## DATAIDEA Quickstart
### What is the `dataidea` package?
This is a package we are currently developing to help new and old data analysists walk around some repetitive and sometimes disturbing tasks that a data analyst does day to day
This library currently extends and depends on majorly numpy, pandas as sklearn and these, among a few others will be installed once you install dataidea
## Installing `dataidea`
- To install dataidea, you must have python installed on your machine
- It's advised that you install it in a virtual environment
- You can install `dataidea` using the command below
```python
pip install dataidea
```
### Learning `dataidea`
The best way to get started with dataidea (and data analysis) is to complete the free course.
To see what’s possible with dataidea, take a look at the Quick Start
Read through the Tutorials to learn how to load datasets, train your own models on your own datasets. Use the navigation to look through the dataidea documentation. Every class, function, and method is documented here.
### Loading Datasets
`dataidea` has `loadDataset` method which allows you to quickly load inbuilt datasets useful for the course. You can import `loadDataset` from the `datasets` module and use it to load your dataset
```python
from dataidea.datasets import loadDataset
weather_data = loadDataset('weather')
weather_data.head()
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>day</th>
<th>temperature</th>
<th>windspead</th>
<th>event</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>01/01/2017</td>
<td>32.0</td>
<td>6.0</td>
<td>Rain</td>
</tr>
<tr>
<th>1</th>
<td>04/01/2017</td>
<td>NaN</td>
<td>9.0</td>
<td>Sunny</td>
</tr>
<tr>
<th>2</th>
<td>05/01/2017</td>
<td>28.0</td>
<td>NaN</td>
<td>Snow</td>
</tr>
<tr>
<th>3</th>
<td>06/01/2017</td>
<td>NaN</td>
<td>7.0</td>
<td>NaN</td>
</tr>
<tr>
<th>4</th>
<td>07/01/2017</td>
<td>32.0</td>
<td>NaN</td>
<td>Rain</td>
</tr>
</tbody>
</table>
</div>
### Saving and loading models
The `dataidea` package offers `saveModel` and `loadModel` which allow you save and load your models while maintaining the programming priciples that we learn through the course. You can import `saveModel` and `loadModel` from the models module.
```python
# dimporting the model
from sklearn.tree import DecisionTreeClassifier
# setting X and y
X = music_data.drop('genre', axis=1)
y = music_data.genre
# initializing and fitting the model
classifier = DecisionTreeClassifier()
classifier.fit(X, y)
```
<style>#sk-container-id-2 {
/* Definition of color scheme common for light and dark mode */
--sklearn-color-text: black;
--sklearn-color-line: gray;
/* Definition of color scheme for unfitted estimators */
--sklearn-color-unfitted-level-0: #fff5e6;
--sklearn-color-unfitted-level-1: #f6e4d2;
--sklearn-color-unfitted-level-2: #ffe0b3;
--sklearn-color-unfitted-level-3: chocolate;
/* Definition of color scheme for fitted estimators */
--sklearn-color-fitted-level-0: #f0f8ff;
--sklearn-color-fitted-level-1: #d4ebff;
--sklearn-color-fitted-level-2: #b3dbfd;
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--sklearn-color-icon: #696969;
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--sklearn-color-icon: #878787;
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color: var(--sklearn-color-text);
}
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padding: 0;
}
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clip: rect(1px 1px 1px 1px);
clip: rect(1px, 1px, 1px, 1px);
height: 1px;
margin: -1px;
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position: absolute;
width: 1px;
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border: 1px dashed var(--sklearn-color-line);
margin: 0 0.4em 0.5em 0.4em;
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padding-bottom: 0.4em;
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/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
but bootstrap.min.css set `[hidden] { display: none !important; }`
so we also need the `!important` here to be able to override the
default hidden behavior on the sphinx rendered scikit-learn.org.
