,1,5,No,No 240 | 4-5 days,25-28,26-30,mumbai,No,,No,No,No,Not Applicable,normal,normal,moderate,moderate,moderate,normal,No,Yes,Yes,No,No,4,4,No,No 241 | 2-3 days,36+ days,18-25,thane,Yes,20,No,Yes,Yes,No,excessive,normal,normal,excessive,normal,normal,Yes,Yes,Yes,Yes,Yes,0,2,Yes,No 242 | 2-3 days,25-28,40-45,kharghar,No,,,Yes,No,No,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,No,Yes,3,1,No,No 243 | 4-5 days,25-28,18-25,mumbai,Yes,20,No,No,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,No,Yes,No,No,1,1,No,No 244 | 2-3 days,29-35 days,31-35,thane,No,,No,No,No,No,normal,normal,normal,moderate,moderate,moderate,No,Yes,No,Yes,Yes,0,1,No,No 245 | 4-5 days,25-28,40-45,mumbai,No,,Yes,Yes,Yes,No,normal,normal,normal,normal,moderate,moderate,No,Yes,Yes,Yes,Yes,5,3,No,No 246 | 2-3 days,25-28,26-30,mumbai,No,,,Yes,Yes,Not Applicable,moderate,moderate,normal,moderate,moderate,moderate,Yes,Yes,No,Yes,Yes,0,3,No,No 247 | 2-3 days,20-24 days,26-30,mumbai,No,,,Yes,No,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,0,3,No,No 248 | 4-5 days,29-35 days,Below 18,mumbai,No,,,No,Yes,Not Applicable,normal,normal,normal,moderate,moderate,normal,No,Yes,No,No,No,0,2,No,No 249 | 2-3 days,29-35 days,40-45,mumbai,Yes,30,Yes,Yes,No,Not Applicable,normal,normal,normal,normal,normal,moderate,Yes,Yes,No,Yes,No,2,2,No,No 250 | More than 7 days,Keeps Variating,18-25,mumbai,Yes,15,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,moderate,excessive,No,Yes,Yes,Yes,Yes,0,4,No,No 251 | 2-3 days,25-28,Above 45,mumbai,No,,,No,Yes,Yes,normal,normal,normal,normal,normal,normal,No,No,No,No,No,5,1,No,No 252 | 6-7 days,25-28,26-30,mumbai,No,,,Yes,No,No,normal,normal,normal,normal,normal,normal,Yes,No,No,No,No,5,1,No,No 253 | 2-3 days,25-28,18-25,dharwad,No,,,No,No,Not Applicable,moderate,moderate,moderate,moderate,moderate,moderate,Yes,Yes,Yes,Yes,No,0,3,No,Yes 254 | 4-5 days,29-35 days,31-35,ahmedabad,No,,No,No,Yes,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,No,3,1,No,No 255 | 2-3 days,20-24 days,Above 45,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,6,2,No,No 256 | 6-7 days,Keeps Variating,Above 45,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,No,No,No,No,7,0,No,No 257 | 4-5 days,29-35 days,26-30,mumbai,Yes,16,No,Yes,Yes,Not Applicable,normal,normal,normal,normal,normal,normal,Yes,Yes,Yes,Yes,Yes,2,1,No,No 258 | 2-3 days,Keeps Variating,40-45,mumbai,Yes,28,Maybe,No,Yes,Yes,moderate,moderate,moderate,moderate,moderate,moderate,No,Yes,No,Yes,No,2,0,No,No 259 | 4-5 days,25-28,40-45,thane,No,,,Yes,No,Not Applicable,normal,normal,normal,normal,normal,moderate,Yes,Yes,Yes,Yes,Yes,0,2,No,No 260 | 6-7 days,25-28,Above 45,mumbai,Yes,40,No,No,Yes,Not Applicable,moderate,normal,moderate,moderate,normal,normal,Yes,Yes,No,Yes,No,7,1,No,No 261 | 4-5 days,25-28,Above 45,mumbai,No,,No,No,No,No,normal,normal,normal,normal,normal,normal,No,Yes,No,No,Yes,4,1,No,No 262 | 6-7 days,Keeps Variating,Below 18,mumbai,Yes,14,Yes,Yes,Yes,Not Applicable,normal,normal,normal,normal,moderate,normal,Yes,No,Yes,Yes,Yes,0,2,No,No 263 | 2-3 days,25-28,18-25,mumbai,No,,No,Yes,No,Not Applicable,normal,normal,moderate,normal,moderate,excessive,Yes,Yes,Yes,Yes,Yes,0,3,No,No 264 | 6-7 days,25-28,36-40,mumbai,No,,,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,6,1,No,No 265 | 6-7 days,29-35 days,18-25,delhi,No,,,No,No,Not Applicable,normal,normal,normal,moderate,moderate,moderate,Yes,Yes,Yes,No,No,1,6,No,No 266 | 4-5 days,25-28,18-25,udaipur,No,,No,No,No,Not Applicable,normal,normal,normal,normal,normal,normal,No,No,No,No,No,2,3,No,No 267 | 6-7 days,20-24 days,18-25,mumbai,No,,No,No,No,Not