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Abstract

cts</a>, <a href="https://readmedium.com/implemented-big-data-projects-9973d14131ca?sk=f41dfc9c96be347127ab78ac998e06ee">Implemented Big Data Projects</a>, <a href="https://readmedium.com/implemented-cloud-machine-learning-projects-b5a34d1d7f8?sk=6fa9d02dde908aa397dcaeb02cf754b4">Implemented Cloud Machine Learning Projects</a>, <a href="https://readmedium.com/implemented-neural-networks-projects-d25a6476d72b?sk=022a810763e8e8366974c066fa9c1c85">Implemented Neural Networks Projects</a>, <a href="https://readmedium.com/implemented-opencv-projects-7406d9b89032?sk=eea2d41edcb2da4a87830dfb7d702524">Implemented OpenCV Projects</a>,<a href="https://readmedium.com/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd">Complete ML Research Papers Summarized</a>, <a href="https://readmedium.com/data-analytics-projects-series-b6abc25e4815?sk=571e1a7e344560ab7aa01d7af7004824&utm_campaign=Become%20a%20Tech%20Samurai&utm_medium=email&utm_source=Revue%20newsletter">Implemented Data Analytics projects</a></i></b>,<b><i> <a href="https://readmedium.com/implemented-data-visualization-projects-9576431db13d?sk=280a40c65eced3fd9febd11a40d68bf0">Implemented Data Visualization Projects</a>, <a href="https://readmedium.com/implemented-data-mining-projects-b448780b5869?sk=a41f09a7fe9c71566977dfd47ed76e9f">Implemented Data Mining Projects</a>, <a href="https://readmedium.com/implemented-natural-leaning-processing-projects-f5efa8c4cb31?sk=597f814c51b392abd8b2a9e28c1eebb5">Implemented Natural Leaning Processing Projects</a>, <a href="https://readmedium.com/day-1-of-30-days-of-machine-learning-ops-7c299e4b09be?sk=4ab48350a5c359fc157109e48b1d738f">MLOps </a>and <a href="https://readmedium.com/day-1-of-60-days-of-deep-learning-with-projects-series-4a5caa305cf6?sk=89f3d43dd450035546bf3a8cf85bb125">Deep Learning</a>, <a href="https://readmedium.com/60-days-of-applied-machine-learning-with-projects-series-cd975641da0a?sk=09cf1f30e912774cba6501c8bac5edde">Applied Machine Learning with Projects Series</a>, <a href="https://readmedium.com/30-days-of-pytorch-with-projects-series-737941e5aa4f?sk=d0ead140034be9f1fff27d059b525221">PyTorch with Projects Series</a>, <a href="https://readmedium.com/30-days-of-tensorflow-and-keras-with-projects-series-f52e0815d696?sk=945bb73c32bc967b7e056f894fab7626">Tensorflow and Keras with Projects Series</a>, <a href="https://readmedium.com/day-1-of-30-days-of-scikit-learn-series-with-projects-76341935e5fd?sk=44a6845c53109c2482c368bdb7924e46">Scikit Learn Series with Projects</a>, <a href="https://readmedium.com/day-1-of-15-days-of-time-series-analysis-and-forecasting-with-projects-series-5ba3b6cf7528?sk=7a5826927d95b8fd22deae9ee53bc54d">Time Series Analysis and Forecasting with Projects Series</a>, <a href="https://readmedium.com/day-1-of-ml-system-design-case-studies-series-ml-system-design-basics-dbf7765b3c0c?sk=9ce5aee0a8b5208be05ac5284872e91b">ML System Design Case Studies Series</a> videos will be published on our youtube channel ( just launched).</i></b></p><p id="4b19"><b><i>Subscribe today!</i></b></p><div id="1520" class="link-block"> <a href="https://www.youtube.com/@ignito5917/about"> <div> <div> <h2>Ignito</h2> <div><h3>Excited to share that we have launched our Youtube channel — Ignito to cover all the projects and coding exercise for …</h3></div> <div><p>www.youtube.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*N9OmxhpEw0AuQEey)"></div> </div> </div> </a> </div><h2 id="9083">Tech Newsletter —</h2><blockquote id="8abe"><p>If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to <b>Tech Brew :</b></p></blockquote><div id="8d5c" class="link-block"> <a href="https://naina0405.substack.com/"> <div> <div> <h2>Ignito</h2> <div><h3>Data Science, ML, AI and more… Click to read Ignito, by Naina Chaturvedi, a Substack publication. Launched 7 months…</h3></div> <div><p>naina0405.substack.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*_ER1J-h50iqAjH70)"></div> </div> </div> </a> </div><p id="6719">Let’s dive in!