├── README.md ├── adult.csv ├── data_head.PNG ├── flask_project_structure.PNG ├── income_prediction_blog ├── index.html ├── model.pkl ├── preprocessing.py ├── result.html └── script.py /README.md: -------------------------------------------------------------------------------- 1 | # machine_learning_model_using_flask_web_framework 2 | This repository contains the whole process to build a machine learning model using python and also explain the steps to deploy it using Flask web framework. 3 | -------------------------------------------------------------------------------- /data_head.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hoshangk/machine_learning_model_using_flask_web_framework/e597bb690d83e3d0666e09507d3a33619a53e3b2/data_head.PNG -------------------------------------------------------------------------------- /flask_project_structure.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hoshangk/machine_learning_model_using_flask_web_framework/e597bb690d83e3d0666e09507d3a33619a53e3b2/flask_project_structure.PNG -------------------------------------------------------------------------------- /income_prediction_blog: -------------------------------------------------------------------------------- 1 | Machine learning:- A process which is widely used for prediction. N number of algoriths are available in various libraries 2 | which can be used for prediction. 3 | 4 | We build a prediction model on historic data using different machine learning algorithms and classifiers, plot the results and calculate the accuracy of the model on the testing data. 5 | 6 | Building/Training a model using various algoriths on large dataset is one part of the data. But using thse model within different application is second p[art of deploying machine learning in real world. 7 | 8 | Now what? To put it to use in order to predict the new data we have to deploy it over the internet so that the outside world can use it. In this article I talk about how I trained a machine learning model, created a web application on it using Flask 9 | 10 | 11 | Decision Tree 12 | 13 | Decision Tree is a well known supervised machine learning algorithm because its ease to use, resilient and flexible. I have implemented the algorithm on Adult dataset from UCI machine learning repository. You can find the dataset from my git hub repository 14 | 15 | ===============Here is the Code========== 16 | 17 | 18 | 19 | 20 | 21 | Getting the dataset is not the end. We have to preprocess the data.It means we need to clean the dataset. Cleaning of the dataset includes different types of process like remove missing values, fill NA values etc. 22 | 23 | Preprocessing the dataset 24 | It consists of 14 attributes and a class label telling whether the income of the individual is less than or more than 50K a year. These attributes range from the age of the person, the working class label to relationship status and the race the person belongs to. The information about all the attributes can be found here. 25 | 26 | 27 | At first we find and remove any missing values from the data. I have replaced the missing values with the mode value in that column. There are many other ways to replace missing values but for this type of dataset it seemed most optimal. 28 | 29 | df=df.drop(['fnlwgt','educational-num'], axis=1) 30 | 31 | col_names = df.columns 32 | for c in col_names: 33 | df[c] = df[c].replace("?", numpy.NaN) 34 | 35 | df = df.apply(lambda x:x.fillna(x.value_counts().index[0])) 36 | 37 | 38 | Machine learning algorithm cannot preocess categorical data values. It can only process numnerical values. 39 | To fit the data into prediction model, we need convert categorical values to numerical ones. Before that, we will evaluate if any transformation on categorical columns are necessary. 40 | 41 | Discretisation is a common way to make categorical data more tidy and meaningful. I applied discretisation on column marital_status where they are narrowed down to only to values married or not married. Later I apply label encoder in the remaining data columns. Also there are two redundant columns {‘education’, ‘educational-num’}, therefore I have removed one of them. 42 | 43 | df.replace(['Divorced', 'Married-AF-spouse', 44 | 'Married-civ-spouse', 'Married-spouse-absent', 45 | 'Never-married','Separated','Widowed'], 46 | ['divorced','married','married','married', 47 | 'not married','not married','not married'], inplace = True) 48 | 49 | category_col =['workclass', 'race', 'education','marital-status', 'occupation', 50 | 'relationship', 'gender', 'native-country', 'income'] 51 | labelEncoder = preprocessing.LabelEncoder() 52 | 53 | 54 | mapping_dict={} 55 | for col in category_col: 56 | df[col] = labelEncoder.fit_transform(df[col]) 57 | le_name_mapping = dict(zip(labelEncoder.classes_, labelEncoder.transform(labelEncoder.classes_))) 58 | mapping_dict[col]=le_name_mapping 59 | print(mapping_dict) 60 | 61 | {'workclass': {' ?': 0, ' Federal-gov': 1, ' Local-gov': 2, ' Never-worked': 3, ' Private': 4, ' Self-emp-inc': 5, ' Self-emp-not-inc': 6, ' State-gov': 7, ' Without-pay': 8}, 'race': {' Amer-Indian-Eskimo': 0, ' Asian-Pac-Islander': 1, ' Black': 2, ' Other': 3, ' White': 4}, 'education': {' 10th': 0, ' 11th': 1, ' 12th': 2, ' 1st-4th': 3, ' 5th-6th': 4, ' 7th-8th': 5, ' 9th': 6, ' Assoc-acdm': 7, ' Assoc-voc': 8, ' Bachelors': 9, ' Doctorate': 10, ' HS-grad': 11, ' Masters': 12, ' Preschool': 13, ' Prof-school': 14, ' Some-college': 15}, 'marital-status': {' Divorced': 0, ' Married-AF-spouse': 1, ' Married-civ-spouse': 2, ' Married-spouse-absent': 3, ' Never-married': 4, ' Separated': 5, ' Widowed': 6}, 'occupation': {' ?': 0, ' Adm-clerical': 1, ' Armed-Forces': 2, ' Craft-repair': 3, ' Exec-managerial': 4, ' Farming-fishing': 5, ' Handlers-cleaners': 6, ' Machine-op-inspct': 7, ' Other-service': 8, ' Priv-house-serv': 9, ' Prof-specialty': 10, ' Protective-serv': 11, ' Sales': 12, ' Tech-support': 13, ' Transport-moving': 14}, 'relationship': {' Husband': 0, ' Not-in-family': 1, ' Other-relative': 2, ' Own-child': 3, ' Unmarried': 4, ' Wife': 5}, 'gender': {' Female': 0, ' Male': 1}, 'native-country': {' ?': 0, ' Cambodia': 1, ' Canada': 2, ' China': 3, ' Columbia': 4, ' Cuba': 5, ' Dominican-Republic': 6, ' Ecuador': 7, ' El-Salvador': 8, ' England': 9, ' France': 10, ' Germany': 11, ' Greece': 12, ' Guatemala': 13, ' Haiti': 14, ' Holand-Netherlands': 15, ' Honduras': 16, ' Hong': 17, ' Hungary': 18, ' India': 19, ' Iran': 20, ' Ireland': 21, ' Italy': 22, ' Jamaica': 23, ' Japan': 24, ' Laos': 25, ' Mexico': 26, ' Nicaragua': 27, ' Outlying-US(Guam-USVI-etc)': 28, ' Peru': 29, ' Philippines': 30, ' Poland': 31, ' Portugal': 32, ' Puerto-Rico': 33, ' Scotland': 34, ' South': 35, ' Taiwan': 36, ' Thailand': 37, ' Trinadad&Tobago': 38, ' United-States': 39, ' Vietnam': 40, ' Yugos lavia': 41}, 'income': {' <=50K': 0, ' >50K': 1}} 62 | 63 | 64 | Fitting the model 65 | After preprocessing the data, the data is ready to feed it to the machine learning algorithm. We then slice the data separating the labels with the attributes. Now, we split the dataset into two halves, one for training and on for testing. This is achieved using train_test_split() function of sklearn. 66 | 67 | from sklearn.model_selection import train_test_split 68 | from sklearn.tree import DecisionTreeClassifier 69 | from sklearn.metrics import accuracy_score 70 | X = df.values[:, 0:12] 71 | Y = df.values[:,12] 72 | 73 | I have used decision tree classifier here as a predicting model. I fed the training part of the data to train the model. 74 | Once training is done, we test what is the accuracy of the model by providing testing part of the data to the model. 75 | With this we achieve an accuracy of 84% approximately. Now in order to use this model with new unknown data we need to save the model so that we can predict the values later. For this we make use of pickle in python which is a powerful algorithm for serializing and de-serializing a Python object structure. 76 | 77 | 78 | X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size = 0.3, random_state = 100) 79 | dt_clf_gini = DecisionTreeClassifier(criterion = "gini", random_state = 100,max_depth=5, min_samples_leaf=5) 80 | dt_clf_gini.fit(X_train, y_train) 81 | y_pred_gini = dt_clf_gini.predict(X_test) 82 | 83 | print ("Desicion Tree using Gini Index\nAccuracy is ", accuracy_score(y_test,y_pred_gini)*100 ) 84 | 85 | #creating and training a model 86 | #serializing our model to a file called model.pkl 87 | import pickle 88 | pickle.dump(dt_clf_gini, open("model.pkl","wb")) 89 | 90 | Flask 91 | Flask is a python based microframework used for developing small scale websites. Flask is very easy to make Restful API's using python. 