├── .gitignore ├── .vscode └── settings.json ├── Data ├── test.csv └── train.csv ├── README.md ├── Src ├── api │ ├── __pycache__ │ │ └── predict.cpython-36.pyc │ └── predict.py ├── ml-model │ └── voting_classifier_v1.pk ├── notebook │ └── Titanic Survival Solution Notebook.ipynb ├── templates │ └── index.html └── utils │ ├── ClassificationModelBuilder.py │ ├── DataPreparation.py │ ├── __init__.py │ └── __pycache__ │ ├── ClassificationModelBuilder.cpython-36.pyc │ ├── ClassifierModelBuilder.cpython-36.pyc │ ├── DataPreparation.cpython-36.pyc │ └── __init__.cpython-36.pyc ├── application.py └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | Include/ 2 | Lib/ 3 | Scripts/ 4 | xgboost/ 5 | pip-selfcheck.json 6 | pyvenv.cfg 7 | 8 | # Elastic Beanstalk Files 9 | .elasticbeanstalk/* 10 | !.elasticbeanstalk/*.cfg.yml 11 | !.elasticbeanstalk/*.global.yml 12 | .ebignore -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "python.linting.enabled": false, 3 | "git.ignoreLimitWarning": true 4 | } -------------------------------------------------------------------------------- /Data/test.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 3 | 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 4 | 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q 5 | 895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S 6 | 896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S 7 | 897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S 8 | 898,3,"Connolly, Miss. 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Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C 878 | 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S 879 | 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S 880 | 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S 881 | 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C 882 | 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S 883 | 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S 884 | 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S 885 | 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S 886 | 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S 887 | 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q 888 | 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S 889 | 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S 890 | 889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S 891 | 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C 892 | 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q 893 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## API-First approach to make Machine Learning solution usable 2 | 3 | ### Introduction 4 | In this application I have solved 'Titanic Survival Prediction problem' and using simple **VotingClassifier** to make predictions. I have trained many other models as well and if you want to then you can view their performance and can choose any of the desired model (by making some changes in `Src/utils/ClassificationModelBuilder.py` file) to make predictions. 5 | 6 | ### Application Setup 7 | I have used Python's `venv` module for creating/managing virtual environment and `flask` framework for API creation. 8 | 9 | If you're not much aware of `venv` environment setup, than you can go through [this](https://docs.python.org/3/tutorial/venv.html) documentation. I learnt from same. 10 | 11 | Once you have `venv` installed and got basic understanding, follow below steps to run this application: 12 | 1. `git clone https://github.com/amdp-chauhan/titanic-survival-complete-ml-solution.git` && `cd titanic-survival-complete-ml-solution` 13 | 2. `python -m venv ./` - It will create a virtual environment in application directory. 