Applicable,moderate,moderate,moderate,excessive,excessive,excessive,Yes,Yes,No,Yes,No,5,4,Yes,Yes 268 | 4-5 days,25-28,31-35,mumbai,No,,,No,No,No,normal,normal,normal,normal,normal,normal,Yes,Yes,No,No,No,3,1,No,No 269 | -------------------------------------------------------------------------------- /Dataset/clean_data.csv: -------------------------------------------------------------------------------- 1 | ,Period Length,Cycle Length,Age,City,PCOS,PCOS_from,Overweight,loss weight gain / weight loss,irregular or missed periods,Difficulty in conceiving,Hair growth on Chin,Hair growth on Cheeks,Hair growth Between breasts,Hair growth on Upper lips ,Hair growth in Arms,Hair growth on Inner thighs,Acne or skin tags,Hair thinning or hair loss ,Dark patches,always tired,more Mood Swings,exercise per week,eat outside per week,canned food often,relocated city 2 | 0,5,6,2,mumbai,No,,0,0,1,0,1,0,0,1,0,0,1,1,0,0,0,0,4,0,0 3 | 1,5,2,2,mumbai,No,,0,1,0,0,1,1,1,2,2,1,0,1,0,1,1,1,1,0,0 4 | 2,5,4,2,mumbai,Yes,20.0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,1,0 5 | 3,5,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,1,0,0 6 | 4,5,2,2,thane,No,,0,0,1,0,1,0,1,1,1,0,1,0,1,1,1,0,4,0,0 7 | 5,5,4,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0 8 | 6,5,2,2,mumbai,No,,0,0,0,0,0,0,0,1,0,1,1,1,0,0,1,0,4,0,1 9 | 7,5,4,2,mumbai,No,,0,0,0,0,0,1,0,1,2,2,0,0,1,1,1,0,2,0,1 10 | 8,7,2,2,mumbai,Yes,20.0,0,0,0,0,1,0,0,0,1,1,1,1,0,0,1,3,2,0,0 11 | 9,3,4,2,mumbai,No,,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,0,4,0,1 12 | 10,5,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,3,4,0,0 13 | 11,7,6,2,mumbai,Yes,17.0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,1,2,7,0,1 14 | 12,5,4,2,mumbai,No,,0,1,1,0,0,0,0,0,1,0,1,1,1,1,1,0,4,0,0 15 | 13,5,6,4,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0 16 | 14,5,3,2,mumbai,Yes,21.0,1,1,1,0,0,0,0,0,0,0,1,1,0,0,1,4,1,0,0 17 | 15,5,3,2,thane,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0 18 | 16,7,4,2,pune,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,4,0,1 19 | 17,5,4,2,mumbai,No,,0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,4,0,0 20 | 18,5,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,1 21 | 19,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,2,2,0,0 22 | 20,3,4,2,mumbai,No,,0,0,0,0,0,0,0,1,1,1,1,1,1,0,1,0,3,0,0 23 | 21,7,3,1,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,2,0,0 24 | 22,5,6,2,thane,Yes,21.0,0,1,1,0,1,1,2,2,2,1,1,1,1,1,1,0,3,0,0 25 | 23,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,5,0,1 26 | 24,5,4,2,mumbai,Yes,20.0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,4,0,0 27 | 25,5,3,2,kalyan,No,,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,4,0,0 28 | 26,5,4,2,thane,No,,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,2,0,0 29 | 27,5,6,2,mumbai,Yes,17.0,0,0,1,0,0,0,1,0,1,1,0,1,0,1,1,3,4,0,0 30 | 28,5,4,2,thane,No,,0,1,1,0,2,2,2,2,2,2,1,1,1,1,1,4,3,0,0 31 | 29,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,7,0,0 32 | 30,3,3,3,jhajha,No,,0,0,1,0,0,0,0,0,1,0,1,1,0,1,1,3,0,0,0 33 | 31,5,1,2,mumbai,No,,0,0,0,0,0,0,0,1,1,0,0,0,1,1,1,3,3,0,0 34 | 32,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,3,1,0,0 35 | 33,5,3,7,mumbai,No,,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0 36 | 34,5,1,2,mumbai,No,,0,0,0,0,0,0,1,1,1,0,1,1,1,1,1,1,5,1,0 37 | 35,5,4,7,mumbai,Yes,14.0,1,1,1,0,0,1,0,1,0,0,1,0,1,1,1,0,1,0,0 38 | 36,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,3,0,0 39 | 37,9,5,3,patna,Yes,,0,1,1,0,0,0,1,0,0,0,0,1,0,1,1,0,2,0,1 40 | 38,9,5,3,patna,Yes,,0,1,1,0,0,0,1,0,0,0,0,1,0,1,1,0,2,0,1 41 | 