diff --git a/assets/jupyter/Model_Evaluation_and_Selection.ipynb.html b/assets/jupyter/Model_Evaluation_and_Selection.ipynb.html
index 3a0e5fd..15115f4 100644
--- a/assets/jupyter/Model_Evaluation_and_Selection.ipynb.html
+++ b/assets/jupyter/Model_Evaluation_and_Selection.ipynb.html
@@ -1,4 +1,4 @@
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diff --git a/assets/jupyter/blog.ipynb.html b/assets/jupyter/blog.ipynb.html
index f81c19c..cf251af 100644
--- a/assets/jupyter/blog.ipynb.html
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diff --git a/blog/index.html b/blog/index.html
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--- a/blog/index.html
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-<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title> Blog | Rutvik Joshi Career Portfolio</title> <meta name="author" content="Rutvik N Joshi"/> <meta name="description" content="A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. 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&middot; &nbsp; <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2023/Machine-Learning-roadmap/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">Machine/Deep Learning</h3> <p class="card-text">Study resources for Machine Learning</p> <p class="post-meta"> 3 min read &nbsp; &middot; &nbsp; <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> </p> </div> </div> </div> </div> </a> </div> </div> </div> <hr> <ul class="post-list"> <li><h3> <a class="post-title" href="/blog/2023/SupervisedMachineLearning/">Supervised Machine Learning</a> </h3> <p>Study topics</p> <p class="post-meta"> 6 min read &nbsp; &middot; &nbsp; December 1, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/machine"> <i class="fas fa-hashtag fa-sm"></i> Machine</a> &nbsp; <a href="/blog/tag/learning"> <i class="fas fa-hashtag fa-sm"></i> Learning</a> &nbsp; <a href="/blog/tag/masters"> <i class="fas fa-hashtag fa-sm"></i> Masters</a> &nbsp; &nbsp; &middot; &nbsp; <a href="/blog/category/sample-posts"> <i class="fas fa-tag fa-sm"></i> sample-posts</a> &nbsp; </p></li> <li><h3> <a class="post-title" href="/blog/2023/Advance-Learning-Algorithm/">Advance Learning Algorithms</a> </h3> <p>Study topics</p> <p class="post-meta"> 2 min read &nbsp; &middot; &nbsp; October 31, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/machine"> <i class="fas fa-hashtag fa-sm"></i> Machine</a> &nbsp; <a href="/blog/tag/learning"> <i class="fas fa-hashtag fa-sm"></i> Learning</a> &nbsp; <a href="/blog/tag/masters"> <i class="fas fa-hashtag fa-sm"></i> Masters</a> &nbsp; &nbsp; &middot; &nbsp; <a href="/blog/category/sample-posts"> <i class="fas fa-tag fa-sm"></i> sample-posts</a> &nbsp; </p></li> <li><h3> <a class="post-title" href="/assets/pdf/FlexureModuleExample.pdf">Flexure Member Output</a> </h3> <p>Design Output generated using PyLaTeX</p> <p class="post-meta"> 1 min read &nbsp; &middot; &nbsp; October 1, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> </p></li> <li><h3> <a class="post-title" href="/blog/2023/NeuralNetworkEvaluation/">Neural Network Evaluation</a> </h3> <p>Study topics</p> <p class="post-meta"> 6 min read &nbsp; &middot; &nbsp; October 1, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/machine"> <i class="fas fa-hashtag fa-sm"></i> Machine</a> &nbsp; <a href="/blog/tag/learning"> <i class="fas fa-hashtag fa-sm"></i> Learning</a> &nbsp; <a href="/blog/tag/masters"> <i class="fas fa-hashtag fa-sm"></i> Masters</a> &nbsp; &nbsp; &middot; &nbsp; <a href="/blog/category/sample-posts"> <i class="fas fa-tag fa-sm"></i> sample-posts</a> &nbsp; </p></li> <li><h3> <a class="post-title" href="/blog/2023/GFG-list/">Lists</a> </h3> <p>Study resources for Data Structures & Algorithms</p> <p class="post-meta"> 4 min read &nbsp; &middot; &nbsp; October 1, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/data"> <i class="fas fa-hashtag fa-sm"></i> Data</a> &nbsp; <a href="/blog/tag/structures"> <i class="fas fa-hashtag fa-sm"></i> Structures</a> &nbsp; <a href="/blog/tag/"> <i class="fas fa-hashtag fa-sm"></i> &</a> &nbsp; <a href="/blog/tag/algorithms"> <i class="fas fa-hashtag fa-sm"></i> Algorithms</a> &nbsp; </p></li> </ul> <nav aria-label="Blog page naviation"> <ul class="pagination pagination-lg justify-content-center"> <li class="page-item disabled"> <a class="page-link" href="" tabindex="-1" aria-disabled="">Newer</a> </li><li class="page-item active"><a class="page-link" href="/blog/index.html" title="blog">1</a></li> <li class="page-item "><a class="page-link" href="/blog/page/2/index.html" title="blog - page 2">2</a></li> <li class="page-item "><a class="page-link" href="/blog/page/3/index.html" title="blog - page 3">3</a></li> <li class="page-item "> <a class="page-link" href="/blog/page/2/">Older</a> </li> </ul> </nav> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> &copy; 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+<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title> Blog | Rutvik Joshi Career Portfolio</title> <meta name="author" content="Rutvik N Joshi"/> <meta name="description" content="A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. 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&middot; &nbsp; <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2023/Machine-Learning-roadmap/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">Machine/Deep Learning</h3> <p class="card-text">Study resources for Machine Learning</p> <p class="post-meta"> 3 min read &nbsp; &middot; &nbsp; <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> </p> </div> </div> </div> </div> </a> </div> </div> </div> <hr> <ul class="post-list"> <li><h3> <a class="post-title" href="/blog/2023/SupervisedMachineLearning/">Supervised Machine Learning</a> </h3> <p>Study topics</p> <p class="post-meta"> 6 min read &nbsp; &middot; &nbsp; December 1, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; 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diff --git a/blog/page/2/index.html b/blog/page/2/index.html
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@@ -1 +1 @@
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diff --git a/feed.xml b/feed.xml