</p><h2 id="3984">Import necessary Libraries</h2><div id="aff6"><pre><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt %matplotlib inline <span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd <span class="hljs-keyword">import</span> warnings <span class="hljs-title">warnings</span>.simplefilter('ignore') <span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np <span class="hljs-keyword">import</span> yellowbrick <span class="hljs-keyword">as</span> yb <span class="hljs-title">from</span> yellowbrick.contrib.scatter <span class="hljs-keyword">import</span> ScatterVisualizer <span class="hljs-title">from</span> yellowbrick.features.radviz <span class="hljs-keyword">import</span> RadViz <span class="hljs-title">from</span> yellowbrick.features.pcoords <span class="hljs-keyword">import</span> ParallelCoordinates <span class="hljs-title">from</span> yellowbrick.features.rankd <span class="hljs-keyword">import</span> Rank2D</pre></div><h2 id="edb1">Anscombe’s Quartet</h2><div id="077f"><pre><span class="hljs-meta">#data</span></pre></div><div id="c7ec"><pre><span class="hljs-attribute">x</span> = np.array([<span class="hljs-number">10</span>, <span class="hljs-number">8</span>, <span class="hljs-number">13</span>, <span class="hljs-number">9</span>, <span class="hljs-number">11</span>, <span class="hljs-number">14</span>, <span class="hljs-number">6</span>, <span class="hljs-number">4</span>, <span class="hljs-number">12</span>, <span class="hljs-number">7</span>, <span class="hljs-number">5</span>]) <span class="hljs-attribute">y1</span> = np.array([<span class="hljs-number">8</span>.<span class="hljs-number">04</span>, <span class="hljs-number">6</span>.<span class="hljs-number">95</span>, <span class="hljs-number">7</span>.<span class="hljs-number">58</span>, <span class="hljs-number">8</span>.<span class="hljs-number">81</span>, <span class="hljs-number">8</span>.<span class="hljs-number">33</span>, <span class="hljs-number">9</span>.<span class="hljs-number">96</span>, <span class="hljs-number">7</span>.<span class="hljs-number">24</span>, <span class="hljs-number">4</span>.<span class="hljs-number">26</span>, <span class="hljs-number">10</span>.<span class="hljs-number">84</span>, <span class="hljs-number">4</span>.<span class="hljs-number">82</span>, <span class="hljs-number">5</span>.<span class="hljs-number">68</span>]) <span class="hljs-attribute">y2</span> = np.array([<span class="hljs-number">9</span>.<span class="hljs-number">14</span>, <span class="hljs-number">8</span>.<span class="hljs-number">14</span>, <span class="hljs-number">8</span>.<span class="hljs-number">74</span>, <span class="hljs-number">8</span>.<span class="hljs-number">77</span>, <span class="hljs-number">9</span>.<span class="hljs-number">26</span>, <span class="hljs-number">8</span>.<span class="hljs-number">10</span>, <span class="hljs-number">6</span>.<span class="hljs-number">13</span>, <span class="hljs-number">3</span>.<span class="hljs-number">10</span>, <span class="hljs-number">9</span>.<span class="hljs-number">13</span>, <span class="hljs-number">7</span>.<span class="hljs-number">26</span>, <span class="hljs-number">4</span>.<span class="hljs-number">74</span>]) <span class="hljs-attribute">y3</span> = np.array([<span class="hljs-number">7</span>.<span class="hljs-number">46</span>, <span class="hljs-number">6</span>.<span class="hljs-number">77</span>, <span class="hljs-number">12</span>.<span class="hljs-number">74</span>, <span class="hljs-number">7</span>.<span class="hljs-number">11</span>, <span class="hljs-number">7</span>.<span class="hljs-number">81</span>, <span class="hljs-number">8</span>.<span class="hljs-number">84</span>, <span class="hljs-number">6</span>.<span class="hljs-number">08</span>, <span class="hljs-number">5</span>.<span class="hljs-number">39</span>, <span class="hljs-number">8</span>.<span class="hljs-number">15</span>, <span class="hljs-number">6</span>.<span class="hljs-number">42</span>, <span class="hljs-number">5</span>.<span class="hljs-number">73</span>]) <span class="hljs-attribute">x4</span> = np.array([<span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">19</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>]) <span class="hljs-attribute">y4</span> = np.array([<span class="hljs-number">6</span>.<span class="hljs-number">58</span>, <span class="hljs-number">5</span>.<span class="hljs-number">76</span>, <span class="hljs-number">7</span>.<span class="hljs-number"