39,7,6,2,mumbai,No,,1,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,5,0,0 42 | 40,5,4,1,mumbai,No,,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,3,1,0,0 43 | 41,7,3,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,5,0,0 44 | 42,3,3,2,dombivli,No,,0,1,1,0,0,0,0,0,1,0,1,1,1,1,1,0,2,0,0 45 | 43,5,4,2,mumbai,No,,0,0,0,0,0,1,0,1,1,0,0,1,0,1,1,3,2,0,0 46 | 44,3,3,7,mumbai,No,,0,1,0,0,0,0,0,1,1,1,0,1,0,1,1,5,6,0,0 47 | 45,5,4,2,kalyan,No,,0,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0 48 | 46,5,6,2,mumbai,No,,0,1,1,0,2,2,0,1,2,0,1,1,0,1,1,1,1,0,0 49 | 47,3,4,7,mumbai,No,,0,1,1,0,1,0,0,1,1,0,1,0,1,1,1,3,1,0,0 50 | 48,3,4,5,mumbai,No,36.0,1,1,1,1,0,0,0,0,0,0,1,1,0,1,1,3,0,0,0 51 | 49,5,6,3,navi mumbai,Yes,18.0,1,1,1,0,0,0,0,1,0,0,1,1,0,1,1,5,4,0,0 52 | 50,5,3,7,mumbai,No,,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,7,1,0,0 53 | 51,3,1,3,mumbai,No,,0,0,0,0,1,1,0,0,1,0,0,1,1,0,1,1,1,0,0 54 | 52,5,5,3,mumbai,Yes,18.0,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,0,2,0,0 55 | 53,5,1,7,mumbai,No,,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0 56 | 54,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0 57 | 55,7,4,7,mumbai,Yes,37.0,1,1,0,0,1,2,0,1,1,2,1,1,0,1,1,0,2,0,0 58 | 56,5,4,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0 59 | 57,3,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0 60 | 58,5,3,7,mumbai,No,,0,0,0,0,1,1,0,1,1,0,1,1,1,1,0,0,1,0,0 61 | 59,3,4,4,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0 62 | 60,5,3,2,bhopal,No,,0,1,0,0,0,0,0,1,1,0,0,1,1,1,1,0,2,0,0 63 | 61,3,3,2,patna,No,,0,0,0,0,0,0,1,0,1,0,1,1,1,0,1,0,7,1,1 64 | 62,3,3,2,patna,No,,0,0,0,0,0,0,1,0,1,0,1,1,1,0,1,0,7,1,1 65 | 63,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0 66 | 64,5,5,2,pune,No,,1,1,1,1,0,1,2,1,1,2,1,1,0,1,1,0,5,1,1 67 | 65,5,5,2,pune,No,,1,1,1,1,0,1,2,1,1,2,1,1,0,1,1,0,5,1,1 68 | 66,5,3,7,mumbai,No,,1,1,0,0,0,1,0,0,0,0,1,1,1,1,1,0,0,0,1 69 | 67,5,3,7,mumbai,No,,0,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,1 70 | 68,3,4,7,mumbai,No,,0,0,0,1,1,0,0,1,1,0,0,0,1,1,1,4,1,0,1 71 | 69,3,5,2,pune,Yes,20.0,1,1,1,1,2,2,1,0,2,2,1,1,1,1,1,0,3,0,1 72 | 70,5,6,7,mumbai,No,,0,1,1,0,1,0,0,0,0,0,0,1,1,0,1,2,2,0,0 73 | 71,9,6,2,mumbai,Yes,16.0,1,1,1,0,2,2,2,2,2,2,1,1,1,1,0,0,7,0,0 74 | 72,3,4,4,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0 75 | 73,5,1,2,thane,No,,0,0,0,0,1,0,0,1,1,0,1,0,1,0,0,3,3,0,0 76 | 74,3,3,2,mumbai,Yes,18.0,0,1,0,0,0,0,0,0,0,0,1,1,0,1,1,3,7,1,0 77 | 75,5,3,3,mumbai,No,,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,3,0,0 78 | 76,3,4,4,mumbai,Yes,30.0,1,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,1,0,0 79 | 77,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,2,0,0 80 | 78,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,2,1,0 81 | 79,3,3,3,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,1,1,0,0 82 | 80,5,6,3,mumbai,Yes,17.0,1,1,1,1,0,0,0,0,0,0,0,1,0,1,1,2,0,0,0 83 | 81,5,3,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,7,0,1 84 | 82,3,4,2,mumbai,Yes,22.0,0,1,1,0,0,0,0,0,0,1,1,0,0,1,0,5,4,0,0 85 | 83,3,4,7,"manama, bahrain",No,,0,1,1,0,0,1,0,1,0,0,0,1,0,0,0,2,2,0,0 86 | 84,5,3,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,5,2,0,0 87 | 85,7,3,2,mumbai,No,,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,4,2,0,0 88 | 86,5,3,1,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,2,0,0 89 | 87,5,1,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,1,0 90 | 88,5,4,2,mumbai,No,,0,1,0,0,0,0,0,1,1,1,1,1,1,0,1,0,3,0,0 91 | 89,3,4,2,mumbai,Yes,19.0,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,2,2,0,0 92 | 90,3,4,2,mumbai,Yes,19.0,1,1,1,0,0,0,0,0,0,0,1,1,1,1,0,2,2,0,0 93 | 