92 | 93 | As of now we have develop a model i.e model.pkl which can predict a class of the data based on various attribute of the data. The class label are Salary >=50K or <50K 94 | 95 | Now we will design a web application where user will input all the attribute values and the data will be given the model, based on the training given to the model, the model will predict the what should be the salary of the person wchose details has been fed. 96 | 97 | HTML Form 98 | For predicting the income from various attributes we first need to collect the data(new attribute values) and then use the decision tree model we build above to predict whether the income is more than 50K or less. Therefore, in order to collect the data we create html form which would contain all the different options to select from each attribute. Here, I have created a simple form using html only. You can review the code from [here]. If you want to make the form more interactive you can do so as well. 99 | 100 | Down load full code from github repository. 101 | 102 | 103 | Important: in-order to predict the data correctly the corresponding values of each label should match with the value of each input selected. 104 | For example — In the attribute Relationship there are 6 categorical values. These are converted to numerical like this {‘Husband’: 0, ‘Not-in-family’: 1, ‘Other-relative’: 2, ‘Own-child’: 3, ‘Unmarried’: 4, ‘Wife’: 5}. Therefore we need to put the same values to the html form. 105 | 106 | #prediction function 107 | def ValuePredictor(to_predict_list): 108 | to_predict = np.array(to_predict_list).reshape(1,12) 109 | loaded_model = pickle.load(open("model.pkl","rb")) 110 | result = loaded_model.predict(to_predict) 111 | return result[0] 112 | 113 | 114 | @app.route('/result',methods = ['POST']) 115 | def result(): 116 | if request.method == 'POST': 117 | to_predict_list = request.form.to_dict() 118 | to_predict_list=list(to_predict_list.values()) 119 | to_predict_list = list(map(int, to_predict_list)) 120 | result = ValuePredictor(to_predict_list) 121 | 122 | if int(result)==1: 123 | prediction='Income more than 50K' 124 | else: 125 | prediction='Income less that 50K' 126 | 127 | return render_template("result.html",prediction=prediction) 128 | 129 | Once the Data is posted from the form, the data should eb fed to the model. 130 | In the gist preprocessing.py above I have created a dictionary mapping_dict which stores the numerical values of all the categorical labels in the form of key and value. This would help in creating the html form. 131 | 132 | Flask script 133 | Before starting with the coding part, we need to download flask and some other libraries. Here, we make use of virtual environment, where all the libraries are managed and makes both the development and deployment job easier. 134 | 135 | Here is the code to run the code using virtual environment. 136 | 137 | mkdir income-prediction 138 | cd income-prediction 139 | python3 -m venv venv 140 | 141 | After the virtual environment is created we activate it. 142 | 143 | source venv/bin/activate 144 | Now let’s install Flask. 145 | 146 | pip install flask 147 | 148 | Lets create folder templates. In your application, you will use templates to render HTML which will display in the user’s browser. This folder contains our html form file index.html. 149 | 150 | mkdir templates 151 | 152 | Create script.py file in the project folder and copy the following code. 153 | 154 | Here we import the libraries, then using app=Flask(__name__) we create an instance of flask. @app.route('/') is used to tell flask what url should trigger the function index() and in the function index we use render_template('index.html') to display the script index.html in the browser. 155 | 156 | 157 | Let’s run the application 158 | export FLASK_APP=script.py #this line will work in linux 159 | 160 | set FLASK_APP=script.py # this it the code for wiondows. 161 | 162 | run flask 163 | 164 | This should run the application and launch a simple server. Open http://127.0.0.1:5000/ to see the html form. 165 | 166 | 167 | Predicting the income value 168 | When someone submits the form, the webpage should display the predicted value of income. For this, we require the model file(model.pkl) we created before, in the same project folder. 169 | 170 | Here after the form is submitted, the form values are stored in variable to_predict_list in the form of dictionary. We convert it into a list of the dictionary’s values and pass it as an argument to ValuePredictor() function. In this function, we load the model.pkl file and predict the new values and return the result. 171 | This result/prediction(Income more than or less than 50k) is then passed as an argument to the template engine with the html page to be displayed. 172 | Create the following result.html file and add it to templates folder. 173 | 174 | 175 | 176 | 177 |

{{ prediction }}

