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<main>
<article id="content">
<header>
<h1 class="title">Module <code>0214-PROJECT_Auto_Documentation</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># importing toy &amp; real world datasets from the scikit-learn library
from sklearn import datasets
def dataload():
&#34;&#34;&#34;
upload toy datasets from scikit-learn
&#34;&#34;&#34;
data = None
return data
def datafetch(file_name):
&#34;&#34;&#34;
upload real world datasets from scikit-learn
&#34;&#34;&#34;
data = None
print(&#34;reading data from:&#34;, file_name)
return data
# standard descriptive statistic analysis of data
def descriptive_statistic(df):
&#34;&#34;&#34;
Provides brief descriptive statistics on dataset.
Takes dataframe as input.
&#34;&#34;&#34;
print(&#34;Type : &#34;, None, &#34;\n\n&#34;)
print(&#34;Shape : &#34;, None)
print(&#34;Head -- \n&#34;, None)
print(&#34;\n\n Tail -- \n&#34;, None)
print(&#34;Describe : &#34;, None)
# model selection
def model_selection(df):
&#34;&#34;&#34;
Takes dateframe as input. Performs foward/backward stepwise
regression. Returns best model for both methods.
&#34;&#34;&#34;
null_fit = None
foward_step = None
backward_step = None
return foward_step, backward_step
# model accuracy
def MSE_fit(fit):
&#34;&#34;&#34;
Takes in a fitted model as the input.
Calculates the MSU of the fitted model.
Outputs the model&#39;s MSE.
&#34;&#34;&#34;
MSE = None
return MSE
def accuracy_metrics(fit, MSE):
&#34;&#34;&#34;
This function is used for model validation. It returns a dictionary
of several regression model accuracy metrics. Its inputs are a fitted model
and the MSE of the fitted model.
&#34;&#34;&#34;
d = dict()
sumObj = None
SSE = None
n = None
p = None
pr = None
d[&#39;R2&#39;] = None
d[&#39;R2ad&#39;] = None
d[&#39;AIC&#39;] = None
d[&#39;BIC&#39;] = None
d[&#39;PRESS&#39;] = None
d[&#39;Cp&#39;]= None
return d
# test code
file_name = &#39;data.csv&#39;
a = datafetch(file_name)
print(a)
b = descriptive_statistic(a)
print(b)
c = model_selection(a)
print(c)
d = MSE_fit(c)
print(d)
print(accuracy_metrics(c, d))</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="0214-PROJECT_Auto_Documentation.MSE_fit"><code class="name flex">
<span>def <span class="ident">MSE_fit</span></span>(<span>fit)</span>
</code></dt>
<dd>
<section class="desc"><p>Takes in a fitted model as the input.
Calculates the MSU of the fitted model.
Outputs the model's MSE.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def MSE_fit(fit):
&#34;&#34;&#34;
Takes in a fitted model as the input.
Calculates the MSU of the fitted model.
Outputs the model&#39;s MSE.
&#34;&#34;&#34;
MSE = None
return MSE</code></pre>
</details>
</dd>
<dt id="0214-PROJECT_Auto_Documentation.accuracy_metrics"><code class="name flex">
<span>def <span class="ident">accuracy_metrics</span></span>(<span>fit, MSE)</span>
</code></dt>
<dd>
<section class="desc"><p>This function is used for model validation. It returns a dictionary
of several regression model accuracy metrics. Its inputs are a fitted model
and the MSE of the fitted model.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def accuracy_metrics(fit, MSE):
&#34;&#34;&#34;
This function is used for model validation. It returns a dictionary
of several regression model accuracy metrics. Its inputs are a fitted model
and the MSE of the fitted model.
&#34;&#34;&#34;
d = dict()
sumObj = None
SSE = None
n = None
p = None
pr = None
d[&#39;R2&#39;] = None
d[&#39;R2ad&#39;] = None
d[&#39;AIC&#39;] = None
d[&#39;BIC&#39;] = None
d[&#39;PRESS&#39;] = None
d[&#39;Cp&#39;]= None
return d</code></pre>
</details>
</dd>
<dt id="0214-PROJECT_Auto_Documentation.datafetch"><code class="name flex">
<span>def <span class="ident">datafetch</span></span>(<span>file_name)</span>
</code></dt>
<dd>
<section class="desc"><p>upload real world datasets from scikit-learn</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def datafetch(file_name):
&#34;&#34;&#34;
upload real world datasets from scikit-learn
&#34;&#34;&#34;
data = None
print(&#34;reading data from:&#34;, file_name)
return data</code></pre>
</details>
</dd>
<dt id="0214-PROJECT_Auto_Documentation.dataload"><code class="name flex">
<span>def <span class="ident">dataload</span></span>(<span>)</span>
</code></dt>
<dd>
<section class="desc"><p>upload toy datasets from scikit-learn</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def dataload():
&#34;&#34;&#34;
upload toy datasets from scikit-learn
&#34;&#34;&#34;
data = None
return data</code></pre>
</details>
</dd>
<dt id="0214-PROJECT_Auto_Documentation.descriptive_statistic"><code class="name flex">
<span>def <span class="ident">descriptive_statistic</span></span>(<span>df)</span>
</code></dt>
<dd>
<section class="desc"><p>Provides brief descriptive statistics on dataset.
Takes dataframe as input.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def descriptive_statistic(df):
&#34;&#34;&#34;
Provides brief descriptive statistics on dataset.
Takes dataframe as input.
&#34;&#34;&#34;
print(&#34;Type : &#34;, None, &#34;\n\n&#34;)
print(&#34;Shape : &#34;, None)
print(&#34;Head -- \n&#34;, None)
print(&#34;\n\n Tail -- \n&#34;, None)
print(&#34;Describe : &#34;, None)</code></pre>
</details>
</dd>
<dt id="0214-PROJECT_Auto_Documentation.model_selection"><code class="name flex">
<span>def <span class="ident">model_selection</span></span>(<span>df)</span>
</code></dt>
<dd>
<section class="desc"><p>Takes dateframe as input. Performs foward/backward stepwise
regression. Returns best model for both methods.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def model_selection(df):
&#34;&#34;&#34;
Takes dateframe as input. Performs foward/backward stepwise
regression. Returns best model for both methods.
&#34;&#34;&#34;
null_fit = None
foward_step = None
backward_step = None
return foward_step, backward_step</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="0214-PROJECT_Auto_Documentation.MSE_fit" href="#0214-PROJECT_Auto_Documentation.MSE_fit">MSE_fit</a></code></li>
<li><code><a title="0214-PROJECT_Auto_Documentation.accuracy_metrics" href="#0214-PROJECT_Auto_Documentation.accuracy_metrics">accuracy_metrics</a></code></li>
<li><code><a title="0214-PROJECT_Auto_Documentation.datafetch" href="#0214-PROJECT_Auto_Documentation.datafetch">datafetch</a></code></li>
<li><code><a title="0214-PROJECT_Auto_Documentation.dataload" href="#0214-PROJECT_Auto_Documentation.dataload">dataload</a></code></li>
<li><code><a title="0214-PROJECT_Auto_Documentation.descriptive_statistic" href="#0214-PROJECT_Auto_Documentation.descriptive_statistic">descriptive_statistic</a></code></li>
<li><code><a title="0214-PROJECT_Auto_Documentation.model_selection" href="#0214-PROJECT_Auto_Documentation.model_selection">model_selection</a></code></li>
</ul>
</li>
</ul>
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