Options

71</span>, <span class="hljs-number">8</span>.<span class="hljs-number">84</span>, <span class="hljs-number">8</span>.<span class="hljs-number">47</span>, <span class="hljs-number">7</span>.<span class="hljs-number">04</span>, <span class="hljs-number">5</span>.<span class="hljs-number">25</span>, <span class="hljs-number">12</span>.<span class="hljs-number">50</span>, <span class="hljs-number">5</span>.<span class="hljs-number">56</span>, <span class="hljs-number">7</span>.<span class="hljs-number">91</span>, <span class="hljs-number">6</span>.<span class="hljs-number">89</span>])</pre></div><h2 id="666c">Load Data</h2><div id="99d9"><pre><span class="hljs-meta"># Load the classification data set</span> <span class="hljs-class"><span class="hljs-keyword">data</span> = pd.read_csv('<span class="hljs-type">Path</span> <span class="hljs-title">to</span> <span class="hljs-title">the</span> <span class="hljs-title">file</span> /<span class="hljs-title">data</span>.<span class="hljs-title">csv'</span>)</span></pre></div><div id="e35a"><pre><span class="hljs-comment"># features of interest</span> <span class="hljs-attr">f</span> = [<span class="hljs-string">'temperature'</span>,<span class="hljs-string">'relative humidity'</span>, <span class="hljs-string">'light'</span>, <span class="hljs-string">'C02'</span>, <span class="hljs-string">'humidity'</span>] <span class="hljs-attr">classes</span> = [<span class="hljs-string">'unoccupied'</span>,<span class="hljs-string">'occupied'</span>]</pre></div><div id="bf76"><pre><span class="hljs-meta"># instances and target</span> <span class="hljs-type">X</span> = <span class="hljs-class"><span class="hljs-keyword">data</span>[f]</span> <span class="hljs-title">y</span>=<span class="hljs-class"><span class="hljs-keyword">data</span>.occupancy</span></pre></div><h2 id="6a33">Scatter Plot</h2><div id="e775"><pre><span class="hljs-attr">v</span> = ScatterVisualizer(x=<span class="hljs-string">'light'</span>,y=<span class="hljs-string">'C02'</span>,classes=classes,size=(<span class="hljs-number">800</span>,<span class="hljs-number">600</span>))</pre></div><div id="9a6a"><pre>v<span class="hljs-selector-class">.fit</span>(X,y) v<span class="hljs-selector-class">.transform</span>(X) v<span class="hljs-selector-class">.poof</span>()</pre></div><p id="8b10">Output —</p><figure id="a18c"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*f7c2K4PKONA8qaj3Bo2V8g.png"><figcaption></figcaption></figure><h2 id="9d77">RadViz</h2><div id="fdd5"><pre>v = <span class="hljs-built_in">RadViz</span>(classes=classes,features=f,size=(<span class="hljs-number">800</span>,<span class="hljs-number">600</span>)) v<span class="hljs-selector-class">.fit</span>(X,y) v<span class="hljs-selector-class">.transform</span>(X) v<span class="hljs-selector-class">.poof</span>()</pre></div><p id="3536">Output —</p><figure id="e64a"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*hlk2oIAOkXkJ25UjTHQliw.png"><figcaption></figcaption></figure><h2 id="4bc9">Parallel Coordinates Plot</h2><div id="eb3b"><pre><span class="hljs-comment"># Instantiate </span> <span class="hljs-attr">v</span> = ParallelCoordinates( <span class="hljs-attr">classes</span>=classes, features=f,normalize=<span class="hljs-string">'standard'</span>,sample=<span class="hljs-number">0.1</span>,size=(<span class="hljs-number">800</span>,<span class="hljs-number">600</span>)</pre></div><div id="ff55"><pre>) v<span class="hljs-selector-class">.fit</span>(X,y) v<span class="hljs-selector-class">.transform</span>(X) v<span class="hljs-selector-class">.poof</span>()</pre></div><p id="a704">Output —</p><figure id="9dc2"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*tDhpDDrz9tGicSxdJRLRDw.png"><figcaption></figcaption></figure><h2 id="a7ae">Rank Features</h2><div id="9ad9"><pre>v = <span class="hljs-built_in">Rank2D</span>(features=f,algorithm=<span class="hljs-string">'covariance'</span>) v<span class="hljs-selector-class">.fit</span>(X,y) v<span class="hljs-selector-class">.transform</span>(X) v<span