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<section id="predict-bike-sharing-demand-with-autogluon-template" class="cell markdown" id="MKqZqhSPbJmZ">
<h1>Predict Bike Sharing Demand with AutoGluon Template</h1>
</section>
<section id="project-predict-bike-sharing-demand-with-autogluon" class="cell markdown" id="WRrDCZIsbJme">
<h2>Project: Predict Bike Sharing Demand with AutoGluon</h2>
<p>This notebook is a template with each step that you need to complete for the project.</p>
<p>Please fill in your code where there are explicit <code>?</code> markers in the notebook. You are welcome to add more cells and code as you see fit.</p>
<p>Once you have completed all the code implementations, please export your notebook as a HTML file so the reviews can view your code. Make sure you have all outputs correctly outputted.</p>
<p><code>File-> Export Notebook As... -> Export Notebook as HTML</code></p>
<p>There is a writeup to complete as well after all code implememtation is done. Please answer all questions and attach the necessary tables and charts. You can complete the writeup in either markdown or PDF.</p>
<p>Completing the code template and writeup template will cover all of the rubric points for this project.</p>
<p>The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this notebook and also discuss the results in the writeup file.</p>
</section>
<section id="step-1-create-an-account-with-kaggle" class="cell markdown" id="tS0N3buDbJmf">
<h2>Step 1: Create an account with Kaggle</h2>
</section>
<section id="create-kaggle-account-and-download-api-key" class="cell markdown" id="tBy2NeErbJmg">
<h3>Create Kaggle Account and download API key</h3>
<p>Below is example of steps to get the API username and key. Each student will have their own username and key.</p>
</section>
<div class="cell markdown" id="FjMId0eIbJmg">
<ol>
<li>Open account settings. <img src="kaggle1.png" alt="kaggle1.png" /> <img src="kaggle2.png" alt="kaggle2.png" /></li>
<li>Scroll down to API and click Create New API Token. <img src="kaggle3.png" alt="kaggle3.png" /> <img src="kaggle4.png" alt="kaggle4.png" /></li>
<li>Open up <code>kaggle.json</code> and use the username and key. <img src="kaggle5.png" alt="kaggle5.png" /></li>
</ol>
</div>
<section id="step-2-download-the-kaggle-dataset-using-the-kaggle-python-library" class="cell markdown" id="EzG036BFbJmh">
<h2>Step 2: Download the Kaggle dataset using the kaggle python library</h2>
</section>
<section id="open-up-sagemaker-studio-and-use-starter-template" class="cell markdown" id="Y6hQVoccbJmh">
<h3>Open up Sagemaker Studio and use starter template</h3>
</section>
<div class="cell markdown" id="m804_FGLbJmh">
<ol>
<li>Notebook should be using a <code>ml.t3.medium</code> instance (2 vCPU + 4 GiB)</li>
<li>Notebook should be using kernal: <code>Python 3 (MXNet 1.8 Python 3.7 CPU Optimized)</code></li>
</ol>
</div>
<section id="install-packages" class="cell markdown" id="ukckBAwWbJmi">
<h3>Install packages</h3>
</section>
<div class="cell code" data-colab="{"base_uri":"https://localhost:8080/","height":1000}" id="Lmv-RkiLbJmi" data-outputId="6c85a3ce-56b0-47bf-b088-50341dbe2c11">
<div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="op">-</span>U pip</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="op">-</span>U setuptools wheel</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="op">-</span>U <span class="st">"mxnet<2.0.0"</span> bokeh<span class="op">==</span><span class="fl">2.0.1</span></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install autogluon <span class="op">--</span>no<span class="op">-</span>cache<span class="op">-</span><span class="bu">dir</span></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Without --no-cache-dir, smaller aws instances may have trouble installing</span></span></code></pre></div>
<div class="output stream stdout">
<pre><code>Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.1.2)
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (67.7.2)
Collecting setuptools
Downloading setuptools-67.8.0-py3-none-any.whl (1.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.1/1.1 MB 49.9 MB/s eta 0:00:00
ent already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (0.40.0)
Installing collected packages: setuptools
Attempting uninstall: setuptools
Found existing installation: setuptools 67.7.2
Uninstalling setuptools-67.7.2:
Successfully uninstalled setuptools-67.7.2
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
ipython 7.34.0 requires jedi>=0.16, which is not installed.