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
display: inline-block !important;
position: relative;
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div.sk-serial,
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background-position: center center;
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/* Parallel-specific style estimator block */
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content: "";
width: 100%;
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flex-grow: 1;
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display: flex;
flex-direction: column;
align-items: center;
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padding-right: 1em;
padding-left: 1em;
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clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
*/
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text-align: center;
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color: var(--sklearn-color-text);
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/* Toggleable content - dropdown */
#sk-container-id-2 div.sk-toggleable__content {
max-height: 0;
max-width: 0;
overflow: hidden;
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overflow: auto;
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#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
content: "▾";
}
/* Pipeline/ColumnTransformer-specific style */
#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-unfitted-level-2);
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#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
background-color: var(--sklearn-color-fitted-level-2);
}
/* Estimator-specific style */
/* Colorize estimator box */
#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
/* fitted */
background-color: var(--sklearn-color-fitted-level-2);
}
#sk-container-id-2 div.sk-label label.sk-toggleable__label,
#sk-container-id-2 div.sk-label label {
/* The background is the default theme color */
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}
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color: var(--sklearn-color-text);
background-color: var(--sklearn-color-fitted-level-2);
}
/* Estimator label */
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font-weight: bold;
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border-radius: 0.25em;
box-sizing: border-box;
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/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
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#sk-container-id-2 div.sk-estimator.fitted {
/* fitted */
background-color: var(--sklearn-color-fitted-level-0);
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/* on hover */
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/* unfitted */
background-color: var(--sklearn-color-unfitted-level-2);
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#sk-container-id-2 div.sk-estimator.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-2);
}
/* Specification for estimator info (e.g. "i" and "?") */
/* Common style for "i" and "?" */
.sk-estimator-doc-link,
a:link.sk-estimator-doc-link,
a:visited.sk-estimator-doc-link {
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font-size: smaller;
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font-family: monospace;
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border-radius: 1em;
height: 1em;
width: 1em;
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margin-left: 1ex;
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color: var(--sklearn-color-unfitted-level-1);
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/* fitted */
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div.sk-estimator:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover,
div.sk-label-container:hover .sk-estimator-doc-link:hover,
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</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> DecisionTreeClassifier<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html">?<span>Documentation for DecisionTreeClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>
Now that we have trained our model, let's try saving and loading it, and use the loaded model for predictions
```python
from dataidea.models import saveModel, loadModel
# saving the model
saveModel(model=classifier, filename='genre_classifier.di')
# loading the model
loaded_model = loadModel(filename='genre_classifier.di')
# predicting X
loaded_model.predict(X)
```
array(['HipHop', 'HipHop', 'HipHop', 'Jazz', 'Jazz', 'Jazz', 'Classical',
'Classical', 'Classical', 'Dance', 'Dance', 'Dance', 'Acoustic',
'Acoustic', 'Acoustic', 'Classical', 'Classical', 'Classical',
'Classical', 'Classical'], dtype=object)
more to follow...
Raw data
