Rusty  Shanahan

Rusty Shanahan

1596480120

Identifying Outliers — Part Three

In Part one and Part two, I introduced the mean and standard deviation (std) to set the outliers boundary. Here we are going to use Exponential Moving Average (EMA) as the boundary. The method is the same as before, the difference lays in the way of calculating the EMA mean and std.

1. Data preparation

Again we reuse the same dataset as before. The following code snippet will get you started. If you have been following these series along, you can ignore this part and jump to the next section straight away.

import pandas as pd 
import yfinance as yf

import matplotlib.pyplot as plt
plt.style.use('seaborn')
plt.rcParams['figure.dpi'] = 300
df = yf.download('AAPL',
                 start = '2000-01-01',
                 end= '2010-12-31')
d1 = pd.DataFrame(df['Adj Close'])
d1.rename(columns={'Adj Close':'adj_close'}, inplace=True)
d1['simple_rtn']=d1.adj_close.pct_change()
d1.head()

2. Using the Exponential Moving Average (EMA) mean and standard deviation as the boundary.

Here I am going to use the same window size as before, 21 days, to calculate the Exponential Moving Average (EMA) mean and standard deviation. One line of code is all we need.

d1[['mean', 'std']] = d1['simple_rtn'].ewm(span=21) \
                               .agg(['mean', 'std'])
d1.head()

✍Tip!

In case somebody asks, the _\_ in the code just mean that 2 lines of codes are related, sometimes the _( )_ do the same trick if you get an Indentation Error Messages.

#finance #data-science #stock-market #data-cleaning #machine-learning #data analysis

What is GEEK

Buddha Community

Identifying Outliers — Part Three

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 

Wanda  Huel

Wanda Huel

1601528520

What is an Outlier? Algorithms that are affected by outliers.

In statistics, an outlier is an observation point that is distant from other observations.

These extreme values need not necessarily impact the model performance or accuracy, but when they do they are called “Influential” points.

Note: _An _outlier_ is a data point that diverges from an overall pattern in a sample. An influential point is any point that has a large effect on the slope of a regression line._

Now the question arises that how we can detect these outliers and how to handle them?

Well before jumping straight into the solution lets explore that how the outliers being added to our dataset. What is the root cause of it.

#outliers #anomaly-detection #algorithms #outlier-detection #machine-learning

Rusty  Shanahan

Rusty Shanahan

1596480120

Identifying Outliers — Part Three

In Part one and Part two, I introduced the mean and standard deviation (std) to set the outliers boundary. Here we are going to use Exponential Moving Average (EMA) as the boundary. The method is the same as before, the difference lays in the way of calculating the EMA mean and std.

1. Data preparation

Again we reuse the same dataset as before. The following code snippet will get you started. If you have been following these series along, you can ignore this part and jump to the next section straight away.

import pandas as pd 
import yfinance as yf

import matplotlib.pyplot as plt
plt.style.use('seaborn')
plt.rcParams['figure.dpi'] = 300
df = yf.download('AAPL',
                 start = '2000-01-01',
                 end= '2010-12-31')
d1 = pd.DataFrame(df['Adj Close'])
d1.rename(columns={'Adj Close':'adj_close'}, inplace=True)
d1['simple_rtn']=d1.adj_close.pct_change()
d1.head()

2. Using the Exponential Moving Average (EMA) mean and standard deviation as the boundary.

Here I am going to use the same window size as before, 21 days, to calculate the Exponential Moving Average (EMA) mean and standard deviation. One line of code is all we need.

d1[['mean', 'std']] = d1['simple_rtn'].ewm(span=21) \
                               .agg(['mean', 'std'])
d1.head()

✍Tip!

In case somebody asks, the _\_ in the code just mean that 2 lines of codes are related, sometimes the _( )_ do the same trick if you get an Indentation Error Messages.

#finance #data-science #stock-market #data-cleaning #machine-learning #data analysis

jack son

jack son

1610638240

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