Successfully installed setuptools-67.8.0
</code></pre>
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<div class="output display_data">
<div class="sourceCode" id="cb3"><pre class="sourceCode json"><code class="sourceCode json"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">{</span><span class="dt">"pip_warning"</span><span class="fu">:{</span><span class="dt">"packages"</span><span class="fu">:</span><span class="ot">[</span><span class="st">"_distutils_hack"</span><span class="ot">,</span><span class="st">"pkg_resources"</span><span class="ot">,</span><span class="st">"setuptools"</span><span class="ot">]</span><span class="fu">}}</span></span></code></pre></div>
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<div class="output stream stdout">
<pre><code>Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting mxnet<2.0.0
Downloading mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl (49.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 49.1/49.1 MB 17.8 MB/s eta 0:00:00
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
panel 0.14.4 requires bokeh<2.5.0,>=2.4.0, but you have bokeh 2.0.1 which is incompatible.
torchaudio 2.0.2+cu118 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.
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Successfully installed Pillow-9.5.0 accelerate-0.16.0 aiohttp-3.8.4 aiosignal-1.3.1 antlr4-python3-runtime-4.9.3 async-timeout-4.0.2 autogluon-0.7.0 autogluon.common-0.7.0 autogluon.core-0.7.0 autogluon.features-0.7.0 autogluon.multimodal-0.7.0 autogluon.tabular-0.7.0 autogluon.timeseries-0.7.0 boto3-1.26.146 botocore-1.29.146 catboost-1.1.1 colorama-0.4.6 datasets-2.12.0 deprecated-1.2.14 dill-0.3.6 distlib-0.3.6 evaluate-0.3.0 fairscale-0.4.13 frozenlist-1.3.3 gluonts-0.12.8 huggingface-hub-0.15.1 jmespath-1.0.1 jsonschema-4.17.3 lightning-utilities-0.8.0 model-index-0.1.11 multidict-6.0.4 multiprocess-0.70.14 networkx-2.8.8 nlpaug-1.1.11 nptyping-2.4.1 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 omegaconf-2.2.3 openmim-0.3.7 ordered-set-4.1.0 pmdarima-1.8.5 pyDeprecate-0.3.2 pytesseract-0.3.10 pytorch-lightning-1.9.5 pytorch-metric-learning-1.7.3 ray-2.2.0 responses-0.18.0 s3transfer-0.6.1 sentencepiece-0.1.99 seqeval-1.2.2 sktime-0.15.1 statsforecast-1.4.0 tbats-1.1.3 tensorboardX-2.6 timm-0.6.13 tokenizers-0.13.3 torch-1.13.1 torchmetrics-0.8.2 torchvision-0.14.1 transformers-4.26.1 ujson-5.7.0 virtualenv-20.23.0 xxhash-3.2.0 yarl-1.9.2
</code></pre>
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<div class="output display_data">
<div class="sourceCode" id="cb5"><pre class="sourceCode json"><code class="sourceCode json"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">{</span><span class="dt">"pip_warning"</span><span class="fu">:{</span><span class="dt">"packages"</span><span class="fu">:</span><span class="ot">[</span><span class="st">"PIL"</span><span class="ot">,</span><span class="st">"pydevd_plugins"</span><span class="ot">]</span><span class="fu">}}</span></span></code></pre></div>
</div>
</div>
<section id="setup-kaggle-api-key" class="cell markdown" id="zpgmF7SYbJmk">
<h3>Setup Kaggle API Key</h3>
</section>
<div class="cell code" id="XxtMS1ObbJmk">
<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># create the .kaggle directory and an empty kaggle.json file</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>mkdir <span class="op">-</span>p <span class="op">/</span>root<span class="op">/</span>.kaggle</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>touch <span class="op">/</span>root<span class="op">/</span>.kaggle<span class="op">/</span>kaggle.json</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>chmod <span class="dv">600</span> <span class="op">/</span>root<span class="op">/</span>.kaggle<span class="op">/</span>kaggle.json</span></code></pre></div>
</div>
<div class="cell code" id="P1YzxdAGbJmk">
<div class="sourceCode" id="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Fill in your user name and key from creating the kaggle account and API token file</span></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> json</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>kaggle_username <span class="op">=</span> <span class="st">"ganeshchowdhary"</span></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>kaggle_key <span class="op">=</span> <span class="st">"9e46a5a30ab2b72b30f74f817f96761c"</span></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a><span class="co"># Save API token the kaggle.json file</span></span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(<span class="st">"/root/.kaggle/kaggle.json"</span>, <span class="st">"w"</span>) <span class="im">as</span> f:</span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a> f.write(json.dumps({<span class="st">"username"</span>: kaggle_username, <span class="st">"key"</span>: kaggle_key}))</span></code></pre></div>
</div>
<section id="download-and-explore-dataset" class="cell markdown" id="eWliIwjUbJml">
<h3>Download and explore dataset</h3>
</section>
<section id="go-to-the-bike-sharing-demand-competition-and-agree-to-the-terms" class="cell markdown" id="m9zJ8Mh1bJml">
<h3>Go to the bike sharing demand competition and agree to the terms</h3>
<p><img src="kaggle6.png" alt="kaggle6.png" /></p>