class="hljs-selector-class">.poof</span>()</pre></div><p id="ec82">Output —</p><figure id="da02"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*OKK3F4vi2RpOuaOXTxVt0g.png"><figcaption></figcaption></figure><div id="f9c1"><pre>v = <span class="hljs-built_in">Rank2D</span>(features=f,algorithm=<span class="hljs-string">'pearson'</span>) v<span class="hljs-selector-class">.fit</span>(X,y) v<span class="hljs-selector-class">.transform</span>(X) v<span class="hljs-selector-class">.poof</span>()</pre></div><p id="8c38">Output —</p><figure id="0fae"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*xfPSXLy6Qxj6dYwbn3eXvA.png"><figcaption></figcaption></figure><p id="2bf3"><b><i>Learnings —</i></b></p><p id="477f">How to perform feature analysis techniques using visual tools from Yellowbrick.</p><p id="d38a"><b><i>Day 52: Coming soon!</i></b></p><p id="a039">Follow and Stay tuned. Keep coding :)</p><h1 id="a69d">For other projects, tune to —</h1><p id="b31f"><b>Build Machine Learning Pipelines( With Code)</b></p><div id="5b37" class="link-block"> <a href="https://medium.datadriveninvestor.com/build-machine-learning-pipelines-with-code-part-1-bd3ed7152124"> <div> <div> <h2>Build Machine Learning Pipelines( With Code) — Part 1</h2> <div><h3>Complete implementation…</h3></div> <div><p>medium.datadriveninvestor.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*KdToBD8RDMBH4jXM.png)"></div> </div> </div> </a> </div><p id="946c"><b>Recurrent Neural Network with Keras</b></p><div id="607d" class="link-block"> <a href="https://medium.datadriveninvestor.com/recurrent-neural-network-with-keras-b5b5f6fe5187"> <div> <div> <h2>Recurrent Neural Network with Keras</h2> <div><h3>Project Implementation and cheatsheet…</h3></div> <div><p>medium.datadriveninvestor.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*xs3Dya3qQBx6IU7C.png)"></div> </div> </div> </a> </div><p id="56e1"><b>Clustering Geolocation Data in Python using DBSCAN and K-Means</b></p><div id="2b3e" class="link-block"> <a href="https://medium.datadriveninvestor.com/clustering-geolocation-data-in-python-using-dbscan-and-k-means-3705d9f44522"> <div> <div> <h2>Clustering Geolocation Data in Python using DBSCAN and K-Means</h2> <div><h3>Project Implementation…</h3></div> <div><p>medium.datadriveninvestor.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*0uPCZnohdaPCO4NN.png)"></div> </div> </div> </a> </div><p id="a29c"><b>Facial Expression Recognition using Keras</b></p><div id="ccaa" class="link-block"> <a href="https://medium.datadriveninvestor.com/facial-expression-recognition-using-keras-cbdd661a0a54"> <div> <div> <h2>Facial Expression Recognition using Keras</h2> <div><h3>Project Implementation…</h3></div> <div><p>medium.datadriveninvestor.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*CGch7hzdjg1fpgKy.jpg)"></div> </div> </div> </a> </div><p id="0db7"><b>Hyperparameter Tuning with Keras Tuner</b></p><div id="6dff" class="link-block"> <a href="https://medium.datadriveninvestor.com/hyperparameter-tuning-with-keras-tuner-3a609d3fd85b"> <div> <div> <h2>Hyperparameter Tuning with Keras Tuner</h2> <div><h3>Project Implementation….</h3></div> <div><p>medium.datadriveninvestor.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*jlaEz8AZaptNWHEr.png)"></div> </div> </div> </a> </div><p id="fed8"><b>Custom Layers in Keras</b></p><div id="e4fd" class="link-block"> <a href="https://medium.datadriveninvestor.com/custom-layers-in-keras-de5f793217aa"> <div> <div> <h2>Custom Layers in Keras</h2> <div><h3>Code implementation …</h3></div> <div><p>medium.datadriveninvestor.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*1IH67KJadqeqeO01.png)"></div> </div> </div> </a> </div><p id="2ea9"><b><i>That’s it fellas. Peace out and keep coding :)</i></b></p><p id="ec55">Stay Tuned and of-course let me end this post with a quote by Steve Jobs ;)</p><p id="5004" type="7">“You have to be burning with an idea, or a problem, or a wrong that you want to right. If you’re not passionate enough from the start, you’ll never stick it out.”</p></article></body>