14 | 3. `Scripts\activate.bat` - It will run this virtual environment. 15 | 4. `pip install -r packages.txt` - it will install all required dependencies. 16 | 5. `python application.py` - it will run the application. 17 | 6. `deactivate` - If you want to exit from virtual environment. 18 | 19 | Upper commands will work fine in Windows 10, for Linux you can find alternatives in venv documentation. 20 | 21 | > Note that in `application.py` file, second import statement is commented out, it is because if it is enabled then it starts retraining classifier models, which is not required if you already have created a final model and data-set is same. Final models exists in `Src/ml-model/voting_classifier_v1.pk` we use same model to make predictions for requested JSON record. 22 | 23 | ### Making Predictions 24 | For predictions I have created an POST API: 25 | ``` 26 | http://{domain}/titanic-survival-classification-model/predict 27 | ``` 28 | 29 | It accepts list of JSON of test records and in return will give you a predicted Survival values in 0/1. 30 | 31 | For example, for below input parameters: 32 | 33 | ``` 34 | [{ 35 | "PassengerId": 892, 36 | "Pclass": 3, 37 | "Name": "Kelly, Mr. James", 38 | "Sex": "male", 39 | "Age": 34.5, 40 | "SibSp": 0, 41 | "Parch": 0, 42 | "Ticket": 330911, 43 | "Fare": 7.8292, 44 | "Cabin": "", 45 | "Embarked": "Q" 46 | },{ 47 | "PassengerId": 893, 48 | "Pclass": 3, 49 | "Name": "Wilkes, Mrs. James (Ellen Needs)", 50 | "Sex": "female", 51 | "Age": 47, 52 | "SibSp": 1, 53 | "Parch": 0, 54 | "Ticket": 363272, 55 | "Fare": 7, 56 | "Cabin": "", 57 | "Embarked": "S" 58 | },{ 59 | "PassengerId": 894, 60 | "Pclass": 2, 61 | "Name": "Myles, Mr. Thomas Francis", 62 | "Sex": "male", 63 | "Age": 62, 64 | "SibSp": 0, 65 | "Parch": 0, 66 | "Ticket": 240276, 67 | "Fare": 9.6875, 68 | "Cabin": "", 69 | "Embarked": "Q" 70 | },{ 71 | "PassengerId": 895, 72 | "Pclass": 3, 73 | "Name": "Wirz, Mr. Albert", 74 | "Sex": "male", 75 | "Age": 27, 76 | "SibSp": 0, 77 | "Parch": 0, 78 | "Ticket": 315154, 79 | "Fare": 8.6625, 80 | "Cabin": "", 81 | "Embarked": "S" 82 | },{ 83 | "PassengerId": 896, 84 | "Pclass": 3, 85 | "Name": "Hirvonen, Mrs. Alexander (Helga E Lindqvist)", 86 | "Sex": "female", 87 | "Age": 22, 88 | "SibSp": 1, 89 | "Parch": 1, 90 | "Ticket": 3101298, 91 | "Fare": 12.2875, 92 | "Cabin": "", 93 | "Embarked": "S" 94 | },{ 95 | "PassengerId": 897, 96 | "Pclass": 3, 97 | "Name": "Svensson, Mr. Johan Cervin", 98 | "Sex": "male", 99 | "Age": 14, 100 | "SibSp": 0, 101 | "Parch": 0, 102 | "Ticket": 7538, 103 | "Fare": 9.225, 104 | "Cabin": "", 105 | "Embarked": "S" 106 | },{ 107 | "PassengerId": 898, 108 | "Pclass": 3, 109 | "Name": "Connolly, Miss. Kate", 110 | "Sex": "female", 111 | "Age": 30, 112 | "SibSp": 0, 113 | "Parch": 0, 114 | "Ticket": 330972, 115 | "Fare": 7.6292, 116 | "Cabin": "", 117 | "Embarked": "Q" 118 | },{ 119 | "PassengerId": 899, 120 | "Pclass": 2, 121 | "Name": "Caldwell, Mr. Albert Francis", 122 | "Sex": "male", 123 | "Age": 26, 124 | "SibSp": 1, 125 | "Parch": 1, 126 | "Ticket": 248738, 127 | "Fare": 29, 128 | "Cabin": "", 129 | "Embarked": "S" 130 | },{ 131 | "PassengerId": 900, 132 | "Pclass": 3, 133 | "Name": "Abrahim, Mrs. Joseph (Sophie Halaut Easu)", 134 | "Sex": "female", 135 | "Age": 18, 136 | "SibSp": 0, 137 | "Parch": 0, 138 | "Ticket": 2657, 139 | "Fare": 7.2292, 140 | "Cabin": "", 141 | "Embarked": "C" 142 | },{ 143 | "PassengerId": 901, 144 | "Pclass": 3, 145 | "Name": "Davies, Mr. John Samuel", 146 | "Sex": "male", 147 | "Age": 21, 148 | "SibSp": 2, 149 | "Parch": 0, 150 | "Ticket": "A/4 48871", 151 | "Fare": 24.15, 152 | "Cabin": "", 153 | "Embarked": "S" 154 | },{ 155 | "PassengerId": 902, 156 | "Pclass": 3, 157 | "Name": "Ilieff, Mr. Ylio", 158 | "Sex": "male", 159 | "Age": "", 160 | "SibSp": 0, 161 | "Parch": 0, 162 | "Ticket": 349220, 163 | "Fare": 7.8958, 164 | "Cabin": "", 165 | "Embarked": "S" 166 | },{ 167 | "PassengerId": 903, 168 | "Pclass": 1, 169 | "Name": "Jones, Mr. Charles Cresson", 170 | "Sex": "male", 171 | "Age": 46, 172 | "SibSp": 0, 173 | "Parch": 0, 174 | "Ticket": 694, 175 | "Fare": 26, 176 | "Cabin": "", 177 | "Embarked": "S" 178 | }] 179 | ``` 180 | We will get below output: 181 | ``` 182 | { 183 | "predictions": "[{\"PassengerId\":892,\"Survived\":1},{\"PassengerId\":893,\"Survived\":1},{\"PassengerId\":894,\"Survived\":0},{\"PassengerId\":895,\"Survived\":1},{\"PassengerId\":896,\"Survived\":1},{\"PassengerId\":897,\"Survived\":1},{\"PassengerId\":898,\"Survived\":0},{\"PassengerId\":899,\"Survived\":0},{\"PassengerId\":900,\"Survived\":1},{\"PassengerId\":901,\"Survived\":0},{\"PassengerId\":902,\"Survived\":1},{\"PassengerId\":903,\"Survived\":0}]" 184 | } 185 | ``` -------------------------------------------------------------------------------- /Src/api/__pycache__/predict.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amdp-chauhan/complete-deployable-ml-solution/8921374a6daee8671405fedc237652696b1f2a9a/Src/api/__pycache__/predict.cpython-36.pyc -------------------------------------------------------------------------------- /Src/api/predict.py: -------------------------------------------------------------------------------- 1 | from flask import Blueprint, jsonify, request 2 | import pandas as pd 3 | import dill as pickle 4 | import json 5 | from Src.utils.DataPreparation import * 6 | 7 | predict_api = Blueprint("predict_api", __name__) 8 | 9 | @predict_api.route('/predict', methods=['POST']) 10 | def apicall(): 11 | """ 12 | API Call 13 | Pandas dataframe (sent as a payload) from API Call 14 | """ 15 | 16 | try: 17 | test_json_dump = json.dumps(request.get_json()) 18 | test_df = pd.read_json(test_json_dump, orient='records') 19 | # Because of request processing Age is being considered as object, but it needs to be float type. 20 | test_df['Age'] = test_df.Age.convert_objects(convert_numeric=True) 21 | #Getting the PassengerId separated out 22 | passenger_ids = test_df['PassengerId'] 23 | 24 | except Exception as e: 25 | print(':::: Exception occurred while reading json content ::::') 26 | raise e 27 | 28 | if test_df.empty: 29 | return(bad_request()) 30 | else: 31 | #Load the saved model 32 | (f'Model loading..') 33 | loaded_model = None 34 | with open('./Src/ml-model/voting_classifier_v1.pk','rb') as model: 35 | loaded_model = pickle.load(model) 36 | 37 | # Before we make any prediction, lets pre-process first. 38 | data_preparation = DataPreparation() 39 | test_df = data_preparation.preprocess(test_df) 40 | print(f'After pre-process test df - \n {test_df}') 41 | print("The model has been loaded...doing predictions now...") 42 | predictions = loaded_model.predict(test_df) 43 | 44 | prediction_series = list(pd.Series(predictions)) 45 | 46 | final_predictions = pd.DataFrame({'PassengerId': passenger_ids, 'Survived': prediction_series}) 47 | 48 | responses = jsonify(predictions=final_predictions.to_json(orient="records")) 49 | responses.status_code = 200 50 | 51 | return (responses) 52 | -------------------------------------------------------------------------------- /Src/ml-model/voting_classifier_v1.pk: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amdp-chauhan/complete-deployable-ml-solution/8921374a6daee8671405fedc237652696b1f2a9a/Src/ml-model/voting_classifier_v1.pk -------------------------------------------------------------------------------- /Src/templates/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 |

Hello.

