akash-ch2812

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akash-ch2812 / result.html

Created September 1, 2020 14:14

	<!DOCTYPE html>
	<html lang="en" dir="ltr">
	<head>
	<meta charset="utf-8">
	<title>Result</title>
	</head>
	<body>
	<h1>Prediction</h1>

	{{ result }}

akash-ch2812 / index.html

Last active September 1, 2020 13:57

	<!DOCTYPE html>
	<html lang="en" dir="ltr">
	<head>
	<meta charset="utf-8">
	<title>Home page</title>
	</head>
	<body>
	<h1>Titanic survival prediction</h1>
	<form action="{% urls 'result' %}">
	{% csrf_token %}

akash-ch2812 / views.py

Last active September 1, 2020 14:31

	from django.shortcuts import render

	# our home page view
	def home(request):
	return render(request, 'index.html')


	# custom method for generating predictions
	def getPredictions(pclass, sex, age, sibsp, parch, fare, C, Q, S):
	import pickle

akash-ch2812 / urls.py

Last active September 1, 2020 13:25

	# default present
	from django.contrib import admin
	from django.urls import path

	# add this to import our views file
	from Titanic_Survial_Prediction_Web import views

	urlpatterns = [
	path('admin/', admin.site.urls),

akash-ch2812 / Create_Titanic_Model.py

Created September 1, 2020 12:21

	import pandas as pd
	import numpy as np

	from sklearn.model_selection import train_test_split
	from sklearn.preprocessing import MinMaxScaler
	from sklearn.linear_model import LogisticRegression

	from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

	# load dataset

akash-ch2812 / Search.py

Created August 14, 2020 09:14

	from spacy.matcher import PhraseMatcher
	from scipy import spatial

	# method for searching keyword from the text
	def search_for_keyword(keyword, doc_obj, nlp):
	phrase_matcher = PhraseMatcher(nlp.vocab)
	phrase_list = [nlp(keyword)]
	phrase_matcher.add("Text Extractor", None, *phrase_list)

	matched_items = phrase_matcher(doc_obj)

akash-ch2812 / Similar_Keywords.py

Created August 14, 2020 09:06

	# convert keywords to vector
	def createKeywordsVectors(keyword, nlp):
	doc = nlp(keyword) # convert to document object

	return doc.vector


	# method to find cosine similarity
	def cosineSimilarity(vect1, vect2):
	# return cosine distance

akash-ch2812 / Data_Preprocessing.py

Last active September 6, 2020 02:38

	import spacy
	import PyPDF2

	# spacy english model (large)
	nlp = spacy.load('en_core_web_lg')

	# method for reading a pdf file
	def readPdfFile(filename, folder_name):

	# storing path of PDF-Documents folder

akash-ch2812 / Generate_Captions.py

Last active July 24, 2020 08:37

	import matplotlib.pyplot as plt
	import seaborn as sns
	from PIL import Image
	%matplotlib inline

	# method for generating captions
	def generate_captions(model, image, tokenizer.word_index, max_caption_length, tokenizer.index_word):

	# input is <start>
	input_text = '<start>'

akash-ch2812 / Train_Caption_Model.py

Created July 24, 2020 08:20

	# get training data
	train_data = create_trianing_data(train_image_captions, train_image_features, tokenizer, max_caption_len, vocab_length, 32)

	# initialize model
	model = create_model(max_caption_len, vocab_len)

	steps_per_epochs = len(train_image_captions)//32

	# compile model
	model.compile(optimizer='adam', loss='categorical_crossentropy')

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