Day 51: 60 days of Data Science and Machine Learning Series

Yellowbrick combines scikit-learn with matplotlib and provides the scikit-learn API to produce visualizations for the machine learning workflow. A good reference point to understand the vastness of Yellowbrick and how to use it —

Yellowbrick: Machine Learning Visualization — Yellowbrick v1.3.post1 documentation

No matter your level of technical skill, you can be helpful. We appreciate bug reports, user testing, feature requests…

www.scikit-yb.org

Quick Start — Yellowbrick v1.3.post1 documentation

If you’re new to Yellowbrick, this guide will get you started and help you include visualizers in your machine learning…

www.scikit-yb.org

You can install yellowbrick using the command below —

$ pip install yellowbrick

In this post, we will analyze the text data using Yellowbrick and assess document similarity, topic modelling etc that are predicated on the notion of “similarity” between documents.

Some of the other best Series —

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All the Data Science and Machine Learning Resources

210 Machine Learning Projects

30 days of Machine Learning Ops

Projects Videos —

Subscribe today!

Ignito

Excited to share that we have launched our Youtube channel — Ignito to cover all the projects and coding exercise for …

www.youtube.com

Tech Newsletter —

If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :

Ignito

Data Science, ML, AI and more… Click to read Ignito, by Naina Chaturvedi, a Substack publication. Launched 7 months…

naina0405.substack.com

Let’s dive in!

Import necessary Libraries

import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import warnings
warnings.simplefilter('ignore')
import numpy as np
import yellowbrick as yb
from yellowbrick.contrib.scatter import ScatterVisualizer
from yellowbrick.features.radviz import RadViz
from yellowbrick.features.pcoords import ParallelCoordinates
from yellowbrick.features.rankd import Rank2D

Anscombe’s Quartet

#data

x = np.array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])
y1 = np.array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])
y2 = np.array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])
y3 = np.array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])
x4 = np.array([8, 8, 8, 8, 8, 8, 8, 19, 8, 8, 8])
y4 = np.array([6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89])

Load Data

# Load the classification data set
data = pd.read_csv('Path to the file /data.csv')

# features of interest
f = ['temperature','relative humidity', 'light', 'C02', 'humidity']
classes = ['unoccupied','occupied']

# instances and target
X = data[f]
y=data.occupancy

Scatter Plot

v = ScatterVisualizer(x='light',y='C02',classes=classes,size=(800,600))

v.fit(X,y)
v.transform(X)
v.poof()

Output —

RadViz

v = RadViz(classes=classes,features=f,size=(800,600))
v.fit(X,y)
v.transform(X)
v.poof()

Output —

Parallel Coordinates Plot

# Instantiate 
v = ParallelCoordinates(
    classes=classes, features=f,normalize='standard',sample=0.1,size=(800,600)

)
v.fit(X,y)
v.transform(X)
v.poof()

Output —

Rank Features

v = Rank2D(features=f,algorithm='covariance')
v.fit(X,y)
v.transform(X)
v.poof()

Output —

v = Rank2D(features=f,algorithm='pearson')
v.fit(X,y)
v.transform(X)
v.poof()

Output —

Learnings —

How to perform feature analysis techniques using visual tools from Yellowbrick.

Day 52: Coming soon!

Follow and Stay tuned. Keep coding :)

For other projects, tune to —

Build Machine Learning Pipelines( With Code)

Build Machine Learning Pipelines( With Code) — Part 1

Complete implementation…

medium.datadriveninvestor.com

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Project Implementation and cheatsheet…

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Clustering Geolocation Data in Python using DBSCAN and K-Means

Clustering Geolocation Data in Python using DBSCAN and K-Means

Project Implementation…

medium.datadriveninvestor.com

Facial Expression Recognition using Keras

Facial Expression Recognition using Keras

Project Implementation…

medium.datadriveninvestor.com

Hyperparameter Tuning with Keras Tuner

Hyperparameter Tuning with Keras Tuner

Project Implementation….

medium.datadriveninvestor.com

Custom Layers in Keras

Custom Layers in Keras

Code implementation …

medium.datadriveninvestor.com

That’s it fellas. Peace out and keep coding :)

Stay Tuned and of-course let me end this post with a quote by Steve Jobs ;)

“You have to be burning with an idea, or a problem, or a wrong that you want to right. If you’re not passionate enough from the start, you’ll never stick it out.”