app.py

from flask import Flask,render_template,request
import os
from werkzeug.utils import secure_filename

app = Flask(__name__)

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/prediction')
def predict():
    return render_template('dataloader.html')


@app.route('/uploadajax',methods=['POST'])
def upldfile():

    prod_mas = request.files['prod_mas']
    filename = secure_filename(prod_mas.filename)
    prod_mas.save(os.path.join('./static/Upload/',filename))
    
    import pandas as pd
    dataset = pd.read_csv('./static/Upload/'+filename)
    print(dataset)
    print("****************************")
    print(dataset.shape)
    print("****************************")
    print(dataset.head(5))
    print("****************************")
    print(dataset.info())

    from sklearn.model_selection import train_test_split
    predictors = dataset.drop('target',axis=1)#X Value
    target = dataset["target"]

    X_train,X_test,Y_train,Y_test = train_test_split(predictors,target,test_size=0.20,random_state=0)

    from sklearn.metrics import accuracy_score
    from sklearn.linear_model import LogisticRegression

    lr = LogisticRegression()

    #fitness model
    lr.fit(X_train,Y_train)    

    #Prediction
    y_pred_lr = lr.predict(X_test)

    #Accuracy Score
    print("Accuarcy Score: "+str(accuracy_score(y_pred_lr,Y_test)*100))

    return render_template('dataloader.html',data="Data Loaded Successfully")


if __name__ == "__main__":
    app.run(debug=True)