```
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from sklearn.linear_model import LinearRegression

df = px.data.tips()
X = df.total_bill.values.reshape(-1, 1)

model = LinearRegression()
model.fit(X, df.tip)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig = px.scatter(df, x='total_bill', y='tip', opacity=0.65)
fig.add_traces(go.Scatter(x=x_range, y=y_range, name='Regression Fit'))
fig.show()
```


Linear Regression with scikit-learn

```
import plotly.express as px

df = px.data.tips()
fig = px.scatter(
    df, x='total_bill', y='tip', opacity=0.65,
    trendline='ols', trendline_color_override='darkblue'
)
fig.show()
```


OLS with plotly.express

```
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from sklearn.svm import SVR

mesh_size = .02
margin = 0

df = px.data.iris()

X = df[['sepal_width', 'sepal_length']]
y = df['petal_width']

# Condition the model on sepal width and length, predict the petal width
model = SVR(C=1.)
model.fit(X, y)

# Create a mesh grid on which we will run our model
x_min, x_max = X.sepal_width.min() - margin, X.sepal_width.max() + margin
y_min, y_max = X.sepal_length.min() - margin, X.sepal_length.max() + margin
xrange = np.arange(x_min, x_max, mesh_size)
yrange = np.arange(y_min, y_max, mesh_size)
xx, yy = np.meshgrid(xrange, yrange)

# Run model
pred = model.predict(np.c_[xx.ravel(), yy.ravel()])
pred = pred.reshape(xx.shape)

# Generate the plot
fig = px.scatter_3d(df, x='sepal_width', y='sepal_length', z='petal_width')
fig.update_traces(marker=dict(size=5))
fig.add_traces(go.Surface(x=xrange, y=yrange, z=pred, name='pred_surface'))
fig.show()
```


3D regression surface

```
import plotly.express as px
import plotly.graph_objects as go
from sklearn.linear_model import LinearRegression

df = px.data.iris()
X = df[['sepal_width', 'sepal_length']]
y = df['petal_width']

# Condition the model on sepal width and length, predict the petal width
model = LinearRegression()
model.fit(X, y)
y_pred = model.predict(X)

fig = px.scatter(x=y, y=y_pred, labels={'x': 'ground truth', 'y': 'prediction'})
fig.add_shape(
    type="line", line=dict(dash='dash'),
    x0=y.min(), y0=y.min(),
    x1=y.max(), y1=y.max()
)
fig.show()
```


Prediction Error Plots

- Linear regression with scikit-learn and plotly
- Ordinary Least Square(OLS) with plotly.express
- 3D regression surface with px.scatter3d and go.Surface
- Prediction Error Plots

University of Michigan - Ann Arbor

The plotly Python library is an interactive, open-source plotting library that supports over unique chart types covering a wide range of statistical, financial, geographic, and scientific graphs. Built on top of the Plotly JavaScript library, plotly enables python users to create interactive web-based visualizations. This library is sometimes referred to as "plotly.py" to differentiate it from the JavaScript library. 

Plotly

Plotly Documentation

The plotly.express module contains functions that can create entire figures at once. It is usually imported as px and is a built-in part of the plotly library. Thus to use this module, simply use the code snippet 
```
import plotly.express as px
```

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