91,7,1,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,1,1,0,0,3,0,0 94 | 92,5,3,4,bangalore,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,2,0,0 95 | 93,7,1,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0 96 | 94,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,6,0,0 97 | 95,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0 98 | 96,7,5,2,mumbai,No,,0,1,1,0,0,0,0,0,1,0,1,1,0,1,1,0,3,0,0 99 | 97,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,4,0,0 100 | 98,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,4,0,0 101 | 99,7,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 102 | 100,7,4,7,mumbai,No,,0,1,1,0,0,0,0,0,0,0,0,1,0,0,1,4,2,0,0 103 | 101,5,4,2,mumbai,No,,0,0,0,0,0,0,0,1,1,0,1,1,0,1,0,2,1,0,0 104 | 102,5,4,2,mumbai,No,,0,1,1,1,0,0,0,0,1,1,1,1,0,0,1,3,2,0,1 105 | 103,5,6,2,mumbai,Yes,23.0,1,1,1,1,1,0,0,1,0,0,1,1,1,1,1,0,7,0,1 106 | 104,5,6,4,mumbai,No,,0,1,1,0,0,0,0,0,0,0,0,1,0,1,1,2,2,1,0 107 | 105,3,3,7,mumbai,No,,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0 108 | 106,5,3,2,thane,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,2,0,0 109 | 107,5,4,7,mumbai,No,,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,5,1,0,0 110 | 108,7,1,7,pune,No,,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,2,1,0,1 111 | 109,5,4,7,dibai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0 112 | 110,3,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0 113 | 111,5,3,7,mumbai,Yes,,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0 114 | 112,3,3,7,thane,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0 115 | 113,3,6,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0 116 | 114,5,3,3,mumbai,No,,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,0,3,0,0 117 | 115,5,3,2,panvel,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,4,0,0 118 | 116,5,6,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,5,0,1 119 | 117,5,3,3,canada,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,1,0,1 120 | 118,5,4,2,mumbai,No,,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0 121 | 119,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0 122 | 120,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,6,0,0 123 | 121,7,6,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,1,1,0,2,1,0,0 124 | 122,5,3,4,mumbai,Yes,32.0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,3,2,0,0 125 | 123,3,6,2,mumbai,Yes,21.0,1,1,1,0,2,2,0,2,2,2,1,1,0,1,1,3,1,0,1 126 | 124,7,4,4,mumbai,Yes,,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,0 127 | 125,3,5,2,thane,No,,0,0,1,0,1,0,1,1,1,0,1,1,0,0,1,3,1,0,0 128 | 126,5,1,3,mumbai,Yes,23.0,1,1,1,0,2,2,0,2,2,2,0,1,1,1,1,4,4,0,0 129 | 127,3,3,4,mumbai,No,,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 130 | 128,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,2,2,0,0 131 | 129,5,4,2,mumbai,No,,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0 132 | 130,5,4,3,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,2,0,0 133 | 131,5,3,2,mumbai,No,,0,0,1,0,0,0,0,1,1,1,1,1,1,0,0,0,2,0,0 134 | 132,7,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0 135 | 133,3,4,2,mumbai,Yes,,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,5,3,0,0 136 | 134,5,4,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,3,0,0 137 | 135,3,3,2,mumbai,No,,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,3,4,0,0 138 | 136,7,4,2,bangalore,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0 139 | 137,7,4,2,mumbai,No,,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,3,5,0,1 140 | 138,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,4,4,0,0 141 | 