Five ways I learned to handle missing values in a dataset

When one starts to learn the art of working with Data, one of the most frequent problems he or she comes across is handling missing values in a dataset. Missing values may or may not impact the accuracy of the model that you are going to develop. Neither the less, it is important to handle them and it just takes some practice and common sense. It is a problem that ‘will’ be faced by anyone working with data (Because we think anyone can learn to work with data if they are willing to learn!). If we do not handle the missing data properly, we might end up drawing an inaccurate inference from the data.

Firstly, let us try to understand what are missing values?

Let us take a scenario, you are at your favorite restaurant and had an amazing dinner with your friends. When you are about to leave, the waiter hands over to you a form (or a Tablet in some cases) and requests you to fill a survey/feedback form. However, you are receiving back to back calls from your home and a constraint of time comes into the picture. In the process, we sometimes forget to fill in some details because of the lack of time or we just do not feel they are of much importance. And, many people miss filling out some fields or attributes.

I

n order to analyze the data, now either we can delete/skip these records or we can explore the possibility of filling them (which is also called imputing).

However, even if it seems convenient, we cannot go about deleting the records. Allow me to explain, maybe it is important to know for the restaurant to know if a person is employed or not. Maybe it is a possibility, employed people spend more money at the restaurant compared to counterparts like students.

We will first mention some techniques to handle missing values and then we will try to practice it on a dataset which is a semi-cleaned version of the infamous Titanic Dataset. Feel free to follow along and download the dataset from the GitHub link: https://github.com/Raman-rd/Handling-missing-values

How to find the total number of missing values in a dataset?

The very first and obvious method to deal with the missing values:

1. Dropping records with missing values

We can delete the records with missing values BUT only if we have a very huge dataset because otherwise it may lead to information loss and our model might miss out on important information and maybe it won’t perform as expected. Look at the following table as an example (Nan stands for missing values).

Notice that the third record has 3 missing values except one. If we have a huge dataset, maybe it is for the best if we delete this record because otherwise, we may have to estimate and fill the rest of the values. This method only makes sense if you have a lot of data so that the information is not missed out. Most of the resources we checked, suggest if more than ~70–75% of the data is missing we should drop that feature.

#titanic-dataset #scikit-learn #missing-values #data analysis

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Five ways I learned to handle missing values in a dataset

When one starts to learn the art of working with Data, one of the most frequent problems he or she comes across is handling missing values in a dataset. Missing values may or may not impact the accuracy of the model that you are going to develop. Neither the less, it is important to handle them and it just takes some practice and common sense. It is a problem that ‘will’ be faced by anyone working with data (Because we think anyone can learn to work with data if they are willing to learn!). If we do not handle the missing data properly, we might end up drawing an inaccurate inference from the data.

Firstly, let us try to understand what are missing values?

Let us take a scenario, you are at your favorite restaurant and had an amazing dinner with your friends. When you are about to leave, the waiter hands over to you a form (or a Tablet in some cases) and requests you to fill a survey/feedback form. However, you are receiving back to back calls from your home and a constraint of time comes into the picture. In the process, we sometimes forget to fill in some details because of the lack of time or we just do not feel they are of much importance. And, many people miss filling out some fields or attributes.

I

n order to analyze the data, now either we can delete/skip these records or we can explore the possibility of filling them (which is also called imputing).

However, even if it seems convenient, we cannot go about deleting the records. Allow me to explain, maybe it is important to know for the restaurant to know if a person is employed or not. Maybe it is a possibility, employed people spend more money at the restaurant compared to counterparts like students.

We will first mention some techniques to handle missing values and then we will try to practice it on a dataset which is a semi-cleaned version of the infamous Titanic Dataset. Feel free to follow along and download the dataset from the GitHub link: https://github.com/Raman-rd/Handling-missing-values

How to find the total number of missing values in a dataset?