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@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.2">Jekyll</generator><link href="https://rutvikjoshi63.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://rutvikjoshi63.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2023-12-15T14:50:22+00:00</updated><id>https://rutvikjoshi63.github.io/feed.xml</id><title type="html">Rutvik Joshi Career Portfolio</title><subtitle>A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. Latest blog for information </subtitle><entry><title type="html">Supervised Machine Learning</title><link href="https://rutvikjoshi63.github.io/blog/2023/SupervisedMachineLearning/" rel="alternate" type="text/html" title="Supervised Machine Learning"/><published>2023-12-01T10:56:00+00:00</published><updated>2023-12-01T10:56:00+00:00</updated><id>https://rutvikjoshi63.github.io/blog/2023/SupervisedMachineLearning</id><content type="html" xml:base="https://rutvikjoshi63.github.io/blog/2023/SupervisedMachineLearning/"><![CDATA[<h2 id="topics-covered-in-supervised-machine-learning">Topics covered in Supervised Machine Learning</h2> <p>It is usually a good idea to perform feature scaling to help your model converge faster. This is especially true if your input features have widely different ranges of values. For that, you will use the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html"><code class="language-plaintext highlighter-rouge">StandardScaler</code></a> class from scikit-learn. This computes the z-score of your inputs. As a refresher, the z-score is given by the equation:</p> \[z = \frac{x - \mu}{\sigma}\] <p>where $\mu$ is the mean of the feature values and $\sigma$ is the standard deviation.</p> <h1 id="initialize-the-class">Initialize the class</h1> <p>scaler_linear = StandardScaler()</p> <h1 id="compute-the-mean-and-standard-deviation-of-the-training-set-then-transform-it">Compute the mean and standard deviation of the training set then transform it</h1> <p>X_train_scaled = scaler_linear.fit_transform(x_train)</p> <p>print(f”Computed mean of the training set: {scaler_linear.mean_.squeeze():.2f}”) print(f”Computed standard deviation of the training set: {scaler_linear.scale_.squeeze():.2f}”)</p> <h3 id="train-the-model">Train the model</h3> <p>Next, you will create and train a regression model. For this lab, you will use the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html">LinearRegression</a> class but take note that there are other <a href="https://scikit-learn.org/stable/modules/classes.html#classical-linear-regressors">linear regressors</a> which you can also use.</p> <h1 id="initialize-the-class-1">Initialize the class</h1> <p>linear_model = LinearRegression()</p> <h1 id="train-the-model-1">Train the model</h1> <p>linear_model.fit(X_train_scaled, y_train )</p> <h3 id="evaluate-the-model">Evaluate the Model</h3> <p>To evaluate the performance of your model, you will measure the error for the training and cross validation sets. For the training error, recall the equation for calculating the mean squared error (MSE):</p> \[J_{train}(\vec{w}, b) = \frac{1}{2m_{train}}\left[\sum_{i=1}^{m_{train}}(f_{\vec{w},b}(\vec{x}_{train}^{(i)}) - y_{train}^{(i)})^2\right]\] <p>Scikit-learn also has a built-in <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"><code class="language-plaintext highlighter-rouge">mean_squared_error()</code></a> function that you can use. Take note though that <a href="https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-error">as per the documentation</a>, scikit-learn’s implementation only divides by <code class="language-plaintext highlighter-rouge">m</code> and not <code class="language-plaintext highlighter-rouge">2*m</code>, where <code class="language-plaintext highlighter-rouge">m</code> is the number of examples.</p> <h1 id="feed-the-scaled-training-set-and-get-the-predictions">Feed the scaled training set and get the predictions</h1> <p>yhat = linear_model.predict(X_train_scaled)</p> <h1 id="use-scikit-learns-utility-function-and-divide-by-2">Use scikit-learn’s utility function and divide by 2</h1> <p>print(f”training MSE (using sklearn function): {mean_squared_error(y_train, yhat) / 2}”)</p> <p>You can then compute the MSE for the cross validation set with basically the same equation.</p> <ul> <li>Say that your training set has an input feature equal to <code class="language-plaintext highlighter-rouge">500</code> which is scaled down to <code class="language-plaintext highlighter-rouge">0.5</code> using the z-score.</li> <li>After training, your model is able to accurately map this scaled input <code class="language-plaintext highlighter-rouge">x=0.5</code> to the target output <code class="language-plaintext highlighter-rouge">y=300</code>.</li> <li>Now let’s say that you deployed this model and one of your users fed it a sample equal to <code class="language-plaintext highlighter-rouge">500</code>.</li> <li>If you get this input sample’s z-score using any other values of the mean and standard deviation, then it might not be scaled to <code class="language-plaintext highlighter-rouge">0.5</code> and your model will most likely make a wrong prediction (i.e. not equal to <code class="language-plaintext highlighter-rouge">y=300</code>).</li> </ul> <p>You will scale the cross validation set below by using the same <code class="language-plaintext highlighter-rouge">StandardScaler</code> you used earlier but only calling its <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler.transform"><code class="language-plaintext highlighter-rouge">transform()</code></a> method instead of <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler.fit_transform"><code class="language-plaintext highlighter-rouge">fit_transform()</code></a>.