5 |

This app provides solution for {{ app_name }}

6 | 7 | -------------------------------------------------------------------------------- /Src/utils/ClassificationModelBuilder.py: -------------------------------------------------------------------------------- 1 | # Library Import 2 | import numpy as np # linear algebra 3 | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) 4 | from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier, GradientBoostingClassifier, VotingClassifier 5 | from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, LogisticRegression, LogisticRegressionCV 6 | from xgboost import XGBClassifier 7 | from sklearn.tree import DecisionTreeClassifier 8 | from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV 9 | from sklearn.metrics import f1_score, accuracy_score 10 | from sklearn.preprocessing import LabelEncoder 11 | from sklearn.neighbors import KNeighborsClassifier 12 | import dill as pickle 13 | from .DataPreparation import * 14 | 15 | 16 | # Ignoring all warnings 17 | import warnings 18 | warnings.filterwarnings("ignore") 19 | import os 20 | 21 | # read dataset 22 | train_df = pd.read_csv('./Data/train.csv') 23 | 24 | # global random state 25 | rand_state_ = 42 26 | 27 | """ 28 | After analysing, visualising and going through few kernels, my observations are: 29 | - Features like 'PassengerId' and 'Ticket' seems of no use, so we should remove them. 30 | - 'Age' and 'Fare' are of type float, so it will be better if we take their ceil value and convert them to integer. 31 | - 'Cabin' contains more than 77% of missing values, so it should get discarded because we are not expected to fill this many missing values on our own and even if we do then model will loose its significance. 32 | - From 'Name', we can create a new feature called 'Title' It may help model in performing better, not sure though, will see. 33 | - We have 'SibSp' and 'Parch' features which shows person's Siblings, Spouse, Parents and children. We can use this feature to come with two new features called 'FamilySize' and 'IsAlone'. 34 | - Newly created feature 'FamilySize' can be used to create a new feature called 'FarePerHead'. 35 | """ 36 | 37 | 38 | """ 39 | This class will take care of model's hyper parameter tuning, prediction on train/test dataset and evaluation 40 | """ 41 | class Modeling(object): 42 | 43 | def __init__(self, test_train_ratio): 44 | 45 | self.classifiers = {} 46 | self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(train_df.drop(['Survived'], axis=1), train_df.Survived, test_size=test_train_ratio, random_state=rand_state_) 47 | 48 | 49 | def evaluate_model(self, modelName, train_predictions, test_predictions): 50 | 51 | self.classifiers[modelName] = { 52 | 'TrainingAccuracy': accuracy_score(self.y_train, train_predictions), 53 | 'TestAccuracy': accuracy_score(self.y_test, test_predictions) 54 | } 55 | 56 | 57 | def fit_and_predict_using_RandomSearchCV(self, classifier): 58 | random_cv_model = RandomizedSearchCV(estimator=classifier['instance'], param_distributions=classifier['param_grid'], cv=10) 59 | random_cv_model.fit(self.X_train, self.y_train) 60 | self.evaluate_model(classifier['name'], random_cv_model.predict(self.X_train), random_cv_model.predict(self.X_test)) 61 | self.classifiers[classifier['name']]['Estimator'] = random_cv_model.estimator 62 | 63 | return self.classifiers[classifier['name']] 64 | 65 | 66 | def voting_classifier(self, classifier_names): 67 | 68 | selected_classifiers = [(classifier_name, self.classifiers[classifier_name]['Estimator']) for classifier_name in classifier_names] 69 | voting_classifier = VotingClassifier(estimators=selected_classifiers, voting='hard') 70 | voting_classifier.fit(self.X_train, self.y_train) 71 | self.evaluate_model(voting_classifier.