139,3,6,2,mumbai,Yes,20.0,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,3,2,0,0 142 | 140,5,4,2,mumbai,No,,0,1,1,0,0,0,0,1,1,0,0,1,1,0,0,3,3,0,0 143 | 141,5,6,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,7,0,0 144 | 142,5,3,2,mumbai,No,,1,1,0,0,0,0,0,1,0,1,1,1,1,0,0,0,5,0,0 145 | 143,3,4,3,mumbai,No,,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,2,1,0,0 146 | 144,3,6,5,mumbai,No,,0,1,1,0,1,1,1,1,1,1,1,1,0,1,1,0,7,0,0 147 | 145,3,6,5,jaipur,Yes,28.0,1,1,1,0,2,1,0,2,1,1,0,1,1,1,1,0,1,0,1 148 | 146,5,6,2,mumbai,Yes,,0,0,1,0,0,0,1,0,1,0,1,1,0,1,0,4,3,0,0 149 | 147,5,6,2,mumbai,Yes,21.0,0,0,1,0,0,0,0,0,1,1,1,0,0,1,1,0,1,0,0 150 | 148,5,6,2,mumbai,No,,0,0,1,0,0,0,1,2,0,1,0,1,1,1,1,0,3,0,0 151 | 149,5,6,2,navi mumbai,Yes,18.0,0,1,1,0,1,1,1,2,2,2,1,1,1,1,1,1,6,0,0 152 | 150,5,6,2,mumbai,No,,0,1,1,0,1,1,1,1,1,1,0,0,1,0,0,2,0,0,0 153 | 151,3,3,7,maharashtra,No,,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,4,0,0 154 | 152,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,3,3,0,0 155 | 153,3,4,2,mumbai,No,,0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,5,0,0 156 | 154,5,4,2,mumbai,Yes,15.0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,1 157 | 155,5,1,4,mumbai,No,,0,1,0,0,0,0,0,0,0,0,1,1,1,1,1,0,1,0,0 158 | 156,5,3,2,mumbai,Yes,14.0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1 159 | 157,5,6,2,bangalore,Yes,17.0,1,1,1,0,0,0,0,1,0,0,0,1,0,0,0,2,6,0,1 160 | 158,3,4,2,mumbai,Yes,14.0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,2,0,0 161 | 159,5,4,2,mumbai,Yes,14.0,1,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,3,1,0 162 | 160,5,4,2,mumbai,No,,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,1,7,0,0 163 | 161,7,4,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0 164 | 162,5,1,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0 165 | 163,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6,1,1 166 | 164,3,1,7,mumbai,Yes,12.0,0,1,1,0,0,0,0,0,0,0,1,1,1,1,0,3,1,0,0 167 | 165,5,3,1,mumbai,Yes,19.0,1,1,1,0,0,0,0,0,0,0,1,1,1,0,0,0,4,0,1 168 | 166,5,3,3,mumbai,No,,0,0,0,0,0,0,0,1,1,1,0,1,0,1,1,0,1,0,0 169 | 167,5,6,3,bangalore,No,26.0,1,1,1,0,2,0,0,0,0,0,0,1,1,1,1,4,2,0,1 170 | 168,5,1,2,mumbai,No,,0,1,1,1,0,0,0,0,0,1,0,1,0,0,1,1,3,0,0 171 | 169,5,4,3,hyderabad,No,,1,1,1,0,1,1,0,2,0,0,0,1,0,1,1,0,2,0,1 172 | 170,5,3,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0 173 | 171,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0 174 | 172,5,4,2,mumbai,No,,0,0,1,0,0,0,0,0,2,2,1,1,1,1,0,0,1,0,0 175 | 173,7,3,2,thane,No,,0,1,0,0,0,0,2,2,2,0,1,0,0,0,0,3,6,0,1 176 | 174,5,1,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0 177 | 175,5,4,2,mumbai,No,,0,1,1,0,0,0,0,0,1,1,0,1,1,1,0,0,1,0,1 178 | 176,5,3,2,dombivli,No,,0,0,0,0,0,0,0,0,1,0,1,1,0,1,1,0,3,0,0 179 | 177,5,4,2,kalyan,No,,0,0,0,0,0,0,0,0,2,2,1,1,1,1,1,0,3,0,0 180 | 178,5,4,2,kolkata,Yes,15.0,1,1,1,0,0,0,1,1,0,0,0,0,0,0,0,3,2,0,0 181 | 179,5,1,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0 182 | 180,9,5,7,nagpur,Yes,12.0,0,1,0,1,2,1,2,2,0,0,1,1,1,1,1,0,0,0,0 183 | 181,5,6,2,navi mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,5,1,0,0 184 | 182,3,1,2,nagpur,No,,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0 185 | 183,5,6,2,mumbai,No,,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,1,7,0,1 186 | 184,3,4,2,bangalore,No,,0,1,1,0,0,0,0,0,1,0,0,1,0,0,1,0,2,0,0 187 | 185,3,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0 188 | 186,3,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0 189 | 187,5,4,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0 190 | 188,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,4,4,0,0 191 | 