'added_python_code'

app.py

+from flask import Flask,jsonify,request
+from textblob import TextBlob
+import pandas as pd
+import mysql.connector
+from flask_cors import CORS
+import matplotlib.pyplot as plt
+
+
+app = Flask(__name__)
+CORS(app)
+
+@app.route('/prediction/<s_id>', methods=['GET'])
+def get(s_id):
+    mydb = mysql.connector.connect(
+    host="localhost",
+    user="root",
+    password="root",
+    database="inteljr"
+    )
+
+    student_id = s_id
+
+    mycursor = mydb.cursor()
+    sql = """SELECT duration, marks FROM tbl_result_summary WHERE student_id =  '%s' order by added_date desc""" % (s_id) 
+    mycursor.execute(sql)
+    myresult = mycursor.fetchall()
+    df=pd.DataFrame(myresult)
+    df.shape
+    df.head()
+    df.describe()
+
+
+    df = pd.read_sql_query(sql,mydb)
+    df.plot(x='duration',y='marks',style='*')
+    plt.title('Student Mark Prediction')
+    plt.xlabel('Hours')
+    plt.ylabel('Marks')
+    #plt.show()
+
+    x = df.iloc[:, :-1].values
+    y = df.iloc[:, 1].values
+
+    #Split the data into train and test dataset
+    from sklearn.model_selection import train_test_split
+    x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.5,random_state=0)
+
+    #Fitting Simple Linear regression data model to train data set
+    from sklearn.linear_model import LinearRegression
+    regressorObject=LinearRegression()
+    pd.plotting.register_matplotlib_converters()
+    regressorObject.fit(x_train,y_train)
+
+    #predict the test set
+    y_pred_test_data=regressorObject.predict(x_test)
+    y_pred_train_data=regressorObject.predict(x_train)
+
+    # Visualising the Training set results in a scatter plot
+    plt.scatter(x_train, y_train, color = 'red')
+    plt.plot(x_train, regressorObject.predict(x_train), color = 'blue')
+    plt.xlabel('Duration ')
+    plt.ylabel('Marks')
+    #plt.show()
+
+    print(regressorObject.intercept_)
+    print(regressorObject.coef_)
+    df=pd.DataFrame({'actual':y_pred_train_data, 'predicted':y_pred_test_data},columns = ['actual','predicted'])
+    
+    return df.to_json(orient = 'records')
+
+
+if __name__  == "__main__":
+    app.run(debug=True)
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