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"description": "## DATAIDEA Quickstart\n\n### What is the `dataidea` package?\n\nThis is a package we are currently developing to help new and old data analysists walk around some repetitive and sometimes disturbing tasks that a data analyst does day to day\n\nThis library currently extends and depends on majorly numpy, pandas as sklearn and these, among a few others will be installed once you install dataidea\n\n## Installing `dataidea`\n\n- To install dataidea, you must have python installed on your machine\n- It's advised that you install it in a virtual environment\n- You can install `dataidea` using the command below\n\n```python\npip install dataidea\n```\n\n### Learning `dataidea`\n\nThe best way to get started with dataidea (and data analysis) is to complete the free course.\n\nTo see what\u2019s possible with dataidea, take a look at the Quick Start\n\nRead through the Tutorials to learn how to load datasets, train your own models on your own datasets. Use the navigation to look through the dataidea documentation. Every class, function, and method is documented here.\n\n### Loading Datasets\n\n`dataidea` has `loadDataset` method which allows you to quickly load inbuilt datasets useful for the course. You can import `loadDataset` from the `datasets` module and use it to load your dataset\n\n```python\nfrom dataidea.datasets import loadDataset\n\nweather_data = loadDataset('weather')\nweather_data.head()\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>day</th>\n <th>temperature</th>\n <th>windspead</th>\n <th>event</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>01/01/2017</td>\n <td>32.0</td>\n <td>6.0</td>\n <td>Rain</td>\n </tr>\n <tr>\n <th>1</th>\n <td>04/01/2017</td>\n <td>NaN</td>\n <td>9.0</td>\n <td>Sunny</td>\n </tr>\n <tr>\n <th>2</th>\n <td>05/01/2017</td>\n <td>28.0</td>\n <td>NaN</td>\n <td>Snow</td>\n </tr>\n <tr>\n <th>3</th>\n <td>06/01/2017</td>\n <td>NaN</td>\n <td>7.0</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>07/01/2017</td>\n <td>32.0</td>\n <td>NaN</td>\n <td>Rain</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n### Saving and loading models\n\nThe `dataidea` package offers `saveModel` and `loadModel` which allow you save and load your models while maintaining the programming priciples that we learn through the course. You can import `saveModel` and `loadModel` from the models module.\n\n```python\n# dimporting the model\nfrom sklearn.tree import DecisionTreeClassifier\n\n# setting X and y\nX = music_data.drop('genre', axis=1)\ny = music_data.genre\n\n# initializing and fitting the model\nclassifier = DecisionTreeClassifier()\nclassifier.fit(X, y)\n```\n\n<style>#sk-container-id-2 {\n /* Definition of color scheme common for light and dark mode */\n --sklearn-color-text: black;\n --sklearn-color-line: gray;\n /* Definition of color scheme for unfitted estimators */\n --sklearn-color-unfitted-level-0: #fff5e6;\n --sklearn-color-unfitted-level-1: #f6e4d2;\n --sklearn-color-unfitted-level-2: #ffe0b3;\n --sklearn-color-unfitted-level-3: chocolate;\n /* Definition of color scheme for fitted estimators */\n --sklearn-color-fitted-level-0: #f0f8ff;\n --sklearn-color-fitted-level-1: #d4ebff;\n --sklearn-color-fitted-level-2: #b3dbfd;\n --sklearn-color-fitted-level-3: cornflowerblue;\n\n /* Specific color for light theme */\n --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n --sklearn-color-icon: #696969;\n\n @media (prefers-color-scheme: dark) {\n /* Redefinition of color scheme for dark theme */\n --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n --sklearn-color-icon: #878787;\n }\n}\n\n#sk-container-id-2 {\n color: var(--sklearn-color-text);\n}\n\n#sk-container-id-2 pre {\n padding: 0;\n}\n\n#sk-container-id-2 input.sk-hidden--visually {\n border: 0;\n clip: rect(1px 1px 1px 1px);\n clip: rect(1px, 1px, 1px, 1px);\n height: 1px;\n margin: -1px;\n overflow: hidden;\n padding: 0;\n position: absolute;\n width: 1px;\n}\n\n#sk-container-id-2 div.sk-dashed-wrapped {\n border: 1px dashed var(--sklearn-color-line);\n margin: 0 0.4em 0.5em 0.4em;\n box-sizing: border-box;\n padding-bottom: 0.4em;\n background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-2 div.sk-container {\n /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n but bootstrap.min.css set `[hidden] { display: none !important; }`\n so we also need the `!important` here to be able to override the\n default hidden behavior on the sphinx rendered scikit-learn.org.\n See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n display: inline-block !important;\n position: relative;\n}\n\n#sk-container-id-2 div.sk-text-repr-fallback {\n display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n /* draw centered vertical line to link estimators */\n background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n background-size: 2px 100%;\n background-repeat: no-repeat;\n background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-2 div.sk-parallel-item::after {\n content: \"\";\n width: 100%;\n border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n flex-grow: 1;\n}\n\n#sk-container-id-2 div.sk-parallel {\n display: flex;\n align-items: stretch;\n justify-content: center;\n background-color: var(--sklearn-color-background);\n position: relative;\n}\n\n#sk-container-id-2 div.sk-parallel-item {\n display: flex;\n flex-direction: column;\n}\n\n#sk-container-id-2 div.sk-parallel-item:first-child::after {\n align-self: flex-end;\n width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:last-child::after {\n