</p> <h1 id="scale-the-cross-validation-set-using-the-mean-and-standard-deviation-of-the-training-set">Scale the cross validation set using the mean and standard deviation of the training set</h1> <p>X_cv_scaled = scaler_linear.transform(x_cv)</p> <p>print(f”Mean used to scale the CV set: {scaler_linear.mean_.squeeze():.2f}”) print(f”Standard deviation used to scale the CV set: {scaler_linear.scale_.squeeze():.2f}”)</p> <h1 id="feed-the-scaled-cross-validation-set">Feed the scaled cross validation set</h1> <p>yhat = linear_model.predict(X_cv_scaled)</p> <h1 id="use-scikit-learns-utility-function-and-divide-by-2-1">Use scikit-learn’s utility function and divide by 2</h1> <p>print(f”Cross validation MSE: {mean_squared_error(y_cv, yhat) / 2}”)</p> <h2 id="adding-polynomial-features">Adding Polynomial Features</h2> <h3 id="create-the-additional-features">Create the additional features</h3> <p>First, you will generate the polynomial features from your training set. The code below demonstrates how to do this using the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html"><code class="language-plaintext highlighter-rouge">PolynomialFeatures</code></a> class. It will create a new input feature which has the squared values of the input <code class="language-plaintext highlighter-rouge">x</code> (i.e. degree=2).</p> <h1 id="instantiate-the-class-to-make-polynomial-features">Instantiate the class to make polynomial features</h1> <p>poly = PolynomialFeatures(degree=2, include_bias=False)</p> <h1 id="compute-the-number-of-features-and-transform-the-training-set">Compute the number of features and transform the training set</h1> <p>X_train_mapped = poly.fit_transform(x_train)</p> <h1 id="preview-the-first-5-elements-of-the-new-training-set-left-column-is-x-and-right-column-is-x2">Preview the first 5 elements of the new training set. Left column is <code class="language-plaintext highlighter-rouge">x</code> and right column is <code class="language-plaintext highlighter-rouge">x^2</code></h1> <h1 id="note-the-enumber-in-the-output-denotes-how-many-places-the-decimal-point-should">Note: The <code class="language-plaintext highlighter-rouge">e+&lt;number&gt;</code> in the output denotes how many places the decimal point should</h1> <h1 id="be-moved-for-example-324e03-is-equal-to-3240">be moved. For example, <code class="language-plaintext highlighter-rouge">3.24e+03</code> is equal to <code class="language-plaintext highlighter-rouge">3240</code></h1> <p>print(X_train_mapped[:5]) You will then scale the inputs as before to narrow down the range of values.</p> <h1 id="instantiate-the-class">Instantiate the class</h1> <p>scaler_poly = StandardScaler()</p> <h1 id="compute-the-mean-and-standard-deviation-of-the-training-set-then-transform-it-1">Compute the mean and standard deviation of the training set then transform it</h1> <p>X_train_mapped_scaled = scaler_poly.fit_transform(X_train_mapped)</p> <h1 id="preview-the-first-5-elements-of-the-scaled-training-set">Preview the first 5 elements of the scaled training set.</h1> <p>print(X_train_mapped_scaled[:5]) You can then proceed to train the model. After that, you will measure the model’s performance against the cross validation set. Like before, you should make sure to perform the same transformations as you did in the training set. You will add the same number of polynomial features then scale the range of values.</p> <h1 id="initialize-the-class-2">Initialize the class</h1> <p>model = LinearRegression()</p> <h1 id="train-the-model-2">Train the model</h1> <p>model.fit(X_train_mapped_scaled, y_train )</p> <h1 id="compute-the-training-mse">Compute the training MSE</h1> <p>yhat = model.predict(X_train_mapped_scaled) print(f”Training MSE: {mean_squared_error(y_train, yhat) / 2}”)</p> <h1 id="add-the-polynomial-features-to-the-cross-validation-set">Add the polynomial features to the cross validation set</h1> <p>X_cv_mapped = poly.transform(x_cv)</p> <h1 id="scale-the-cross-validation-set-using-the-mean-and-standard-deviation-of-the-training-set-1">Scale the cross validation set using the mean and standard deviation of the training set</h1> <p>X_cv_mapped_scaled = scaler_poly.transform(X_cv_mapped)</p> <h1 id="compute-the-cross-validation-mse">Compute the cross validation MSE</h1> <p>yhat = model.predict(X_cv_mapped_scaled) print(f”Cross validation MSE: {mean_squared_error(y_cv, yhat) / 2}”)</p> <p><strong>You can create a loop that contains all the steps in the previous code cells. Here is one implementation that adds polynomial features up to degree=10.</strong></p> <h1 id="initialize-lists-containing-the-lists-models-and-scalers">Initialize lists containing the lists, models, and scalers</h1> <p>train_mses = [] cv_mses = [] models = [] scalers = []</p> <h1 id="loop-over-10-times-each-adding-one-more-degree-of-polynomial-higher-than-the-last">Loop over 10 times. Each adding one more degree of polynomial higher than the last.</h1> <p>for degree in range(1,11):</p> <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code># Add polynomial features to the training set
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.2">Jekyll</generator><link href="https://rutvikjoshi63.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://rutvikjoshi63.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2023-12-15T17:49:48+00:00</updated><id>https://rutvikjoshi63.github.io/feed.xml</id><title type="html">Rutvik Joshi Career Portfolio</title><subtitle>A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. Latest blog for information </subtitle><entry><title type="html">Supervised Machine Learning</title><link href="https://rutvikjoshi63.github.io/blog/2023/SupervisedMachineLearning/" rel="alternate" type="text/html" title="Supervised Machine Learning"/><published>2023-12-01T10:56:00+00:00</published><updated>2023-12-01T10:56:00+00:00</updated><id>https://rutvikjoshi63.github.io/blog/2023/SupervisedMachineLearning</id><content type="html" xml:base="https://rutvikjoshi63.github.io/blog/2023/SupervisedMachineLearning/"><![CDATA[<h2 id="topics-covered-in-supervised-machine-learning">Topics covered in Supervised Machine Learning</h2> <p>It is usually a good idea to perform feature scaling to help your model converge faster. This is especially true if your input features have widely different ranges of values. For that, you will use the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html"><code class="language-plaintext highlighter-rouge">StandardScaler</code></a> class from scikit-learn. This computes the z-score of your inputs. As a refresher, the z-score is given by the equation:</p> \[z = \frac{x - \mu}{\sigma}\] <p>where $\mu$ is the mean of the feature values and $\sigma$ is the standard deviation.