__class__.__name__, voting_classifier.predict(self.X_train), voting_classifier.predict(self.X_test)) 72 | self.evaluate_model(voting_classifier.__class__.__name__, voting_classifier.predict(self.X_train), voting_classifier.predict(self.X_test)) 73 | self.classifiers[voting_classifier.__class__.__name__]['Estimator'] = voting_classifier 74 | return self.classifiers[voting_classifier.__class__.__name__] 75 | 76 | 77 | """ 78 | Processing Datasets 79 | """ 80 | data_preparation = DataPreparation() 81 | train_df = data_preparation.preprocess(train_df) 82 | 83 | 84 | """ 85 | Building and comparing Models 86 | """ 87 | model_ops = Modeling(3/10) 88 | 89 | classifiers = [ 90 | { 91 | 'name': 'DecisionTreeClassifier', 92 | 'instance': DecisionTreeClassifier(), 93 | 'param_grid': { 94 | 'splitter': ['best', 'random'], 95 | 'criterion': ['gini', 'entropy'], 96 | 'max_depth': [3, 4], 97 | 'min_samples_split': [2, 3, 4], 98 | 'max_features': ['sqrt'], 99 | 'random_state': [rand_state_] 100 | } 101 | }, { 102 | 'name': 'RandomForestClassifier', 103 | 'instance': RandomForestClassifier(), 104 | 'param_grid': { 105 | 'n_estimators': [10, 30, 60, 90, 100], 106 | 'criterion': ['gini', 'entropy'], 107 | 'max_depth': [3, 4], 108 | 'min_samples_split': [2, 3, 4], 109 | 'max_features': ['sqrt'], 110 | 'random_state': [rand_state_] 111 | } 112 | }, { 113 | 'name': 'XGBClassifier', 114 | 'instance': XGBClassifier(), 115 | 'param_grid': { 116 | 'max_depth': [3, 4, 5], 117 | 'learning_rate': [.1, .06, .03, .01], 118 | 'n_estimators': [80, 100, 120], 119 | 'booster': ['gbtree', 'gblinear', 'dart'], 120 | 'gamma': [0, 2, 4], 121 | 'random_state': [rand_state_] 122 | } 123 | }, { 124 | 'name': 'KNeighborsClassifier', 125 | 'instance': KNeighborsClassifier(), 126 | 'param_grid': { 127 | 'n_neighbors': [5, 6, 7, 8, 9], 128 | 'weights': ['uniform', 'distance'], 129 | 'algorithm': ['ball_tree', 'kd_tree', 'brute'], 130 | 'p': [1, 2] 131 | } 132 | }, { 133 | 'name': 'ExtraTreesClassifier', 134 | 'instance': ExtraTreesClassifier(), 135 | 'param_grid': { 136 | 'n_estimators': [20, 40, 80], 137 | 'min_samples_split': [2, 3, 4], 138 | 'criterion': ['gini', 'entropy'], 139 | 'max_features': ['sqrt'] 140 | } 141 | }, { 142 | 'name': 'RidgeClassifierCV', 143 | 'instance': RidgeClassifierCV(), 144 | 'param_grid': { 145 | 'alphas': [(0.05, 0.1, 0.5, 1, 2)] 146 | } 147 | }, { 148 | 'name': 'AdaBoostClassifier', 149 | 'instance': AdaBoostClassifier(), 150 | 'param_grid': { 151 | 'base_estimator': [ 152 | # Decided hyper parameter values after RandomSearch Cross Validation 153 | DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=1, min_samples_split=3, random_state=rand_state_, splitter='best'), 154 | XGBClassifier(booster='dart', gamma=2, learning_rate=0.1, max_depth=3, n_estimators=100, random_state=rand_state_) 155 | ], 156 | 'n_estimators': [50, 70, 90], 157 | 'random_state': [rand_state_], 158 | 'algorithm': ['SAMME', 'SAMME.R'], 159 | 'learning_rate': [0.8, 1.0, 1.3] 160 | } 161 | }, { 162 | 'name': 'BaggingClassifier', 163 | 'instance': BaggingClassifier(), 164 | 'param_grid': { 165 | 'base_estimator': [ 166 | # Decided hyper parameter values after RandomSearch Cross Validation 167 | DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=1, min_samples_split=3, random_state=rand_state_, splitter='best'), 168 | XGBClassifier(booster='dart', gamma=2, learning_rate=0.1, max_depth=3, n_estimators=100, random_state=rand_state_) 169 | ], 170 | 'n_estimators': [10, 20, 30], 