diff --git a/docs/TDA_Regression.html b/docs/TDA_Prediction.html
similarity index 88%
rename from docs/TDA_Regression.html
rename to docs/TDA_Prediction.html
index e8ff2cce99f993c19f263b18679387680d459829..b620f9714e608f4bbc97051ad22a3a48bed34a23 100644
--- a/docs/TDA_Regression.html
+++ b/docs/TDA_Prediction.html
@@ -4,7 +4,7 @@
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.2" />
-<title>TDA_Regression API documentation</title>
+<title>TDA_Prediction API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
@@ -17,18 +17,14 @@
<main>
<article id="content">
<header>
-<h1 class="title">Module <code>TDA_Regression</code></h1>
+<h1 class="title">Module <code>TDA_Prediction</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
-<pre><code class="python"># importing toy & real world datasets from the scikit-learn library
-
-from sklearn import datasets
-
-def dataload():
+<pre><code class="python">def dataload():
"""
upload toy datasets from scikit-learn
"""
@@ -43,8 +39,6 @@ def datafetch(file_name):
print("reading data from:", file_name)
return data
-# standard descriptive statistic analysis of data
-
def descriptive_statistic(df):
"""
Provides brief descriptive statistics on dataset.
@@ -56,9 +50,6 @@ def descriptive_statistic(df):
print("\n\n Tail -- \n", None)
print("Describe : ", None)
-
-# model selection
-
def model_selection(df):
"""
Takes dateframe as input. Performs foward/backward stepwise
@@ -69,8 +60,6 @@ def model_selection(df):
backward_step = None
return foward_step, backward_step
-# model accuracy
-
def MSE_fit(fit):
"""
Takes in a fitted model as the input.
@@ -98,25 +87,7 @@ def accuracy_metrics(fit, MSE):
d['BIC'] = None
d['PRESS'] = None
d['Cp']= None
- return d
-
-# test code
-
-file_name = 'data.csv'
-
-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>
+ return d</code></pre>
</details>
</section>
<section>
@@ -126,7 +97,7 @@ print(accuracy_metrics(c, d))</code></pre>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
-<dt id="TDA_Regression.MSE_fit"><code class="name flex">
+<dt id="TDA_Prediction.MSE_fit"><code class="name flex">
<span>def <span class="ident">MSE_fit</span></span>(<span>fit)</span>
</code></dt>
<dd>
@@ -147,7 +118,7 @@ Outputs the model's MSE.</p></section>
return MSE</code></pre>
</details>
</dd>
-<dt id="TDA_Regression.accuracy_metrics"><code class="name flex">
+<dt id="TDA_Prediction.accuracy_metrics"><code class="name flex">
<span>def <span class="ident">accuracy_metrics</span></span>(<span>fit, MSE)</span>
</code></dt>
<dd>
@@ -179,7 +150,7 @@ and the MSE of the fitted model.</p></section>
return d</code></pre>
</details>
</dd>
-<dt id="TDA_Regression.datafetch"><code class="name flex">
+<dt id="TDA_Prediction.datafetch"><code class="name flex">
<span>def <span class="ident">datafetch</span></span>(<span>file_name)</span>
</code></dt>
<dd>
@@ -197,7 +168,7 @@ and the MSE of the fitted model.</p></section>
return data</code></pre>
</details>
</dd>
-<dt id="TDA_Regression.dataload"><code class="name flex">
+<dt id="TDA_Prediction.dataload"><code class="name flex">
<span>def <span class="ident">dataload</span></span>(<span>)</span>
</code></dt>
<dd>
@@ -214,7 +185,7 @@ and the MSE of the fitted model.</p></section>
return data</code></pre>
</details>
</dd>
-<dt id="TDA_Regression.descriptive_statistic"><code class="name flex">
+<dt id="TDA_Prediction.descriptive_statistic"><code class="name flex">
<span>def <span class="ident">descriptive_statistic</span></span>(<span>df)</span>
</code></dt>
<dd>
@@ -236,7 +207,7 @@ Takes dataframe as input.</p></section>
print("Describe : ", None)</code></pre>
</details>
</dd>
-<dt id="TDA_Regression.model_selection"><code class="name flex">
+<dt id="TDA_Prediction.model_selection"><code class="name flex">
<span>def <span class="ident">model_selection</span></span>(<span>df)</span>
</code></dt>
<dd>
@@ -270,12 +241,12 @@ regression. Returns best model for both methods.</p></section>
<ul id="index">
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
-<li><code><a title="TDA_Regression.MSE_fit" href="#TDA_Regression.MSE_fit">MSE_fit</a></code></li>
-<li><code><a title="TDA_Regression.accuracy_metrics" href="#TDA_Regression.accuracy_metrics">accuracy_metrics</a></code></li>
-<li><code><a title="TDA_Regression.datafetch" href="#TDA_Regression.datafetch">datafetch</a></code></li>
-<li><code><a title="TDA_Regression.dataload" href="#TDA_Regression.dataload">dataload</a></code></li>
-<li><code><a title="TDA_Regression.descriptive_statistic" href="#TDA_Regression.descriptive_statistic">descriptive_statistic</a></code></li>
-<li><code><a title="TDA_Regression.model_selection" href="#TDA_Regression.model_selection">model_selection</a></code></li>
+<li><code><a title="TDA_Prediction.MSE_fit" href="#TDA_Prediction.MSE_fit">MSE_fit</a></code></li>
+<li><code><a title="TDA_Prediction.accuracy_metrics" href="#TDA_Prediction.accuracy_metrics">accuracy_metrics</a></code></li>
+<li><code><a title="TDA_Prediction.datafetch" href="#TDA_Prediction.datafetch">datafetch</a></code></li>
+<li><code><a title="TDA_Prediction.dataload" href="#TDA_Prediction.dataload">dataload</a></code></li>
+<li><code><a title="TDA_Prediction.descriptive_statistic" href="#TDA_Prediction.descriptive_statistic">descriptive_statistic</a></code></li>
+<li><code><a title="TDA_Prediction.model_selection" href="#TDA_Prediction.model_selection">model_selection</a></code></li>
</ul>
</li>
</ul>