align-self: flex-start;\n width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:only-child::after {\n width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-2 div.sk-serial {\n display: flex;\n flex-direction: column;\n align-items: center;\n background-color: var(--sklearn-color-background);\n padding-right: 1em;\n padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-2 div.sk-toggleable {\n /* Default theme specific background. It is overwritten whether we have a\n specific estimator or a Pipeline/ColumnTransformer */\n background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-2 label.sk-toggleable__label {\n cursor: pointer;\n display: block;\n width: 100%;\n margin-bottom: 0;\n padding: 0.5em;\n box-sizing: border-box;\n text-align: center;\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n /* Arrow on the left of the label */\n content: \"\u25b8\";\n float: left;\n margin-right: 0.25em;\n color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-2 div.sk-toggleable__content {\n max-height: 0;\n max-width: 0;\n overflow: hidden;\n text-align: left;\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content pre {\n margin: 0.2em;\n border-radius: 0.25em;\n color: var(--sklearn-color-text);\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n /* unfitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n /* Expand drop-down */\n max-height: 200px;\n max-width: 100%;\n overflow: auto;\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n content: \"\u25be\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n color: var(--sklearn-color-text);\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n#sk-container-id-2 div.sk-label label {\n /* The background is the default theme color */\n color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n color: var(--sklearn-color-text);\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n color: var(--sklearn-color-text);\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-2 div.sk-label label {\n font-family: monospace;\n font-weight: bold;\n display: inline-block;\n line-height: 1.2em;\n}\n\n#sk-container-id-2 div.sk-label-container {\n text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-2 div.sk-estimator {\n font-family: monospace;\n border: 1px dotted var(--sklearn-color-border-box);\n border-radius: 0.25em;\n box-sizing: border-box;\n margin-bottom: 0.5em;\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-2 div.sk-estimator:hover {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted:hover {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n float: right;\n font-size: smaller;\n line-height: 1em;\n font-family: monospace;\n background-color: var(--sklearn-color-background);\n border-radius: 1em;\n height: 1em;\n width: 1em;\n text-decoration: none !important;\n margin-left: 1ex;\n /* unfitted */\n border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n /* fitted */\n border: var(--sklearn-color-fitted-level-1) 1pt solid;\n color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-3);\n color: var(--sklearn-color-background);\n text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-3);\n color: var(--sklearn-color-background);\n text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n display: none;\n z-index: 9999;\n position: relative;\n font-weight: normal;\n right: .2ex;\n padding: .5ex;\n margin: .5ex;\n width: min-content;\n min-width: 20ex;\n max-width: 50ex;\n color: var(--sklearn-color-text);\n box-shadow: 2pt 2pt 4pt #999;\n /* unfitted */\n background: var(--sklearn-color-unfitted-level-0);\n border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n /* fitted */\n background: var(--sklearn-color-fitted-level-0);\n border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-2 a.estimator_doc_link {\n float: right;\n font-size: 1rem;\n line-height: 1em;\n font-family: monospace;\n background-color: var(--sklearn-color-background);\n border-radius: 1rem;\n height: 1rem;\n width: 1rem;\n text-decoration: none;\n /* unfitted */\n color: var(--sklearn-color-unfitted-level-1);\n border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted {\n /* fitted */\n border: var(--sklearn-color-fitted-level-1) 1pt solid;\n color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-2 a.estimator_doc_link:hover {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-3);\n color: var(--sklearn-color-background);\n text-decoration: none;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>\n\nNow that we have trained our model, let's try saving and loading it, and use the loaded model for predictions\n\n```python\nfrom dataidea.models import saveModel, loadModel\n\n# saving the model\nsaveModel(model=classifier, filename='genre_classifier.di')\n\n# loading the model\nloaded_model = loadModel(filename='genre_classifier.di')\n# predicting X\nloaded_model.predict(X)\n```\n\n array(['HipHop', 'HipHop', 'HipHop', 'Jazz', 'Jazz', 'Jazz', 'Classical',\n 'Classical', 'Classical', 'Dance', 'Dance', 'Dance', 'Acoustic',\n 'Acoustic', 'Acoustic', 'Classical', 'Classical', 'Classical',\n 'Classical', 'Classical'], dtype=object)\n\nmore to follow...\n\n\n",
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