171 | 'random_state': [rand_state_], 172 | 'bootstrap': [True, False], 173 | 'bootstrap_features': [True, False] 174 | } 175 | }, { 176 | 'name': 'GradientBoostingClassifier', 177 | 'instance': GradientBoostingClassifier(), 178 | 'param_grid': { 179 | 'loss': ['deviance', 'exponential'], 180 | 'n_estimators': [100, 120, 150], 181 | 'random_state': [rand_state_], 182 | 'min_samples_split': [2, 3, 4], 183 | 'max_depth': [3, 4, 5], 184 | 'min_samples_split': [2, 3, 4] 185 | } 186 | } 187 | ] 188 | 189 | 190 | for classifier in classifiers: 191 | classifier_performance = model_ops.fit_and_predict_using_RandomSearchCV(classifier) 192 | print(f"{classifier['name']} Performance - \n{classifier_performance}") 193 | 194 | 195 | # We are going to use the SimpleVoting Ensemble technique to make our final predictions. 196 | voting_classifier = model_ops.voting_classifier(['AdaBoostClassifier', 'BaggingClassifier']) 197 | print(f'VotingClassifier Performance - \n{voting_classifier}') 198 | 199 | 200 | # Comparing performance of Classifiers 201 | score_df = pd.DataFrame([{'ModelName': name, 'Test Accuracy': props['TestAccuracy'], 'Training Accuracy': props['TrainingAccuracy']} for name, props in model_ops.classifiers.items()]) 202 | score_df.set_index('ModelName') 203 | print(f'All Classifiers Performance Table - \n{score_df}') 204 | 205 | 206 | # We are saving VotingClassifier trained instance, if you want you can save any other model/s as well. 207 | filename = 'voting_classifier_v1.pk' 208 | with open('./Src/ml-model/'+filename, 'wb') as file: 209 | pickle.dump(voting_classifier['Estimator'], file) -------------------------------------------------------------------------------- /Src/utils/DataPreparation.py: -------------------------------------------------------------------------------- 1 | # Library Import 2 | import numpy as np # linear algebra 3 | import pandas as pd # data processing 4 | from sklearn.preprocessing import LabelEncoder 5 | import re 6 | 7 | 8 | """ 9 | This class will take care of data preprocessing and feature engineering. 10 | """ 11 | class DataPreparation(): 12 | 13 | def __init__(self): 14 | pass 15 | 16 | 17 | def preprocess(self, df): 18 | df = self.fill_missing_values(df) 19 | df = self.feature_extraction(df) 20 | df = self.handle_categorical_variables(df) 21 | df = self.dimensionality_reduction(df) 22 | 23 | return df 24 | 25 | 26 | def fill_missing_values(self, df): 27 | df.Age = np.ceil(df.Age.fillna(df.Age.median())).astype(int) 28 | df.Embarked = df.Embarked.fillna(df.Embarked.mode()[0]) 29 | df.Fare = np.ceil(df.Fare.fillna(df.Fare.mean())).astype(int) 30 | 31 | return df 32 | 33 | 34 | def feature_extraction(self, df): 35 | # FamilySize includes a person, his parents, siblings, children and spouse 36 | df['FamilySize'] = df.SibSp + df.Parch + 1 37 | df['FarePerHead'] = (df.Fare/df.FamilySize).astype(int) 38 | df['IsAlone'] = df.FamilySize.apply(lambda x: 1 if x==1 else 0) 39 | # In AgeGroup, we have divided population into four groups - kid, teen, adult & old 40 | df['AgeGroup'] = df.Age.apply(lambda x: 'kid' if x<13 else 'teen' if x<20 else 'adult' if x<41 else 'old') 41 | 42 | # adding Title variable 43 | df['Title'] = df.Name.apply(lambda x: re.search('(?