Machine learnning flask project

inusha madurnaga/FlaskProject/Pipfile

+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+
+[packages]
+flask = "*"
+flask-sqlalchemy = "*"
+flask-marshmallow = "*"
+marshmallow-sqlalchemy = "*"
+
+[requires]
+python_version = "3.7"

inusha madurnaga/FlaskProject/app.py

+from flask import Flask, request, jsonify , render_template, redirect
+import os
+from flask_mysqldb import MySQL
+import yaml
+import model
+import numpy as np
+
+# init app
+app = Flask(__name__)
+
+# Configure db
+db = yaml.load(open('db.yaml')) # Load data from db.yaml file
+app.config['MYSQL_HOST'] = db['mysql_host']
+app.config['MYSQL_USER'] = db['mysql_user']
+app.config['MYSQL_PASSWORD'] = db['mysql_password']
+app.config['MYSQL_DB'] = db['mysql_db']
+
+mysql = MySQL(app)
+
+@app.route('/progress/<uniqueId>', methods = ['get'])
+def calculate_progress(uniqueId):
+
+    gradient_list = []
+    #Create connection to MYSql
+    cur = mysql.connection.cursor()
+
+    #get paper numbers of the student
+    cur.execute("SELECT PAPER_NO FROM paper_result_student_final WHERE UNIQUE_ID="+uniqueId)
+    paper_no_list = [item[0] for item in cur.fetchall()] # Fetched data add to the list
+
+    for i in range(0, len(paper_no_list)): # get paper number one by one
+        #Initialize list
+        weight_list = []
+        time_list = []
+        time_list_new = []
+
+        #get WEIGHT using id and paper number
+        cur.execute("SELECT WEIGHT FROM paper_result_student WHERE UNIQUE_ID= " + uniqueId + " AND PAPER_NO= " + str(paper_no_list[i]))
+        weight_list = [item[0] for item in cur.fetchall()]
+        for w in range(0, len(weight_list)): 
+            weight_list[i] = int(weight_list[w]) #convert string to int
+
+        #get TIME using id and paper number
+        cur.execute("SELECT TIME FROM paper_result_student WHERE UNIQUE_ID= " + uniqueId + " AND PAPER_NO= " + str(paper_no_list[i]))
+        time_list  = [item[0] for item in cur.fetchall()] #Tuple convert toList
+        for t in range(0, len(time_list)): 
+            (h, m, s) = time_list[t].split(':') #Time split to decimal number
+            time_in_decimal = int(h) * 3600 + int(m) * 60 + int(s)
+            time_list_new.append(time_in_decimal)
+
+        #pass weight_list and time_list_new to the Machine learning MODEL and get gradient
+        gradient = model.calculateGradientLinerRegressionAndSVR(weight_list ,  time_list_new)
+        gradient_list.append(float(gradient)) #Gradient convert to float and add to the list
+
+    #Close the db connections
+    cur.close()
+
+    #gradient_list Convert to numpy array
+    arr = np.array(gradient_list)
+    print(arr.tolist())
+    return({'learningProgresList': arr.tolist()})
+
+    # return render_template('index2.html', students=data )
+
+# Run Server
+if __name__ == '__main__':
+    app.run(debug=True)

inusha madurnaga/FlaskProject/db.yaml

+mysql_host: 'localhost'
+mysql_user: 'root'
+# Enter your password in field below
+mysql_password: '123'
+mysql_db: 'elearning_db'
\ No newline at end of file

inusha madurnaga/FlaskProject/model.py

+# Importing the libraries
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+from sklearn.impute import SimpleImputer
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score
+from sklearn.svm import SVR
+
+def calculateGradientLinerRegressionAndSVR(x_list,y_list):
+    # -------- Preprocessing Input data -------------------------------------------------------
+    # Importing the dataset
+    X = np.array(x_list)
+    Y = np.array(y_list)
+
+    # Splitting the dataset into the Training set and Test set
+    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)
+
+    # Reshape (Gives a new shape to an array without changing its data. | 1D Array to 2D Array)
+    X_train= X_train.reshape(-1, 1)
+    y_train= y_train.reshape(-1, 1)
+    X_test= X_test.reshape(-1, 1)
+    y_test= y_test.reshape(-1, 1)
+
+    # -------- Build the Model ***Linear Regression(LR)*****---------------------------------
+    # Fitting Simple Linear Regression to the Training set
+    regressor_LR = LinearRegression()
+    regressor_LR.fit(X_train,y_train) #training the algorithm
+    
+    # Predicting the Test set result
+    y_pred = regressor_LR.predict(X_test)
+
+    # Get Gradient
+    gradient_LR = regressor_LR.coef_
+
+    # Calulate r-Squared
+    r_squared_linear_regression = r2_score(y_test, y_pred)
+
+    # Visualising the Training set result
+    plt.scatter(X_train, y_train, color = 'red')
+    plt.plot(X_train, regressor_LR.predict(X_train), color = 'blue')
+    plt.title("Linear Regression")
+    plt.xlabel('Allocated weight for question' )
+    plt.ylabel('Spend time per question')
+    # plt.show()
+
+    # -------- Build the Model ***Support Vector Regression(SVR)*****-------------------------------------
+    # Fitting Support Vector Regression to the Training set
+    regressor_SVR = SVR(kernel = 'linear')
+    regressor_SVR.fit(X_train,y_train) #training the algorithm
+    
+    # Predicting the Test set result
+    y_pred = regressor_SVR.predict(X_test)
+
+    # Get Gradient
+    gradient_SVR = regressor_SVR.coef_
+
+    # Calulate r-Squared
+    r_squared_support_vector_regression = r2_score(y_test, y_pred)
+    
+
+    # Visualising the Training set result
+    plt.scatter(X_train, y_train, color = 'red')
+    plt.plot(X_train, regressor_SVR.predict(X_train), color = 'blue')
+    plt.title("Support Vector Regression")
+    plt.xlabel('Allocated weight for question' )
+    plt.ylabel('Spend time per question')
+    # plt.show()
+
+    # Compare and add best gradient to the list
+    if r_squared_linear_regression >= r_squared_support_vector_regression:
+      print("linear_regression_Gardient = ",gradient_LR)
+      print("linear_regression_Gardient = ",gradient_SVR)
+      print("r_squared_linear_regression = ",r_squared_linear_regression)
+      print("r_squared_support_vector_regression = ",r_squared_support_vector_regression)
+      return gradient_LR
+    else:
+      print("support_vector_regression_Gardient = ",gradient_LR)
+      print("support_vector_regression_Gardient = ",gradient_SVR)
+      print("r_squared_linear_regression = ",r_squared_linear_regression)
+      print("r_squared_support_vector_regression = ",r_squared_support_vector_regression)
+      return gradient_SVR
\ No newline at end of file