</p> <h1 id="initialize-the-class">Initialize the class</h1> <p>scaler_linear = StandardScaler()</p> <h1 id="compute-the-mean-and-standard-deviation-of-the-training-set-then-transform-it">Compute the mean and standard deviation of the training set then transform it</h1> <p>X_train_scaled = scaler_linear.fit_transform(x_train)</p> <p>print(f”Computed mean of the training set: {scaler_linear.mean_.squeeze():.2f}”) print(f”Computed standard deviation of the training set: {scaler_linear.scale_.squeeze():.2f}”)</p> <h3 id="train-the-model">Train the model</h3> <p>Next, you will create and train a regression model. For this lab, you will use the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html">LinearRegression</a> class but take note that there are other <a href="https://scikit-learn.org/stable/modules/classes.html#classical-linear-regressors">linear regressors</a> which you can also use.</p> <h1 id="initialize-the-class-1">Initialize the class</h1> <p>linear_model = LinearRegression()</p> <h1 id="train-the-model-1">Train the model</h1> <p>linear_model.fit(X_train_scaled, y_train )</p> <h3 id="evaluate-the-model">Evaluate the Model</h3> <p>To evaluate the performance of your model, you will measure the error for the training and cross validation sets. For the training error, recall the equation for calculating the mean squared error (MSE):</p> \[J_{train}(\vec{w}, b) = \frac{1}{2m_{train}}\left[\sum_{i=1}^{m_{train}}(f_{\vec{w},b}(\vec{x}_{train}^{(i)}) - y_{train}^{(i)})^2\right]\] <p>Scikit-learn also has a built-in <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"><code class="language-plaintext highlighter-rouge">mean_squared_error()</code></a> function that you can use. Take note though that <a href="https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-error">as per the documentation</a>, scikit-learn’s implementation only divides by <code class="language-plaintext highlighter-rouge">m</code> and not <code class="language-plaintext highlighter-rouge">2*m</code>, where <code class="language-plaintext highlighter-rouge">m</code> is the number of examples.</p> <h1 id="feed-the-scaled-training-set-and-get-the-predictions">Feed the scaled training set and get the predictions</h1> <p>yhat = linear_model.predict(X_train_scaled)</p> <h1 id="use-scikit-learns-utility-function-and-divide-by-2">Use scikit-learn’s utility function and divide by 2</h1> <p>print(f”training MSE (using sklearn function): {mean_squared_error(y_train, yhat) / 2}”)</p> <p>You can then compute the MSE for the cross validation set with basically the same equation.</p> <ul> <li>Say that your training set has an input feature equal to <code class="language-plaintext highlighter-rouge">500</code> which is scaled down to <code class="language-plaintext highlighter-rouge">0.5</code> using the z-score.</li> <li>After training, your model is able to accurately map this scaled input <code class="language-plaintext highlighter-rouge">x=0.5</code> to the target output <code class="language-plaintext highlighter-rouge">y=300</code>.</li> <li>Now let’s say that you deployed this model and one of your users fed it a sample equal to <code class="language-plaintext highlighter-rouge">500</code>.</li> <li>If you get this input sample’s z-score using any other values of the mean and standard deviation, then it might not be scaled to <code class="language-plaintext highlighter-rouge">0.5</code> and your model will most likely make a wrong prediction (i.e. not equal to <code class="language-plaintext highlighter-rouge">y=300</code>).</li> </ul> <p>You will scale the cross validation set below by using the same <code class="language-plaintext highlighter-rouge">StandardScaler</code> you used earlier but only calling its <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler.transform"><code class="language-plaintext highlighter-rouge">transform()</code></a> method instead of <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler.fit_transform"><code class="language-plaintext highlighter-rouge">fit_transform()</code></a>.</p> <h1 id="scale-the-cross-validation-set-using-the-mean-and-standard-deviation-of-the-training-set">Scale the cross validation set using the mean and standard deviation of the training set</h1> <p>X_cv_scaled = scaler_linear.transform(x_cv)</p> <p>print(f”Mean used to scale the CV set: {scaler_linear.mean_.squeeze():.2f}”) print(f”Standard deviation used to scale the CV set: {scaler_linear.scale_.squeeze():.2f}”)</p> <h1 id="feed-the-scaled-cross-validation-set">Feed the scaled cross validation set</h1> <p>yhat = linear_model.predict(X_cv_scaled)</p> <h1 id="use-scikit-learns-utility-function-and-divide-by-2-1">Use scikit-learn’s utility function and divide by 2</h1> <p>print(f”Cross validation MSE: {mean_squared_error(y_cv, yhat) / 2}”)</p> <h2 id="adding-polynomial-features">Adding Polynomial Features</h2> <h3 id="create-the-additional-features">Create the additional features</h3> <p>First, you will generate the polynomial features from your training set. The code below demonstrates how to do this using the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html"><code class="language-plaintext highlighter-rouge">PolynomialFeatures</code></a> class. It will create a new input feature which has the squared values of the input <code class="language-plaintext highlighter-rouge">x</code> (i.e. degree=2).</p> <h1 id="instantiate-the-class-to-make-polynomial-features">Instantiate the class to make polynomial features</h1> <p>poly = PolynomialFeatures(degree=2, include_bias=False)</p> <h1 id="compute-the-number-of-features-and-transform-the-training-set">Compute the number of features and transform the training set</h1> <p>X_train_mapped = poly.fit_transform(x_train)</p> <h1 id="preview-the-first-5-elements-of-the-new-training-set-left-column-is-x-and-right-column-is-x2">Preview the first 5 elements of the new training set. Left column is <code class="language-plaintext highlighter-rouge">x</code> and right column is <code class="language-plaintext highlighter-rouge">x^2</code></h1> <h1 id="note-the-enumber-in-the-output-denotes-how-many-places-the-decimal-point-should">Note: The <code class="language-plaintext highlighter-rouge">e+&lt;number&gt;</code> in the output denotes how many places the decimal point should</h1> <h1 id="be-moved-for-example-324e03-is-equal-to-3240">be moved. For example, <code class="language-plaintext highlighter-rouge">3.24e+03</code> is equal to <code class="language-plaintext highlighter-rouge">3240</code></h1> <p>print(X_train_mapped[:5]) You will then scale the inputs as before to narrow down the range of values.