<=, )\w+', x).group(0)) 44 | df.Title.replace(to_replace=['Ms', 'Lady', 'the', 'Dona'], value='Mrs', inplace=True) 45 | df.Title.replace(to_replace=['Mme', 'Mlle'], value='Miss', inplace=True) 46 | df.Title.replace(to_replace=['Jonkheer', 'Sir', 'Capt', 'Don', 'Col', 'Major', 'Rev', 'Dr'], value='Mr', inplace=True) 47 | 48 | return df 49 | 50 | 51 | def handle_categorical_variables(self, df): 52 | df = pd.get_dummies(df, drop_first=True, columns=['Sex', 'Embarked']) 53 | df.AgeGroup = LabelEncoder().fit_transform(df.AgeGroup) 54 | df.Title = LabelEncoder().fit_transform(df.Title) 55 | 56 | return df 57 | 58 | 59 | def dimensionality_reduction(self, df): 60 | 61 | return df.drop(labels=['PassengerId','Name','Ticket', 'Cabin'], axis=1) 62 | -------------------------------------------------------------------------------- /Src/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amdp-chauhan/complete-deployable-ml-solution/8921374a6daee8671405fedc237652696b1f2a9a/Src/utils/__init__.py -------------------------------------------------------------------------------- /Src/utils/__pycache__/ClassificationModelBuilder.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amdp-chauhan/complete-deployable-ml-solution/8921374a6daee8671405fedc237652696b1f2a9a/Src/utils/__pycache__/ClassificationModelBuilder.cpython-36.pyc -------------------------------------------------------------------------------- /Src/utils/__pycache__/ClassifierModelBuilder.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amdp-chauhan/complete-deployable-ml-solution/8921374a6daee8671405fedc237652696b1f2a9a/Src/utils/__pycache__/ClassifierModelBuilder.cpython-36.pyc -------------------------------------------------------------------------------- /Src/utils/__pycache__/DataPreparation.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amdp-chauhan/complete-deployable-ml-solution/8921374a6daee8671405fedc237652696b1f2a9a/Src/utils/__pycache__/DataPreparation.cpython-36.pyc -------------------------------------------------------------------------------- /Src/utils/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amdp-chauhan/complete-deployable-ml-solution/8921374a6daee8671405fedc237652696b1f2a9a/Src/utils/__pycache__/__init__.cpython-36.pyc -------------------------------------------------------------------------------- /application.py: -------------------------------------------------------------------------------- 1 | from flask import Flask, jsonify, request, render_template, abort 2 | # from Src.utils import ClassificationModelBuilder # Uncomment only if you want to re-train your model 3 | from Src.api.predict import predict_api 4 | from jinja2 import TemplateNotFound 5 | 6 | application = Flask(__name__ , template_folder='./Src/templates') 7 | application.register_blueprint(predict_api, url_prefix='/titanic-survival-classification-model') 8 | 9 | 10 | # Loading home page 11 | @application.route('/', defaults={'page': 'index'}) 12 | @application.route('/') 13 | def show(page): 14 | 15 | try: 16 | print('home route') 17 | return render_template(f'{page}.html', app_name='Titanic Survival: Classification Problem') 18 | 19 | except TemplateNotFound: 20 | abort(404) 21 | 22 | 23 | # Handling 400 Error 24 | @application.errorhandler(400) 25 | def bad_request(error=None): 26 | 27 | message = { 28 | 'status': 400, 29 | 'message': 'Bad Request: ' + request.url + '--> Please check your data payload...', 30 | } 31 | resp = jsonify(message) 32 | resp.status_code = 400 33 | 34 | return resp 35 | 36 | 37 | # run application 38 | if __name__ == "__main__": 39 | application.run() -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | Click==7.0 2 | dill==0.2.9 3 | Flask==1.0.2 4 | itsdangerous==1.1.0 5 | Jinja2==2.10 6 | MarkupSafe==1.1.0 7 | numpy==1.15.4 8 | pandas==0.23.4 9 | python-dateutil==2.7.5 10 | pytz==2018.9 11 | scikit-learn==0.20.2 12 | scipy==1.2.0 13 | six==1.12.0 14 | sklearn==0.0 15 | Werkzeug==0.14.1 16 | xgboost==0.81 17 | --------------------------------------------------------------------------------