The very first and obvious method to deal with the missing values:

1. Dropping records with missing values

We can delete the records with missing values BUT only if we have a very huge dataset because otherwise it may lead to information loss and our model might miss out on important information and maybe it won’t perform as expected. Look at the following table as an example (Nan stands for missing values).

Notice that the third record has 3 missing values except one. If we have a huge dataset, maybe it is for the best if we delete this record because otherwise, we may have to estimate and fill the rest of the values. This method only makes sense if you have a lot of data so that the information is not missed out. Most of the resources we checked, suggest if more than ~70–75% of the data is missing we should drop that feature.

#titanic-dataset #scikit-learn #missing-values #data analysis

How to Create an Image Clip Animation with Slider Controls using Only HTML & CSS

In this blog you’ll learn how to create an Image Clip Animation with Slider Controls using only HTML & CSS.

To create an Image Clip Animation with Slider Controls using only HTML & CSS. First, you need to create two Files one HTML File and another one is CSS File.

1: First, create an HTML file with the name of index.html

<!DOCTYPE html>
<html lang="en" dir="ltr">
  <head>
    <meta charset="utf-8">
    <title>Image Clip Animation | Codequs</title>
    <link rel="stylesheet" href="style.css">
  </head>
  <body>
    <div class="wrapper">
      <input type="radio" name="slide" id="one" checked>
      <input type="radio" name="slide" id="two">
      <input type="radio" name="slide" id="three">
      <input type="radio" name="slide" id="four">
      <input type="radio" name="slide" id="five">
      <div class="img img-1">
        <!-- <img src="images/img-1.jpg" alt="">
      </div>
      <div class="img img-2">
        <img src="images/img-2.jpg" alt="">
      </div>
      <div class="img img-3">
        <img src="images/img-3.jpg" alt="">
      </div>
      <div class="img img-4">
        <img src="images/img-4.jpg" alt="">
      </div>
      <div class="img img-5">
        <img src="images/img-5.jpg" alt="">
      </div>
      <div class="sliders">
        <label for="one" class="one"></label>
        <label for="two" class="two"></label>
        <label for="three" class="three"></label>
        <label for="four" class="four"></label>
        <label for="five" class="five"></label>
      </div>
    </div>
  </body>
</html>

2: Second, create a CSS file with the name of style.css

*{
  margin: 0;
  padding: 0;
  box-sizing: border-box;
}
body{
  min-height: 100vh;
  display: flex;
  align-items: center;
  justify-content: center;
  background: -webkit-linear-gradient(136deg, rgb(224,195,252) 0%, rgb(142,197,252) 100%);
}
.wrapper{
  position: relative;
  width: 700px;
  height: 400px;
}
.wrapper .img{
  position: absolute;
  width: 100%;
  height: 100%;
}
.wrapper .img img{
  height: 100%;
  width: 100%;
  object-fit: cover;
  clip-path: circle(0% at 0% 100%);
  transition: all 0.7s;
}
#one:checked ~ .img-1 img{
  clip-path: circle(150% at 0% 100%);
}
#two:checked ~ .img-1 img,
#two:checked ~ .img-2 img{
  clip-path: circle(150% at 0% 100%);
}
#three:checked ~ .img-1 img,
#three:checked ~ .img-2 img,
#three:checked ~ .img-3 img{
  clip-path: circle(150% at 0% 100%);
}
#four:checked ~ .img-1 img,
#four:checked ~ .img-2 img,
#four:checked ~ .img-3 img,
#four:checked ~ .img-4 img{
  clip-path: circle(150% at 0% 100%);
}
#five:checked ~ .img-1 img,
#five:checked ~ .img-2 img,
#five:checked ~ .img-3 img,
#five:checked ~ .img-4 img,
#five:checked ~ .img-5 img{
  clip-path: circle(150% at 0% 100%);
}
.wrapper .sliders{
  position: absolute;
  bottom: 20px;
  left: 50%;
  transform: translateX(-50%);
  z-index: 99;
  display: flex;
}
.wrapper .sliders label{
  border: 2px solid rgb(142,197,252);
  width: 13px;
  height: 13px;
  margin: 0 3px;
  border-radius: 50%;
  cursor: pointer;
  transition: all 0.3s ease;
}
#one:checked ~ .sliders label.one,
#two:checked ~ .sliders label.two,
#three:checked ~ .sliders label.three,
#four:checked ~ .sliders label.four,
#five:checked ~ .sliders label.five{
  width: 35px;
  border-radius: 14px;
  background: rgb(142,197,252);
}
.sliders label:hover{
  background: rgb(142,197,252);
}
input[type="radio"]{
  display: none;
}