189,5,4,2,thane,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0 192 | 190,5,3,2,thane,No,,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,3,0,0 193 | 191,7,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,3,4,0,0 194 | 192,5,6,2,mumbai,No,,0,0,1,0,1,0,0,1,1,0,0,0,1,0,1,1,4,0,0 195 | 193,5,4,2,amravati,Yes,19.0,0,1,1,0,1,0,0,1,1,1,1,1,0,1,1,0,5,0,0 196 | 194,3,3,2,bangalore,No,,0,1,0,0,0,0,0,0,1,0,1,1,1,1,1,0,4,0,1 197 | 195,3,6,2,mumbai,Yes,17.0,1,1,1,0,0,0,0,1,1,0,1,1,1,1,1,4,5,0,0 198 | 196,5,3,2,mumbai,No,,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,5,4,0,0 199 | 197,5,6,5,palghar,No,,0,1,1,1,0,0,0,0,0,0,0,1,1,1,1,0,2,0,0 200 | 198,5,3,2,mumbai,No,,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,7,1,0,0 201 | 199,5,4,4,mumbai,Yes,,0,1,1,0,1,0,1,1,0,0,0,1,0,0,0,0,2,0,1 202 | 200,7,4,2,mumbai,No,,0,1,1,0,0,0,0,0,0,0,0,0,1,1,0,1,2,0,1 203 | 201,5,3,5,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,4,0,0 204 | 202,7,3,2,mumbai,No,,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,6,2,0,0 205 | 203,5,4,2,mumbai,Yes,16.0,1,1,1,0,0,0,0,0,0,0,1,1,0,1,0,3,5,0,0 206 | 204,5,4,7,mumbai,Yes,38.0,1,1,1,0,0,0,0,0,0,0,1,1,1,0,1,4,1,0,0 207 | 205,7,1,4,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0 208 | 206,5,1,2,mumbai,Yes,21.0,1,1,1,0,1,1,0,1,0,0,1,1,1,1,1,0,6,0,1 209 | 207,3,3,2,mumbai,No,,0,0,1,1,0,0,1,2,2,2,1,1,1,1,1,0,2,0,0 210 | 208,3,3,7,mumbai,No,,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,6,1,0,0 211 | 209,3,3,5,mumbai,No,,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,3,1,0,0 212 | 210,5,4,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,0 213 | 211,3,4,5,mumbai,No,,0,1,1,0,0,2,0,2,0,2,0,1,0,0,0,0,4,0,0 214 | 212,5,1,7,mumbai,No,,0,1,1,1,1,0,0,1,0,0,0,0,0,1,1,3,0,0,0 215 | 213,7,1,2,hyderabad,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0 216 | 214,5,3,7,dubai,Yes,40.0,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,0,0 217 | 215,7,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 218 | 216,7,6,3,mumbai,Yes,20.0,1,1,1,0,0,0,0,2,0,0,0,1,1,0,0,0,7,0,0 219 | 217,7,4,2,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,2,4,0,0 220 | 218,3,4,7,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,1,0,1,4,3,0,0 221 | 219,7,4,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0 222 | 220,3,3,2,mumbai,No,,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,5,0,0 223 | 221,5,1,7,mumbai,No,,0,1,1,0,0,0,0,0,1,1,1,0,0,0,0,3,1,0,0 224 | 222,5,6,7,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,5,1,0,0 225 | 223,5,6,7,mumbai,No,,1,1,1,0,0,0,0,0,0,0,1,1,0,1,0,2,1,0,0 226 | 224,7,1,1,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0 227 | 225,7,3,5,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0 228 | 226,3,3,7,mumbai,No,,0,1,1,0,0,2,1,0,0,1,1,1,0,1,1,2,0,0,0 229 | 227,5,6,2,kalyan,Yes,20.0,0,1,1,0,0,0,0,1,0,0,1,1,0,0,1,0,4,0,0 230 | 228,5,3,2,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0 231 | 229,3,6,2,hyderabad,No,,0,1,1,0,1,0,0,0,0,0,1,1,0,1,1,0,5,0,0 232 | 230,5,1,7,mumbai,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,1,1,1,2,0,0 233 | 231,7,4,2,mumbai,No,,0,0,1,0,0,0,0,1,1,0,0,1,1,0,0,2,2,0,0 234 | 232,5,1,2,mumbai,No,,0,0,0,0,0,0,0,0,1,1,0,1,1,0,1,2,4,0,0 235 | 233,5,3,7,mumbai,No,,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,7,2,0,0 236 | 234,5,3,4,"thane, maharashtra",No,,0,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,3,0,0 237 | 235,5,6,2,mumba,No,,0,1,0,0,0,0,0,1,1,0,1,1,1,1,1,0,2,0,0 238 | 236,9,5,4,navi mumbai,Yes,26.0,0,1,1,1,2,0,0,2,1,1,1,1,1,1,1,2,0,0,0 239 | 237,7,3,2,thane,No,,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,5,0,0 240 | 238,5,3,3,mumbai,No,,0,0,0,0,0,0,1,1,1,0,0,1,1,0,0,4,4,0,0 241 | 239,3,5,2,thane,Yes,20.0,0,1,1,0,2,0,0,2,0,0,1,1,1,1,1,0,2,1,0 242 | 240,3,3,7,kharghar,No,,0,1,0,0,0,0,0,0,0,0,1,1,1,0,1,3,1,0,0 243 | 241,5,3,2,mumbai,Yes,20.0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0 244 | 242,3,4,4,thane,No,,0,0,0,0,0,0,0,1,1,1,0,1,0,1,1,0,1,0,0 245 | 243,5,3,7,mumbai,No,,1,1,1,0,0,0,0,0,1,1,0,1,1,1,1,5,3,0,0 246 | 244,3,3,3,mumbai,No,,0,1,1,0,1,1,0,1,1,1,1,1,0,1,1,0,3,0,0 247 | 245,3,1,3,mumbai,No,,0,1,0,0,0,0,0,0,0,0,1,1,1,1,1,0,3,0,0 248 | 246,5,4,1,mumbai,No,,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,2,0,0 249 | 247,3,4,7,mumbai,Yes,30.0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,2,2,0,0 250 | 248,9,6,2,mumbai,Yes,15.0,1,1,1,0,0,0,0,0,1,2,0,1,1,1,1,0,4,0,0 251 | 249,3,3,7,mumbai,No,,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0 252 | 250,7,3,3,mumbai,No,,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,5,1,0,0 253 | 251,3,3,2,dharwad,No,,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,3,0,1 254 | 252,5,4,4,ahmedabad,No,,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,3,1,0,0 255 | 253,3,1,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,2,0,0 256 | 254,7,6,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0 257 | 255,5,4,3,mumbai,Yes,16.0,0,1,1,0,0,0,0,0,0,0,1,1,1,1,1,2,1,0,0 258 | 256,3,6,7,mumbai,Yes,28.0,0,0,1,1,1,1,1,1,1,1,0,1,0,1,0,2,0,0,0 259 | 257,5,3,7,thane,No,,0,1,0,0,0,0,0,0,0,1,1,1,1,1,1,0,2,0,0 260 | 258,7,3,7,mumbai,Yes,40.0,0,0,1,0,1,0,1,1,0,0,1,1,0,1,0,7,1,0,0 261 | 259,5,3,7,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,4,1,0,0 262 | 260,7,6,1,mumbai,Yes,14.0,1,1,1,0,0,0,0,0,1,0,1,0,1,1,1,0,2,0,0 263 | 261,3,3,2,mumbai,No,,0,1,0,0,0,0,1,0,1,2,1,1,1,1,1,0,3,0,0 264 | 262,7,3,5,mumbai,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0 265 | 263,7,4,2,delhi,No,,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,1,6,0,0 266 | 264,5,3,2,udaipur,No,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0 267 | 265,7,1,2,mumbai,No,,0,0,0,0,1,1,1,2,2,2,1,1,0,1,0,5,4,1,1 268 | 266,5,3,4,mumbai,No,,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,3,1,0,0 269 | -------------------------------------------------------------------------------- /Dataset/results.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PCOS-Prediction/Machine-Learning/828c34bee406db2d529e34b1410829a12ebf2ed5/Dataset/results.xlsx -------------------------------------------------------------------------------- /Final Model - Best Accuracy/final_decisiontree.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """Copy of DecisionTree.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1n7kuJDdRR3hdFVuJfuADPbcuAK1cZTDI 8 | """ 9 | 10 | from IPython.display import Image 11 | from sklearn.externals.six import StringIO 12 | import pickle 13 | import pydotplus 14 | from sklearn.tree import export_graphviz 15 | from sklearn import tree 16 | import matplotlib.pyplot as plt 17 | from sklearn.model_selection import train_test_split 18 | from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report 19 | from sklearn.tree import DecisionTreeClassifier 20 | import numpy as np 21 | import pandas as pd 22 | from google.colab import drive 23 | drive.mount('/content/drive') 24 | 25 | 26 | # preeti 27 | path = "/content/drive/My Drive/ML/Decesion Tree/Copy of data_cleaned (1).csv" 28 | # neha 29 | #path = "/content/drive/My Drive/Academics SPIT/PROJECTS/SEM 4 - PCOS Prediction Research Project/Codes/Decesion Tree/Copy of data_cleaned (1).csv" 30 | data = pd.read_csv(path) 31 | data.head() 32 | 33 | del data['PCOS_from'] 34 | 35 | del data['City'] 36 | 37 | del data['relocated city'] 38 | 39 | del data['Unnamed: 0'] 40 | 41 | data['PCOS_label'] = None 42 | data.head() 43 | 44 | data = data.set_index('PCOS_label') 45 | 46 | data = data.reset_index() 47 | 48 | 49 | def label(row): 50 | if row['PCOS'] == 'Yes': 51 | return 1 52 | else: 53 | return 0 54 | 55 | 56 | data['PCOS_label'] = data.apply(lambda row: label(row), axis=1) 57 | 58 | data.head() 59 | 60 | # Commented out IPython magic to ensure Python compatibility. 