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Abhinav Sagar abhinavsagar

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@abhinavsagar
abhinavsagar / model.py
Created October 2, 2019 06:36
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
dataset = pd.read_csv('sales.csv')
dataset['rate'].fillna(0, inplace=True)
dataset['sales_in_first_month'].fillna(dataset['sales_in_first_month'].mean(), inplace=True)
@abhinavsagar
abhinavsagar / request.py
Created October 2, 2019 06:36
import requests
url = 'http://localhost:5000/results'
r = requests.post(url,json={'rate':5, 'sales_in_first_month':200, 'sales_in_second_month':400})
print(r.json())
@abhinavsagar
abhinavsagar / app.py
Created October 2, 2019 06:35
import numpy as np
from flask import Flask, request, jsonify, render_template
import pickle
app = Flask(__name__)
model = pickle.load(open('model.pkl', 'rb'))
@app.route('/')
def home():
return render_template('index.html')
@abhinavsagar
abhinavsagar / sales.csv
Created October 2, 2019 06:34
rate sales_in_first_month sales_in_second_month sales_in_third_month
2 500 300
4 300 650
four 600 200 400
nine 450 320 650
seven 600 250 350
five 550 200 700
@abhinavsagar
abhinavsagar / style.css
Created October 2, 2019 06:34
@import url(https://fonts.googleapis.com/css?family=Open+Sans);
html { width: 100%; height:100%; overflow:hidden; }
body {
width: 100%;
height:100%;
font-family: 'Helvetica';
background: #000;
color: #fff;
@abhinavsagar
abhinavsagar / index.html
Created October 2, 2019 06:33
<!DOCTYPE html>
<html >
<head>
<meta charset="UTF-8">
<title>Deployment Tutorial 1</title>
<link href='https://fonts.googleapis.com/css?family=Pacifico' rel='stylesheet' type='text/css'>
<link href='https://fonts.googleapis.com/css?family=Arimo' rel='stylesheet' type='text/css'>
<link href='https://fonts.googleapis.com/css?family=Hind:300' rel='stylesheet' type='text/css'>
<link href='https://fonts.googleapis.com/css?family=Open+Sans+Condensed:300' rel='stylesheet' type='text/css'>
<link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
@abhinavsagar
abhinavsagar / predicted_actual.py
Created September 7, 2019 08:22
i=0
prop_class=[]
mis_class=[]
for i in range(len(Y_test)):
if(np.argmax(Y_test[i])==np.argmax(Y_pred_tta[i])):
prop_class.append(i)
if(len(prop_class)==8):
break
@abhinavsagar
abhinavsagar / roc_auc.py
Created September 7, 2019 08:21
from sklearn.metrics import roc_auc_score, auc
from sklearn.metrics import roc_curve
roc_log = roc_auc_score(np.argmax(Y_test, axis=1), np.argmax(Y_pred_tta, axis=1))
false_positive_rate, true_positive_rate, threshold = roc_curve(np.argmax(Y_test, axis=1), np.argmax(Y_pred_tta, axis=1))
area_under_curve = auc(false_positive_rate, true_positive_rate)
plt.plot([0, 1], [0, 1], 'r--')
plt.plot(false_positive_rate, true_positive_rate, label='AUC = {:.3f}'.format(area_under_curve))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
@abhinavsagar
abhinavsagar / classification_report.py
Created September 7, 2019 08:20
from sklearn.metrics import classification_report
classification_report( np.argmax(Y_test, axis=1), np.argmax(Y_pred_tta, axis=1))
from sklearn.metrics import confusion_matrix
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
if normalize:
@abhinavsagar
abhinavsagar / loss_accuracy.py
Created September 7, 2019 08:19
history_df = pd.DataFrame(history.history)
history_df[['loss', 'val_loss']].plot()
history_df = pd.DataFrame(history.history)
history_df[['acc', 'val_acc']].plot()