Now you’ve successfully created an Image Clip Animation with Sliders using only HTML & CSS.

#html #css 

7 Ways to Handle Missing Values in Machine Learning

The real-world data often has a lot of missing values. The cause of missing values can be data corruption or failure to record data. The handling of missing data is very important during the preprocessing of the dataset as many machine learning algorithms do not support missing values.

This article covers 7 ways to handle missing values in the dataset:

1. Deleting Rows with missing values
2. Impute missing values for continuous variable
3. Impute missing values for categorical variable
4. Other Imputation Methods
5. Using Algorithms that support missing values
6. Prediction of missing values
7. Imputation using Deep Learning Library — Datawig

data = pd.read_csv("train.csv")
msno.matrix(data)

Delete Rows with Missing Values:

Missing values can be handled by deleting the rows or columns having null values. If columns have more than half of rows as null then the entire column can be dropped. The rows which are having one or more columns values as null can also be dropped.

Pros:

• A model trained with the removal of all missing values creates a robust model.

Cons:

• Loss of a lot of information.
• Works poorly if the percentage of missing values is excessive in comparison to the complete dataset.

#towards-data-science #data-science #artificial-intelligence #handling-missing-values #machine-learning

Inside ABCD, A Dataset To Build In-Depth Task-Oriented Dialogue Systems

According to a recent study, call centre agents’ spend approximately 82 percent of their total time looking at step-by-step guides, customer data, and knowledge base articles.

Traditionally, dialogue state tracking (DST) has served as a way to determine what a caller wants at a given point in a conversation. Unfortunately, these aspects are not accounted for in popular DST benchmarks. DST is the core part of a spoken dialogue system. It estimates the beliefs of possible user’s goals at every dialogue turn.

To reduce the burden on call centre agents and improve the SOTA of task-oriented dialogue systems, AI-powered customer service company ASAPP recently launched an action-based conversations dataset (ABCD). The dataset is designed to help develop task-oriented dialogue systems for customer service applications. ABCD consists of a fully labelled dataset with over 10,000 human dialogues containing 55 distinct user intents requiring sequences of actions constrained by company policies to accomplish tasks.

https://twitter.com/asapp/status/1397928363923177472

The dataset is currently available on GitHub.

#developers corner #asapp abcd dataset #asapp new dataset #build enterprise chatbot #chatbot datasets latest #customer support datasets #customer support model training #dataset for chatbots #dataset for customer datasets

Create Your Own Real Image Dataset with python (Deep Learning)

We have all worked with famous Datasets like CIFAR10 , MNIST , MNIST-fashion , CIFAR100, ImageNet and more. But , what about working on projects with custom made datasets according to your own needs. This also essentially makes you a complete master when it comes to handling image data

most of us probably know how to handle and store numerical and categorical data in csv files. But, the idea of storing Image data in files is very uncommon. Having said that , let’s see how to make our own image dataset with python

Code Begins Here :

1)Let’s start by importing the necessary libraries

#importing the libraries
import os 
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

2. Then , we need to set the path to the folder or directory that contains the image files. Here, the pictures that I need to upload are being stored in the path mentioned below

#setting the path to the directory containing the pics
path = '/media/ashwinhprasad/secondpart/pics'

#image-dataset #machine-learning-datasets #own-image-dataset #real-data #deep learning