All Discussions Tagged 'normalization' - Data Science Central2020-02-20T00:00:33Zhttps://www.datasciencecentral.com/forum/topic/listForTag?tag=normalization&feed=yes&xn_auth=noInsight in datatag:www.datasciencecentral.com,2019-09-18:6448529:Topic:8892032019-09-18T08:58:11.152ZIlan Perezhttps://www.datasciencecentral.com/profile/IlanPerez
<p>I have a situation with a client. They have 4 sources of data and they are wanting to create a single metric out of these four values to gain a generalised insight into how the company is going overall.</p>
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<p>The problem is that each source has a completely different scale and are not really comparable.</p>
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<p>Source A has a scale in the millions where as Source B's scale is in the hundreds.</p>
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<p>Further to this we wanted to weight each source as some provide more…</p>
<p>I have a situation with a client. They have 4 sources of data and they are wanting to create a single metric out of these four values to gain a generalised insight into how the company is going overall.</p>
<p></p>
<p>The problem is that each source has a completely different scale and are not really comparable.</p>
<p></p>
<p>Source A has a scale in the millions where as Source B's scale is in the hundreds.</p>
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<p>Further to this we wanted to weight each source as some provide more value than others.</p>
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<p>We decided to scale all four between 0 and 1 using this formula</p>
<p><span><span class="mrow" id="MathJax-Span-24"><span class="msubsup" id="MathJax-Span-25"><span class="mi" id="MathJax-Span-26">z</span><span class="mi" id="MathJax-Span-27">i</span></span><span class="mo" id="MathJax-Span-28">= </span><span class="mfrac" id="MathJax-Span-29"><span class="mrow" id="MathJax-Span-30"><span class="msubsup" id="MathJax-Span-31"><span class="mi" id="MathJax-Span-32">x</span><span class="mi" id="MathJax-Span-33">i</span></span><span class="mo" id="MathJax-Span-34">− </span><span class="mo" id="MathJax-Span-35">min</span><span class="mo" id="MathJax-Span-36">(</span><span class="mi" id="MathJax-Span-37">x</span><span class="mo" id="MathJax-Span-38">) / </span></span><span class="mrow" id="MathJax-Span-39"><span class="mo" id="MathJax-Span-40">max</span><span class="mo" id="MathJax-Span-41">(</span><span class="mi" id="MathJax-Span-42">x</span><span class="mo" id="MathJax-Span-43">)</span><span class="mo" id="MathJax-Span-44">−</span><span class="mo" id="MathJax-Span-45">min</span><span class="mo" id="MathJax-Span-46">(</span><span class="mi" id="MathJax-Span-47">x</span><span class="mo" id="MathJax-Span-48">)</span></span></span></span></span></p>
<p>and while its works I am confused as to what insight I can get out of the numbers.</p>
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<p>Here is the google sheet I am preparing with</p>
<p><a href="https://docs.google.com/spreadsheets/d/1Eua7tmqD3B0l3M04QnXDcU5HCAmFIfP65lsA7l52604/edit?usp=sharing">https://docs.google.com/spreadsheets/d/1Eua7tmqD3B0l3M04QnXDcU5HCAmFIfP65lsA7l52604/edit?usp=sharing</a></p>
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<p>If you look at cell H14 and H15 can you say that March was 3 times worse than Feb because the March score was 1.1 and the Feb was 3.2?</p>
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<p>Thanks in advance</p>
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<p></p> Mean normalization and Feature scaling.tag:www.datasciencecentral.com,2018-06-25:6448529:Topic:7383672018-06-25T06:15:04.683ZDeepak Chawlahttps://www.datasciencecentral.com/profile/DeepakChawla
<p>Currently, I am working on multivariable regression problem data set and I came across a problem that my dataset have so many features with different features scale value and so google suggest me use mean normalization and feature scaling techniques but I don't understand which one we have to use mean normalization or feature scaling and why we are using mean normalization and feature scaling techniques simultaneously.?? and from where below formula derived .</p>
<p>x(i) = x(i) - mean(x) /…</p>
<p>Currently, I am working on multivariable regression problem data set and I came across a problem that my dataset have so many features with different features scale value and so google suggest me use mean normalization and feature scaling techniques but I don't understand which one we have to use mean normalization or feature scaling and why we are using mean normalization and feature scaling techniques simultaneously.?? and from where below formula derived .</p>
<p>x(i) = x(i) - mean(x) / std(x)</p>
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