</p> <h1 id="instantiate-the-class">Instantiate the class</h1> <p>scaler_poly = StandardScaler()</p> <h1 id="compute-the-mean-and-standard-deviation-of-the-training-set-then-transform-it-1">Compute the mean and standard deviation of the training set then transform it</h1> <p>X_train_mapped_scaled = scaler_poly.fit_transform(X_train_mapped)</p> <h1 id="preview-the-first-5-elements-of-the-scaled-training-set">Preview the first 5 elements of the scaled training set.</h1> <p>print(X_train_mapped_scaled[:5]) You can then proceed to train the model. After that, you will measure the model’s performance against the cross validation set. Like before, you should make sure to perform the same transformations as you did in the training set. You will add the same number of polynomial features then scale the range of values.</p> <h1 id="initialize-the-class-2">Initialize the class</h1> <p>model = LinearRegression()</p> <h1 id="train-the-model-2">Train the model</h1> <p>model.fit(X_train_mapped_scaled, y_train )</p> <h1 id="compute-the-training-mse">Compute the training MSE</h1> <p>yhat = model.predict(X_train_mapped_scaled) print(f”Training MSE: {mean_squared_error(y_train, yhat) / 2}”)</p> <h1 id="add-the-polynomial-features-to-the-cross-validation-set">Add the polynomial features to the cross validation set</h1> <p>X_cv_mapped = poly.transform(x_cv)</p> <h1 id="scale-the-cross-validation-set-using-the-mean-and-standard-deviation-of-the-training-set-1">Scale the cross validation set using the mean and standard deviation of the training set</h1> <p>X_cv_mapped_scaled = scaler_poly.transform(X_cv_mapped)</p> <h1 id="compute-the-cross-validation-mse">Compute the cross validation MSE</h1> <p>yhat = model.predict(X_cv_mapped_scaled) print(f”Cross validation MSE: {mean_squared_error(y_cv, yhat) / 2}”)</p> <p><strong>You can create a loop that contains all the steps in the previous code cells. Here is one implementation that adds polynomial features up to degree=10.</strong></p> <h1 id="initialize-lists-containing-the-lists-models-and-scalers">Initialize lists containing the lists, models, and scalers</h1> <p>train_mses = [] cv_mses = [] models = [] scalers = []</p> <h1 id="loop-over-10-times-each-adding-one-more-degree-of-polynomial-higher-than-the-last">Loop over 10 times. Each adding one more degree of polynomial higher than the last.</h1> <p>for degree in range(1,11):</p> <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code># Add polynomial features to the training set
 poly = PolynomialFeatures(degree, include_bias=False)
 X_train_mapped = poly.fit_transform(x_train)
 
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-<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>Compression member | Rutvik Joshi Career Portfolio</title> <meta name="author" content="Rutvik N Joshi"> <meta name="description" content="A Design module in Osdag software application for Steel Design"> <meta name="keywords" content="Artificial Intelligence, Machine Learning"> <link href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/css/mdb.min.css" integrity="sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw=" crossorigin="anonymous"> <link defer rel="stylesheet" href="https://unpkg.com/bootstrap-table@1.21.4/dist/bootstrap-table.min.css"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.4.0/css/all.min.css" integrity="sha256-HtsXJanqjKTc8vVQjO4YMhiqFoXkfBsjBWcX91T1jr8=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/academicons@1.9.1/css/academicons.min.css" integrity="sha256-i1+4qU2G2860dGGIOJscdC30s9beBXjFfzjWLjBRsBg=" crossorigin="anonymous"> <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700|Roboto+Slab:100,300,400,500,700|Material+Icons"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jwarby/jekyll-pygments-themes@master/github.css" media="" id="highlight_theme_light"> <link rel="shortcut icon" href="data:image/svg+xml,&lt;svg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20100%20100%22&gt;&lt;text%20y=%22.9em%22%20font-size=%2290%22&gt;%E2%9A%9B%EF%B8%8F&lt;/text&gt;&lt;/svg&gt;"> <link rel="stylesheet" href="/assets/css/main.css"> <link rel="canonical" href="https://rutvikjoshi63.github.io/projects/1_project/"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jwarby/jekyll-pygments-themes@master/native.css" media="none" id="highlight_theme_dark"> <script src="/assets/js/theme.js"></script> <script src="/assets/js/dark_mode.js"></script> </head> <body class="fixed-top-nav "> <header> <nav id="navbar" class="navbar navbar-light navbar-expand-sm fixed-top"> <div class="container"> <a class="navbar-brand title font-weight-lighter" href="/">Rutvik Joshi Career Portfolio</a> <button class="navbar-toggler collapsed ml-auto" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar top-bar"></span> <span class="icon-bar middle-bar"></span> <span class="icon-bar bottom-bar"></span> </button> <div class="collapse navbar-collapse text-right" id="navbarNav"> <ul class="navbar-nav ml-auto flex-nowrap"> <li class="nav-item "> <a class="nav-link" href="/">Rutvik Joshi</a> </li> <li class="nav-item "> <a class="nav-link" href="/blog/">Blog</a> </li> <li class="nav-item "> <a class="nav-link" href="/projects/">Projects</a> </li> <li class="nav-item "> <a class="nav-link" href="/repositories/">Repositories</a> </li> <li class="nav-item "> <a class="nav-link" href="/cv/">Resume</a> </li> <li class="toggle-container"> <button id="light-toggle" title="Change theme"> <i class="fas fa-moon"></i> <i class="fas fa-sun"></i> </button> </li> </ul> </div> </div> </nav> <progress id="progress" value="0"> <div class="progress-container"> <span class="progress-bar"></span> </div> </progress> </header> <div class="container mt-5"> <div class="post"> <header class="post-header"> <h1 class="post-title">Compression member</h1> <p class="post-description">A Design module in Osdag software application for Steel Design</p> </header> <article> <p>The <a href="https://fossee.in/" rel="external nofollow noopener" target="_blank">FOSSEE</a> project is part of the National Mission on Education through Information and Communication Technology (ICT), Ministry of Education (MoE), Government of India.