61 | # %matplotlib notebook 62 | # from adspy_shared_utilities import plot_decision_tree 63 | 64 | PCOS_check = dict(zip(data.PCOS_label.unique(), data.PCOS.unique())) 65 | PCOS_check 66 | 67 | X = data.drop(['PCOS_label', 'PCOS'], axis=1) 68 | y = data.PCOS_label 69 | 70 | X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) 71 | 72 | X_train.head() 73 | 74 | y_train.head() 75 | 76 | clf = DecisionTreeClassifier(max_depth=6).fit(X_train, y_train) 77 | 78 | tree_predicted = clf.predict(X_test) 79 | confusion = confusion_matrix(y_test, tree_predicted) 80 | print(confusion) 81 | 82 | print('Accuracy: {:.2f}'.format(accuracy_score(y_test, tree_predicted))) 83 | print('Precision: {:.2f}'.format(precision_score(y_test, tree_predicted))) 84 | print('Recall: {:.2f}'.format(recall_score(y_test, tree_predicted))) 85 | print('F1: {:.2f}'.format(f1_score(y_test, tree_predicted))) 86 | 87 | print(classification_report( 88 | y_test, tree_predicted, target_names=['No', 'Yes'])) 89 | 90 | print('Accuracy on training set: {:.2f}'.format(clf.score(X_train, y_train))) 91 | 92 | print('Accuracy on test set: {:.2f}'.format(clf.score(X_test, y_test))) 93 | 94 | feature_cols = ['Period Length', 'Cycle Length', 'Age', 'Overweight', 'loss weight gain / weight loss', 'irregular or missed periods', 'Difficulty in conceiving', 'Hair growth on Chin', 'Hair growth on Cheeks', 'Hair growth Between breasts', 95 | 'Hair growth on Upper lips ', 'Hair growth in Arms', 'Hair growth on Inner thighs', 'Acne or skin tags', 'Hair thinning or hair loss ', 'Dark patches', 'always tired', 'more Mood Swings', 'exercise per week', 'eat outside per week', 'canned food often'] 96 | 97 | 98 | # Create DOT data 99 | dot_data = StringIO() 100 | 101 | export_graphviz(clf, out_file=dot_data, filled=True, rounded=True, 102 | special_characters=True, feature_names=feature_cols) 103 | # Draw graph 104 | graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) 105 | 106 | # show graph 107 | Image(graph.create_png()) 108 | 109 | pcos_prediction = clf.predict(X_test) 110 | PCOS_check[pcos_prediction[0]] 111 | 112 | pcos1 = clf.predict( 113 | [[5, 6, 2, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 7, 0]]) 114 | PCOS_check[pcos1[0]] 115 | 116 | pcos2 = clf.predict( 117 | [[5, 1, 2, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 3, 3, 0]]) 118 | PCOS_check[pcos2[0]] 119 | 120 | # clf = trained model 121 | 122 | filename = 'model.pkl' 123 | pickle.dump(clf, open(filename, 'wb')) 124 | 125 | loaded_model = pickle.load(open(filename, 'rb')) 126 | 127 | result = loaded_model.predict( 128 | [[5, 1, 2, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 3, 3, 0]]) 129 | print(result) 130 | PCOS_check[result[0]] 131 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine-Learning 2 | Machine Learning project in python to predict PCOS in women. 3 | 4 | A survy was conducted to form the databas used for training the models. 5 | ML algorithms were implemented to predict PCOS based on certain parameters. The best accuracy was obtained by Decision Tree Algorithm. 6 | --------------------------------------------------------------------------------