178 | 179 | 180 | 181 | Run the application again and it should predict the income after submitting the form. 182 | We have successfully created the Webapp. Now it’s time to use heroku to deploy it. 183 | This is how our project layout looks like — 184 | -------------------------------------------------------------------------------- /index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 |

Income Prediction Form

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92 | 93 | btw:[0-99999] 94 |
95 | 96 | btw:[0-4356] 97 |
98 | 99 | btw:[1-99] 100 |
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149 | 150 | -------------------------------------------------------------------------------- /model.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hoshangk/machine_learning_model_using_flask_web_framework/e597bb690d83e3d0666e09507d3a33619a53e3b2/model.pkl -------------------------------------------------------------------------------- /preprocessing.py: -------------------------------------------------------------------------------- 1 | import pandas 2 | from sklearn import preprocessing 3 | from sklearn.model_selection import train_test_split 4 | from sklearn.tree import DecisionTreeClassifier 5 | import numpy 6 | # Load dataset 7 | url = "C:/Users/Asus/Desktop/Suven/practise_DS/adult.csv" 8 | df = pandas.read_csv(url) 9 | 10 | # filling missing values 11 | 12 | col_names = df.columns 13 | for c in col_names: 14 | df[c] = df[c].replace("?", numpy.NaN) 15 | 16 | df = df.apply(lambda x:x.fillna(x.value_counts().index[0])) 17 | 18 | #discretisation 19 | df.replace(['Divorced', 'Married-AF-spouse', 20 | 'Married-civ-spouse', 'Married-spouse-absent', 21 | 'Never-married','Separated','Widowed'], 22 | ['divorced','married','married','married', 23 | 'not married','not married','not married'], inplace = True) 24 | 25 | #label Encoder 26 | category_col =['workclass', 'race', 'education','marital-status', 'occupation','relationship', 'gender', 'native-country', 'income'] 27 | labelEncoder = preprocessing.LabelEncoder() 28 | 29 | # creating a map of all the numerical values of each categorical labels. 30 | mapping_dict={} 31 | for col in category_col: 32 | df[col] = labelEncoder.fit_transform(df[col]) 33 | le_name_mapping = dict(zip(labelEncoder.classes_, labelEncoder.transform(labelEncoder.classes_))) 34 | mapping_dict[col]=le_name_mapping 35 | print(mapping_dict) 36 | 37 | #droping redundant columns 38 | df=df.drop(['fnlwgt','educational-num'], axis=1) 39 | 40 | 41 | X = df.values[:, 0:12] 42 | Y = df.values[:,12] 43 | 44 | X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size = 0.3, random_state = 100) 45 | dt_clf_gini = DecisionTreeClassifier(criterion = "gini", random_state = 100,max_depth=5, min_samples_leaf=5) 46 | dt_clf_gini.fit(X_train, y_train) 47 | y_pred_gini = dt_clf_gini.predict(X_test) 48 | 49 | print ("Desicion Tree using Gini Index\nAccuracy is ", accuracy_score(y_test,y_pred_gini)*100 ) 50 | 51 | #creating and training a model 52 | #serializing our model to a file called model.pkl 53 | import pickle 54 | pickle.dump(dt_clf_gini, open("model.pkl","wb")) 55 | 56 | -------------------------------------------------------------------------------- /result.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 |

{{ prediction }}

5 | 6 | -------------------------------------------------------------------------------- /script.py: -------------------------------------------------------------------------------- 1 | #importing libraries 2 | import os 3 | import numpy as np 4 | import flask 5 | import pickle 6 | from flask import Flask, render_template, request 7 | 8 | #creating instance of the class 9 | app=Flask(__name__) 10 | 11 | #to tell flask what url shoud trigger the function index() 12 | @app.route('/') 13 | @app.route('/index') 14 | def index(): 15 | return flask.render_template('index.html') 16 | #return "Hello World" 17 | 18 | #prediction function 19 | def ValuePredictor(to_predict_list): 20 | to_predict = np.array(to_predict_list).reshape(1,12) 21 | loaded_model = pickle.load(open("model.pkl","rb")) 22 | result = loaded_model.predict(to_predict) 23 | return result[0] 24 | 25 | 26 | @app.route('/result',methods = ['POST']) 27 | def result(): 28 | if request.method == 'POST': 29 | to_predict_list = request.form.to_dict() 30 | to_predict_list=list(to_predict_list.values()) 31 | to_predict_list = list(map(int, to_predict_list)) 32 | result = ValuePredictor(to_predict_list) 33 | 34 | if int(result)==1: 35 | prediction='Income more than 50K' 36 | else: 37 | prediction='Income less that 50K' 38 | 39 | return render_template("result.html",prediction=prediction) 40 | 41 | if __name__ == "__main__": 42 | app.run(debug=True) --------------------------------------------------------------------------------