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We live in an age where having a good credit score is necessary to help you with any of your financial needs. Right from purchasing or renting a house, getting a loan for your car, house, education, we need a good credit history for someone to trust us with lending credit. This could be a friend or a trusted source or even a bank in the form of a loan/credit card. Now, if this is an unknown entity like a bank, they need some assurance from you that you would be able to repay their loans. When a person’s credit score isn’t easily available or even if it is, there are other factors involved before a lender decides whether a customer would default or not.
In this blog, I am using a Taiwanese credit default dataset from the UCI Machine Learning repository. The dataset is taken from https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset
I will be performing a binary classification by predicting a credit defaulter using age, sex, marital status, education, history of past payment, amount of bill statement, and past payment. This kind of model is useful for banks and credit card companies to determine if a person is likely to default on a bill/loan. We will be using feed-forward neural networks here.
Step 1: Exploring the data:
This dataset is also available on Kaggle:
DATA_FILENAME = "../input/default-of-credit-card-clients-dataset/UCI_Credit_Card.csv"
I will use Pandas to read the CSV dataset in:
Let’s understand the variables used in this dataset:
Information on the different attributes in this dataset
This dataset has 23 input columns which will be used to predict the target variable: default_payment_next_month. This target variable is a binary variable containing 1 for defaulter or 0 for non-defaulter.
#deep-learning #neural-networks #python #finance #pytorch #programming
1618317562
View more: https://www.inexture.com/services/deep-learning-development/
We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.
#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services
1603735200
The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
Also Read: Why Deep Learning DevCon Comes At The Right Time
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
Read an interview with Dipanjan Sarkar.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020
1595563500
In this tutorial, we’re going to create a model to predict House prices🏡 based on various factors across different markets.
The goal of this statistical analysis is to help us understand the relationship between house features and how these variables are used to predict house price.
Data used: Kaggle-kc_house Dataset
GitHub: you can find my source code here
First, Let’s import the data and have a look to see what kind of data we are dealing with:
#import required libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
#import Data
Data = pd.read_csv('kc_house_data.csv')
Data.head(5).T
#get some information about our Data-Set
Data.info()
Data.describe().transpose()
5 records of our dataset
Information about the dataset, what kind of data types are your variables
Statistical summary of your dataset
Our features are:
✔️**Date:**_ Date house was sold_
✔️**Price:**_ Price is prediction target_
✔️**_Bedrooms: _**Number of Bedrooms/House
✔️**Bathrooms:**_ Number of bathrooms/House_
✔️**Sqft_Living:**_ square footage of the home_
✔️**Sqft_Lot:**_ square footage of the lot_
✔️**Floors:**_ Total floors (levels) in house_
✔️**Waterfront:**_ House which has a view to a waterfront_
✔️**View:**_ Has been viewed_
✔️**Condition:**_ How good the condition is ( Overall )_
✔️**Grade:**_ grade given to the housing unit, based on King County grading system_
✔️**Sqft_Above:**_ square footage of house apart from basement_
✔️**Sqft_Basement:**_ square footage of the basement_
✔️**Yr_Built:**_ Built Year_
✔️**Yr_Renovated:**_ Year when house was renovated_
✔️**Zipcode:**_ Zip_
✔️**Lat:**_ Latitude coordinate_
✔️**_Long: _**Longitude coordinate
✔️**Sqft_Living15:**_ Living room area in 2015(implies — some renovations)_
✔️**Sqft_Lot15:**_ lotSize area in 2015(implies — some renovations)_
Let’s plot couple of features to get a better feel of the data
#visualizing house prices
fig = plt.figure(figsize=(10,7))
fig.add_subplot(2,1,1)
sns.distplot(Data['price'])
fig.add_subplot(2,1,2)
sns.boxplot(Data['price'])
plt.tight_layout()
#visualizing square footage of (home,lot,above and basement)
fig = plt.figure(figsize=(16,5))
fig.add_subplot(2,2,1)
sns.scatterplot(Data['sqft_above'], Data['price'])
fig.add_subplot(2,2,2)
sns.scatterplot(Data['sqft_lot'],Data['price'])
fig.add_subplot(2,2,3)
sns.scatterplot(Data['sqft_living'],Data['price'])
fig.add_subplot(2,2,4)
sns.scatterplot(Data['sqft_basement'],Data['price'])
#visualizing bedrooms,bathrooms,floors,grade
fig = plt.figure(figsize=(15,7))
fig.add_subplot(2,2,1)
sns.countplot(Data['bedrooms'])
fig.add_subplot(2,2,2)
sns.countplot(Data['floors'])
fig.add_subplot(2,2,3)
sns.countplot(Data['bathrooms'])
fig.add_subplot(2,2,4)
sns.countplot(Data['grade'])
plt.tight_layout()
With distribution plot of price, we can visualize that most of the prices are between 0 and around 1M with few outliers close to 8 million (fancy houses😉). It would make sense to drop those outliers in our analysis.