<br> <a href="https://osdag.fossee.in/" rel="external nofollow noopener" target="_blank">Osdag</a> is a cross-platform free/libre and open-source software for the design (and detailing) of steel structures, following the Indian Standard IS 800:2007.</p> <p>Below I have added the images of the module - <code class="language-plaintext highlighter-rouge">Compression member</code> and it’s sub-modules: <code class="language-plaintext highlighter-rouge">Columns in Frames</code> and <code class="language-plaintext highlighter-rouge">Struts in Trusses</code> </p> <div class="row"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagColumnlayout1.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagColumnlayout2.1.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagColumnlayout2.2.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> </div> <p>Images for the Columns in Frames sub-module. On the left, you can see the main page. Middle, is the Design Preference pop-up with the description of Column Section. Right, is another pop-up for the Design Preference with the description of Optimization tab. This is done so that user gets better control over the design based on their input from the input dock.</p> <div class="row"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagStrutlayout1.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagStrutlayout2.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagStrutlayout2.2.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> </div> <p>Images for the Struts in Trusses sub-module. On the left, you can see the main page. Middle, is the Design Preference pop-up with the description of Angle Section. Right, is another pop-up for the Design Preference with the description of Optimization tab. This is done so that user gets better control over the design based on their input from the input dock.</p> <p>The code is simple. Checkout <code class="language-plaintext highlighter-rouge">Osdag</code> and dowload it, don’t worry it’s free and open-source, checkout the Github pages(read more about the <a href="https://github.com/osdag-admin/Osdag" rel="external nofollow noopener" target="_blank"><code class="language-plaintext highlighter-rouge">Osdag - Github</code></a> system). The code for the above images is available at my <a href="https://github.com/rutvikjoshi63/Osdag/tree/Column-module-dev" rel="external nofollow noopener" target="_blank">Github page</a></p> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Rutvik N Joshi. Powered by <a href="https://jekyllrb.com/" target="_blank" rel="external nofollow noopener">Jekyll</a> with <a href="https://github.com/alshedivat/al-folio" rel="external nofollow noopener" target="_blank">al-folio</a> theme. Hosted by <a href="https://pages.github.com/" target="_blank" rel="external nofollow noopener">GitHub Pages</a>. </div> </footer> <script src="https://cdn.jsdelivr.net/npm/jquery@3.6.0/dist/jquery.min.js" integrity="sha256-/xUj+3OJU5yExlq6GSYGSHk7tPXikynS7ogEvDej/m4=" crossorigin="anonymous"></script> <script src="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/js/bootstrap.bundle.min.js" integrity="sha256-fgLAgv7fyCGopR/gBNq2iW3ZKIdqIcyshnUULC4vex8=" crossorigin="anonymous"></script> <script src="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/js/mdb.min.js" integrity="sha256-NdbiivsvWt7VYCt6hYNT3h/th9vSTL4EDWeGs5SN3DA=" crossorigin="anonymous"></script> <script defer src="https://cdn.jsdelivr.net/npm/masonry-layout@4.2.2/dist/masonry.pkgd.min.js" integrity="sha256-Nn1q/fx0H7SNLZMQ5Hw5JLaTRZp0yILA/FRexe19VdI=" crossorigin="anonymous"></script> <script defer src="https://cdn.jsdelivr.net/npm/imagesloaded@4/imagesloaded.pkgd.min.js"></script> <script defer src="/assets/js/masonry.js" type="text/javascript"></script> <script defer src="https://cdn.jsdelivr.net/npm/medium-zoom@1.0.8/dist/medium-zoom.min.js" integrity="sha256-7PhEpEWEW0XXQ0k6kQrPKwuoIomz8R8IYyuU1Qew4P8=" crossorigin="anonymous"></script> <script defer src="/assets/js/zoom.js"></script> <script defer src="https://unpkg.com/bootstrap-table@1.21.4/dist/bootstrap-table.min.js"></script> <script src="/assets/js/no_defer.js"></script> <script defer src="/assets/js/common.js"></script> <script defer src="/assets/js/copy_code.js" type="text/javascript"></script> <script async src="https://d1bxh8uas1mnw7.cloudfront.net/assets/embed.js"></script> <script async src="https://badge.dimensions.ai/badge.js"></script> <script type="text/javascript">window.MathJax={tex:{tags:"ams"}};</script> <script defer type="text/javascript" id="MathJax-script" src="https://cdn.jsdelivr.net/npm/mathjax@3.2.0/es5/tex-mml-chtml.js"></script> <script defer src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script> <script type="text/javascript">function progressBarSetup(){"max"in document.createElement("progress")?(initializeProgressElement(),$(document).on("scroll",function(){progressBar.attr({value:getCurrentScrollPosition()})}),$(window).on("resize",initializeProgressElement)):(resizeProgressBar(),$(document).on("scroll",resizeProgressBar),$(window).on("resize",resizeProgressBar))}function getCurrentScrollPosition(){return $(window).scrollTop()}function initializeProgressElement(){let e=$("#navbar").outerHeight(!0);$("body").css({"padding-top":e}),$("progress-container").css({"padding-top":e}),progressBar.css({top:e}),progressBar.attr({max:getDistanceToScroll(),value:getCurrentScrollPosition()})}function getDistanceToScroll(){return $(document).height()-$(window).height()}function resizeProgressBar(){progressBar.css({width:getWidthPercentage()+"%"})}function getWidthPercentage(){return getCurrentScrollPosition()/getDistanceToScroll()*100}const progressBar=$("#progress");window.onload=function(){setTimeout(progressBarSetup,50)};</script> </body> </html>
\ No newline at end of file
