@ -67,15 +67,15 @@ In this course, you will use Scikit-Learn and other tools to build machine learn
Scikit-Learn makes it straightforward to build models and evaluate them for use. It is primarily focused on using numeric data and contains several ready-made datasets for use as learning tools. It also includes pre-built models for students to try. Let's explore the process of loading prepackaged data and using a built in estimator first ML model with Scikit-Learn with some basic data.
## Your First Scikit-Learn Notebook
> This tutorial was inspired by the [Linear Regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) on Skikit-Learn's web site.
> This tutorial was inspired by the [Linear Regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) on Scikit-Learn's web site.
In the `notebook.ipynb` file associated to this lesson, clear out all the cells by pressing the 'trash can' icon.
In this section, you will work with a small dataset about diabetes that is built into Scikit-Learn for learning purposes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic Regression Model, when visualized, might show groupings of variables that would help you organize your theoretical clinical trials.
In this section, you will work with a small dataset about diabetes that is built into Scikit-Learn for learning purposes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic Regression Model, when visualized, might show groupings of variables that would help you organize your theoretical clinical trials.
Let's get started on this task.
1. Import some libraries to help with your tasks. First, import matplotlib, a useful graphing tool. We will use it to create a line plot. Also import [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html), a useful library for handling numeric data in Python. Loa up datasets and the linear_model from the Scikit-Learn library. Load model_selection for splitting data into training and test sets. Finally, load the metrics package to handle some math tasks we will use to plot a line.
1. Import some libraries to help with your tasks. First, import `matplotlib`, a useful [graphing tool](https://matplotlib.org/). We will use it to create a line plot. Also import [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html), a useful library for handling numeric data in Python. Load up `datasets` and the `linear_model` from the Scikit-Learn library. Load `model_selection` for splitting data into training and test sets.
```python
import matplotlib.pyplot as plt
@ -95,12 +95,14 @@ s1 tc: T-Cells (a type of white blood cells)
3. In a new cell, load the diabetes dataset as data and target (X and y, loaded as a tuple). X will be a data matrix, and y will be the regression target. Add some print commands to show the shape of the data matrix and its first element:
> 🎓 A **tuple** is an [ordered list of elements](https://en.wikipedia.org/wiki/Tuple).
✅ Think a bit about the relationship between the data and the regression target. Linear regression predicts relationships between feature X and target variable y. Can you find the [target](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) for the diabetes dataset in the documentation? What is this dataset demonstrating, given that target?
```python
X, y = datasets.load_diabetes(return_X_y=True)
print(X.shape)
print(X[0])
```
You can see that this data has 442 items shaped in arrays of 10 elements:
```text
@ -116,7 +118,7 @@ X = X[:, np.newaxis, 2]
```
✅ At any time, print out the data to check its shape
5. Now that you have data ready to be plotted, you can see if a machine can help determine a logical split between the numbers in this dataset. To do this, you need to split both the data (X) and the targets (y) into test and training sets. Scikit-Learn has a straightforward way to do this; you can split your test data at a given point.
5. Now that you have data ready to be plotted, you can see if a machine can help determine a logical split between the numbers in this dataset. To do this, you need to split both the data (X) and the target (y) into test and training sets. Scikit-Learn has a straightforward way to do this; you can split your test data at a given point.
```python
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
@ -145,7 +147,7 @@ plt.show()
```
Congratulations, you just built your first Linear Regression model, created a prediction with it, and displayed it in a plot!
🚀 Challenge: Try to plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`
🚀 Challenge: Try to plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`.Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
Jen Looper
4 years ago