#linear-regression #machine-learning #python #house-price-prediction #deep-learning #deep learning
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In the previous blog, we looked into the fact why Few Shot Learning is essential and what are the applications of it. In this article, I will be explaining the Relation Network for Few-Shot Classification (especially for image classification) in the simplest way possible. Moreover, I will be analyzing the Relation Network in terms of:
Moreover, effectiveness will be evaluated on the accuracy, time required for training, and the number of required training parameters.
Please watch the GitHub repository to check out the implementations and keep updated with further experiments.
In few shot classification, our objective is to design a method which can identify any object images by analyzing few sample images of the same class. Let’s the take one example to understand this. Suppose Bob has a client project to design a 5 class classifier, where 5 classes can be anything and these 5 classes can even change with time. As discussed in previous blog, collecting the huge amount of data is very tedious task. Hence, in such cases, Bob will rely upon few shot classification methods where his client can give few set of example images for each classes and after that his system can perform classification young these examples with or without the need of additional training.
In general, in few shot classification four terminologies (N way, K shot, support set, and query set) are used.
At this point, someone new to this concept will have doubt regarding the need of support and query set. So, let’s understand it intuitively. Whenever humans sees any object for the first time, we get the rough idea about that object. Now, in future if we see the same object second time then we will compare it with the image stored in memory from the when we see it for the first time. This applied to all of our surroundings things whether we see, read, or hear. Similarly, to recognise new images from query set, we will provide our model a set of examples i.e., support set to compare.
And this is the basic concept behind Relation Network as well. In next sections, I will be giving the rough idea behind Relation Network and I will be performing different experiments on 102-flower dataset.
The Core idea behind Relation Network is to learn the generalized image representations for each classes using support set such that we can compare lower dimensional representation of query images with each of the class representations. And based on this comparison decide the class of each query images. Relation Network has two modules which allows us to perform above two tasks:
Training/Testing procedure:
We can define the whole procedure in just 5 steps.
Few things to know during the training is that we will use only images from the set of selective class, and during the testing, we will be using images from unseen classes. For example, from the 102-flower dataset, we will use 50% classes for training, and rest will be used for validation and testing. Moreover, in each episode, we will randomly select 5 classes to create the support and query set and follow the above 5 steps.
That is all need to know about the implementation point of view. Although the whole process is simple and easy to understand, I’ll recommend reading the published research paper, Learning to Compare: Relation Network for Few-Shot Learning, for better understanding.
#deep-learning #few-shot-learning #computer-vision #machine-learning #deep learning #deep learning
1623394101
Emojis are a wonderful method to express oneself.
This deep learning project automatically predicts emojis based on a given phrase.
In this machine learning project, we predict the emoji from the given text. This means we build a text classifier that returns an emoji that suits the given text.
Our systems should be aware of the relevant emoji to use at the proper moment.
The dataset consists of 2 parts, each is used for training and testing the deep learning model.
The training dataset contains 4 columns, one column being the text and the other contains IDs representing the emojis. Keep in mind that, here in our dataset the same sentence can have more than 1 emoji as a result.
You can download the emoji prediction dataset along with the project code in the next section.
To install the above modules, run the following command:
pip install numpy pandas tensorflow emoji
Please download the dataset & source code of the emoji prediction project (which is explained below): Emoji Prediction Python Code & Dataset
To build this text classifier, we follow the below steps:
1. Perform Exploratory Data Analysis (EDA).
2. Build the classifier model.
3. Train and evaluate the model.
Load the dataset using pandas.
import pandas as pdtrain = pd.read_csv ( ‘./Desktop/DataFlair/train_emoji.csv’ ,header=None ) test = pd.read_csv ( ‘./Desktop/DataFlair/test_emoji.csv’ ,header=None )
Now, let’s have a look at the datasets.
train.head ()
test.head ()
If you observe, there are 5 types of emojis in our dataset: heart, baseball, smile, disappointed, fork and knife.
Let’s store the above information in a dictionary for ease of use.
#deep learning tutorials #deep learning project #emoji prediction #emoji prediction python