# Running Jupyter with SciKit-learn

First start your Jupyter server using the short process:

1. Open https://notebooks.hpc.fau.edu
6. Click “New Server”
7. Click “New” -> “Terminal”
8. Enter “pip install U scikitlearn–user
1. You may wish to update your pip if you wish. This is not required.
2. Enter “pip install upgrade –user pip”
9. Wait for this to complete.
10. Exit the Jupyter server and log back in.
11. You should now have scikit-learn installed.

Running Scikit-learn code:

1. Click “New”
2. “Click Python 3”
3. Enter the following demo code from (https://scikit-learn.org/stable/auto_examples/plot_isotonic_regression.html#sphx-glr-auto-examples-plot-isotonic-regression-py)
```print(__doc__)

# Author: Nelle Varoquaux <nelle.varoquaux@gmail.com>
#         Alexandre Gramfort <alexandre.gramfort@inria.fr>

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection

from sklearn.linear_model import LinearRegression
from sklearn.isotonic import IsotonicRegression
from sklearn.utils import check_random_state

n = 100
x = np.arange(n)
rs = check_random_state(0)
y = rs.randint(-50, 50, size=(n,)) + 50. * np.log1p(np.arange(n))

# #############################################################################
# Fit IsotonicRegression and LinearRegression models

ir = IsotonicRegression()

y_ = ir.fit_transform(x, y)

lr = LinearRegression()
lr.fit(x[:, np.newaxis], y)  # x needs to be 2d for LinearRegression

# #############################################################################
# Plot result

segments = [[[i, y[i]], [i, y_[i]]] for i in range(n)]
lc = LineCollection(segments, zorder=0)
lc.set_array(np.ones(len(y)))
lc.set_linewidths(np.full(n, 0.5))

fig = plt.figure()
plt.plot(x, y, 'r.', markersize=12)
plt.plot(x, y_, 'g.-', markersize=12)
plt.plot(x, lr.predict(x[:, np.newaxis]), 'b-')