+<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>Compression member | Rutvik Joshi Career Portfolio</title> <meta name="author" content="Rutvik N Joshi"> <meta name="description" content="A Design module in Osdag software application for Steel Design"> <meta name="keywords" content="Artificial Intelligence, Machine Learning"> <link href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/css/mdb.min.css" integrity="sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw=" crossorigin="anonymous"> <link defer rel="stylesheet" href="https://unpkg.com/bootstrap-table@1.21.4/dist/bootstrap-table.min.css"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.4.0/css/all.min.css" integrity="sha256-HtsXJanqjKTc8vVQjO4YMhiqFoXkfBsjBWcX91T1jr8=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/academicons@1.9.1/css/academicons.min.css" integrity="sha256-i1+4qU2G2860dGGIOJscdC30s9beBXjFfzjWLjBRsBg=" crossorigin="anonymous"> <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700|Roboto+Slab:100,300,400,500,700|Material+Icons"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jwarby/jekyll-pygments-themes@master/github.css" media="" id="highlight_theme_light"> <link rel="shortcut icon" href="data:image/svg+xml,&lt;svg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20100%20100%22&gt;&lt;text%20y=%22.9em%22%20font-size=%2290%22&gt;%E2%9A%9B%EF%B8%8F&lt;/text&gt;&lt;/svg&gt;"> <link rel="stylesheet" href="/assets/css/main.css"> <link rel="canonical" href="https://rutvikjoshi63.github.io/projects/1_project/"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jwarby/jekyll-pygments-themes@master/native.css" media="none" id="highlight_theme_dark"> <script src="/assets/js/theme.js"></script> <script src="/assets/js/dark_mode.js"></script> </head> <body class="fixed-top-nav "> <header> <nav id="navbar" class="navbar navbar-light navbar-expand-sm fixed-top"> <div class="container"> <a class="navbar-brand title font-weight-lighter" href="/">Rutvik Joshi Career Portfolio</a> <button class="navbar-toggler collapsed ml-auto" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar top-bar"></span> <span class="icon-bar middle-bar"></span> <span class="icon-bar bottom-bar"></span> </button> <div class="collapse navbar-collapse text-right" id="navbarNav"> <ul class="navbar-nav ml-auto flex-nowrap"> <li class="nav-item "> <a class="nav-link" href="/">Rutvik Joshi</a> </li> <li class="nav-item "> <a class="nav-link" href="/blog/">Blog</a> </li> <li class="nav-item "> <a class="nav-link" href="/projects/">Projects</a> </li> <li class="nav-item "> <a class="nav-link" href="/repositories/">Repositories</a> </li> <li class="nav-item "> <a class="nav-link" href="/cv/">Resume</a> </li> <li class="toggle-container"> <button id="light-toggle" title="Change theme"> <i class="fas fa-moon"></i> <i class="fas fa-sun"></i> </button> </li> </ul> </div> </div> </nav> <progress id="progress" value="0"> <div class="progress-container"> <span class="progress-bar"></span> </div> </progress> </header> <div class="container mt-5"> <div class="post"> <header class="post-header"> <h1 class="post-title">Compression member</h1> <p class="post-description">A Design module in Osdag software application for Steel Design</p> </header> <article> <p>The <a href="https://fossee.in/" rel="external nofollow noopener" target="_blank">FOSSEE</a> project is part of the National Mission on Education through Information and Communication Technology (ICT), Ministry of Education (MoE), Government of India.<br> <a href="https://osdag.fossee.in/" rel="external nofollow noopener" target="_blank">Osdag</a> is a cross-platform free/libre and open-source software for the design (and detailing) of steel structures, following the Indian Standard IS 800:2007.</p> <p>Below I have added the images of the module - <code class="language-plaintext highlighter-rouge">Compression member</code> and it’s sub-modules: <code class="language-plaintext highlighter-rouge">Columns in Frames</code> and <code class="language-plaintext highlighter-rouge">Struts in Trusses</code></p> <div class="row"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagColumnlayout1.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagColumnlayout2.1.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagColumnlayout2.2.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> </div> <p>Images for the Columns in Frames sub-module. On the left, you can see the main page. Middle, is the Design Preference pop-up with the description of Column Section. Right, is another pop-up for the Design Preference with the description of Optimization tab. This is done so that user gets better control over the design based on their input from the input dock.</p> <div class="row"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagStrutlayout1.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagStrutlayout2.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/IIT/OsdagStrutlayout2.2.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" title="example image" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> </div> <p>Images for the Struts in Trusses sub-module. On the left, you can see the main page. Middle, is the Design Preference pop-up with the description of Angle Section. Right, is another pop-up for the Design Preference with the description of Optimization tab. This is done so that user gets better control over the design based on their input from the input dock.</p> <p>The code is simple. Checkout <code class="language-plaintext highlighter-rouge">Osdag</code> and dowload it, don’t worry it’s free and open-source, checkout the Github pages(read more about the <a href="https://github.com/osdag-admin/Osdag" rel="external nofollow noopener" target="_blank"><code class="language-plaintext highlighter-rouge">Osdag - Github</code></a> system). The code for the above images is available at my <a href="https://github.com/rutvikjoshi63/Osdag/tree/Column-module-dev" rel="external nofollow noopener" target="_blank">Github page</a></p> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Rutvik N Joshi. Powered by <a href="https://jekyllrb.com/" target="_blank" rel="external nofollow noopener">Jekyll</a> with <a href="https://github.com/alshedivat/al-folio" rel="external nofollow noopener" target="_blank">al-folio</a> theme. Hosted by <a href="https://pages.github.com/" target="_blank" rel="external nofollow noopener">GitHub Pages</a>. </div> </footer> <script