</section>
<div class="cell code" data-colab="{"base_uri":"https://localhost:8080/"}" id="Fn6XHwVdbJml" data-outputId="4cb25aaa-5b56-41dc-f9a4-bce2743ac08e">
<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Download the dataset, it will be in a .zip file so you'll need to unzip it as well.</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>kaggle competitions download <span class="op">-</span>c bike<span class="op">-</span>sharing<span class="op">-</span>demand</span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="co"># If you already downloaded it you can use the -o command to overwrite the file</span></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>unzip <span class="op">-</span>o bike<span class="op">-</span>sharing<span class="op">-</span>demand.<span class="bu">zip</span></span></code></pre></div>
<div class="output stream stdout">
<pre><code>Downloading bike-sharing-demand.zip to /content
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Archive: bike-sharing-demand.zip
inflating: sampleSubmission.csv
inflating: test.csv
inflating: train.csv
</code></pre>
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<div class="cell code" id="OF3JKVqSbJmm">
<div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> autogluon.tabular <span class="im">import</span> TabularPredictor</span></code></pre></div>
</div>
<div class="cell code" data-colab="{"base_uri":"https://localhost:8080/","height":443}" id="9-_475tSbJmm" data-outputId="e25f739f-8e71-4b86-9e35-0de39ee13813">
<div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create the train dataset in pandas by reading the csv</span></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Set the parsing of the datetime column so you can use some of the `dt` features in pandas later</span></span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>train <span class="op">=</span>pd.read_csv(<span class="st">'train.csv'</span>)</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>train[<span class="st">'datetime'</span>] <span class="op">=</span> pd.to_datetime(train[<span class="st">'datetime'</span>]) </span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>train.head()</span></code></pre></div>
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<th>workingday</th>
<th>weather</th>
<th>temp</th>
<th>atemp</th>
<th>humidity</th>
<th>windspeed</th>
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<th>registered</th>
<th>count</th>
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<td>1</td>
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<td>0</td>
<td>1</td>
<td>9.84</td>
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<td>40</td>
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<td>0</td>
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<td>9.02</td>
<td>13.635</td>
<td>80</td>
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<td>5</td>
<td>27</td>
<td>32</td>
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<td>2011-01-01 03:00:00</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>1</td>
<td>9.84</td>
<td>14.395</td>
<td>75</td>
<td>0.0</td>
<td>3</td>
<td>10</td>
<td>13</td>
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<td>2011-01-01 04:00:00</td>
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<td>0</td>
<td>0</td>
<td>1</td>
<td>9.84</td>
<td>14.395</td>
<td>75</td>
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<div class="cell code" data-colab="{"base_uri":"https://localhost:8080/","height":364}" id="z2uv1vbkbJmm" data-outputId="e374e85b-1dc1-4ab5-d16f-b97d5751a78b">
<div class="sourceCode" id="cb12"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Simple output of the train dataset to view some of the min/max/varition of the dataset features.</span></span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>train.describe()</span></code></pre></div>
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<th></th>
<th>season</th>
<th>holiday</th>
<th>workingday</th>
<th>weather</th>
<th>temp</th>
<th>atemp</th>
<th>humidity</th>
<th>windspeed</th>
<th>casual</th>
<th>registered</th>
<th>count</th>
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<tbody>
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<th>count</th>
<td>10886.000000</td>
<td>10886.000000</td>
<td>10886.000000</td>
<td>10886.000000</td>
<td>10886.00000</td>
<td>10886.000000</td>
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<td>10886.000000</td>
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<td>10886.000000</td>
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<td>2.506614</td>
<td>0.028569</td>
<td>0.680875</td>
<td>1.418427</td>
<td>20.23086</td>
<td>23.655084</td>
<td>61.886460</td>
<td>12.799395</td>
<td>36.021955</td>
<td>155.552177</td>
<td>191.574132</td>
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<th>std</th>
<td>1.116174</td>
<td>0.166599</td>
<td>0.466159</td>
<td>0.633839</td>
<td>7.79159</td>
<td>8.474601</td>
<td>19.245033</td>
<td>8.164537</td>
<td>49.960477</td>
<td>151.039033</td>
<td>181.144454</td>
</tr>
<tr>
<th>min</th>
<td>1.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>1.000000</td>
<td>0.82000</td>
<td>0.760000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>1.000000</td>
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<tr>