src="https://cdn.jsdelivr.net/npm/jquery@3.6.0/dist/jquery.min.js" integrity="sha256-/xUj+3OJU5yExlq6GSYGSHk7tPXikynS7ogEvDej/m4=" crossorigin="anonymous"></script> <script src="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/js/bootstrap.bundle.min.js" integrity="sha256-fgLAgv7fyCGopR/gBNq2iW3ZKIdqIcyshnUULC4vex8=" crossorigin="anonymous"></script> <script src="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/js/mdb.min.js" integrity="sha256-NdbiivsvWt7VYCt6hYNT3h/th9vSTL4EDWeGs5SN3DA=" crossorigin="anonymous"></script> <script defer src="https://cdn.jsdelivr.net/npm/masonry-layout@4.2.2/dist/masonry.pkgd.min.js" integrity="sha256-Nn1q/fx0H7SNLZMQ5Hw5JLaTRZp0yILA/FRexe19VdI=" crossorigin="anonymous"></script> <script defer src="https://cdn.jsdelivr.net/npm/imagesloaded@4/imagesloaded.pkgd.min.js"></script> <script defer src="/assets/js/masonry.js" type="text/javascript"></script> <script defer src="https://cdn.jsdelivr.net/npm/medium-zoom@1.0.8/dist/medium-zoom.min.js" integrity="sha256-7PhEpEWEW0XXQ0k6kQrPKwuoIomz8R8IYyuU1Qew4P8=" crossorigin="anonymous"></script> <script defer src="/assets/js/zoom.js"></script> <script defer src="https://unpkg.com/bootstrap-table@1.21.4/dist/bootstrap-table.min.js"></script> <script src="/assets/js/no_defer.js"></script> <script defer src="/assets/js/common.js"></script> <script defer src="/assets/js/copy_code.js" type="text/javascript"></script> <script async src="https://d1bxh8uas1mnw7.cloudfront.net/assets/embed.js"></script> <script async src="https://badge.dimensions.ai/badge.js"></script> <script type="text/javascript">window.MathJax={tex:{tags:"ams"}};</script> <script defer type="text/javascript" id="MathJax-script" src="https://cdn.jsdelivr.net/npm/mathjax@3.2.0/es5/tex-mml-chtml.js"></script> <script defer src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script> <script type="text/javascript">function progressBarSetup(){"max"in document.createElement("progress")?(initializeProgressElement(),$(document).on("scroll",function(){progressBar.attr({value:getCurrentScrollPosition()})}),$(window).on("resize",initializeProgressElement)):(resizeProgressBar(),$(document).on("scroll",resizeProgressBar),$(window).on("resize",resizeProgressBar))}function getCurrentScrollPosition(){return $(window).scrollTop()}function initializeProgressElement(){let e=$("#navbar").outerHeight(!0);$("body").css({"padding-top":e}),$("progress-container").css({"padding-top":e}),progressBar.css({top:e}),progressBar.attr({max:getDistanceToScroll(),value:getCurrentScrollPosition()})}function getDistanceToScroll(){return $(document).height()-$(window).height()}function resizeProgressBar(){progressBar.css({width:getWidthPercentage()+"%"})}function getWidthPercentage(){return getCurrentScrollPosition()/getDistanceToScroll()*100}const progressBar=$("#progress");window.onload=function(){setTimeout(progressBarSetup,50)};</script> </body> </html>
\ No newline at end of file
diff --git a/projects/3_project.md b/projects/3_project.md
deleted file mode 100644
index 0a06c26..0000000
--- a/projects/3_project.md
+++ /dev/null
@@ -1,81 +0,0 @@
-<!-- ---
-layout: page
-title: project 3
-description: a project that redirects to another website
-img: assets/img/7.jpg
-redirect: https://unsplash.com
-importance: 3
-category: work
----
-
-Every project has a beautiful feature showcase page.
-It's easy to include images in a flexible 3-column grid format.
-Make your photos 1/3, 2/3, or full width.
-
-To give your project a background in the portfolio page, just add the img tag to the front matter like so:
-
-    ---
-    layout: page
-    title: project
-    description: a project with a background image
-    img: /assets/img/12.jpg
-    ---
-
-<div class="row">
-    <div class="col-sm mt-3 mt-md-0">
-        {% include figure.html path="assets/img/1.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-    <div class="col-sm mt-3 mt-md-0">
-        {% include figure.html path="assets/img/3.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-    <div class="col-sm mt-3 mt-md-0">
-        {% include figure.html path="assets/img/5.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-</div>
-<div class="caption">
-    Caption photos easily. On the left, a road goes through a tunnel. Middle, leaves artistically fall in a hipster photoshoot. Right, in another hipster photoshoot, a lumberjack grasps a handful of pine needles.
-</div>
-<div class="row">
-    <div class="col-sm mt-3 mt-md-0">
-        {% include figure.html path="assets/img/5.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-</div>
-<div class="caption">
-    This image can also have a caption. It's like magic.
-</div>
-
-You can also put regular text between your rows of images.
-Say you wanted to write a little bit about your project before you posted the rest of the images.
-You describe how you toiled, sweated, *bled* for your project, and then... you reveal its glory in the next row of images.
-
-
-<div class="row justify-content-sm-center">
-    <div class="col-sm-8 mt-3 mt-md-0">
-        {% include figure.html path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-    <div class="col-sm-4 mt-3 mt-md-0">
-        {% include figure.html path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-</div>
-<div class="caption">
-    You can also have artistically styled 2/3 + 1/3 images, like these.
-</div>
-
-
-The code is simple.
-Just wrap your images with `<div class="col-sm">` and place them inside `<div class="row">` (read more about the <a href="https://getbootstrap.com/docs/4.4/layout/grid/">Bootstrap Grid</a> system).
-To make images responsive, add `img-fluid` class to each; for rounded corners and shadows use `rounded` and `z-depth-1` classes.
-Here's the code for the last row of images above:
-
-{% raw %}
-```html
-<div class="row justify-content-sm-center">
-    <div class="col-sm-8 mt-3 mt-md-0">
-        {% include figure.html path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-    <div class="col-sm-4 mt-3 mt-md-0">
-        {% include figure.html path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-</div>
-```
-{% endraw %} -->
diff --git a/sitemap.xml b/sitemap.xml
index 85f24c4..d1b1e38 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -1 +1 @@
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