add diabetes.py and csv for intro to ML

requirements.txt

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+-r requirements/requirements-3.6.txt

src/nlpia/book/examples/diabetes.py

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+""" A brief introduction to machine learning and linear regression 
+
+Almost a verbatum copy of the code from the
+[sklearn tutorial](http://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html).
+Only difference is that Pandas DataFrames are used instead of the custom dataset format that sklearn uses.
+This makes the example code reusable for other problems/datasets that the student might see online (XLS, CSV, and HTML tables).
+
+The "diabetes" dataset is briefly described in the
+[sklearn documentation](http://scikit-learn.org/stable/datasets/index.html#diabetes-dataset).
+"""
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn import linear_model
+from sklearn.metrics import mean_squared_error, r2_score
+
+# # Load the diabetes dataset and conert it to a dataframe
+# from sklearn import datasets
+# diabetes = datasets.load_diabetes()
+# df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
+# df['diabetes_severity'] = diabetes.target
+# df.to_csv('diabetes.csv', index=None)
+
+df = pd.read_csv('diabetes.csv')
+print(df.describe())
+#                 age           sex           bmi        ...                    s5            s6  diabetes_severity
+# count  4.420000e+02  4.420000e+02  4.420000e+02        ...          4.420000e+02  4.420000e+02         442.000000
+# mean  -3.634599e-16  1.296411e-16 -8.042209e-16        ...         -3.830854e-16 -3.411950e-16         152.133484
+# std    4.761905e-02  4.761905e-02  4.761905e-02        ...          4.761905e-02  4.761905e-02          77.093005
+# min   -1.072256e-01 -4.464164e-02 -9.027530e-02        ...         -1.260974e-01 -1.377672e-01          25.000000
+# 25%   -3.729927e-02 -4.464164e-02 -3.422907e-02        ...         -3.324879e-02 -3.317903e-02          87.000000
+# 50%    5.383060e-03 -4.464164e-02 -7.283766e-03        ...         -1.947634e-03 -1.077698e-03         140.500000
+# 75%    3.807591e-02  5.068012e-02  3.124802e-02        ...          3.243323e-02  2.791705e-02         211.500000
+# max    1.107267e-01  5.068012e-02  1.705552e-01        ...          1.335990e-01  1.356118e-01         346.000000
+
+
+# Use only one feature, BMI (Body Mass Index)
+diabetes_X = df[['bmi']]
+diabetes_Y = df[['diabetes_severity']]
+
+# Ignore the last 20 examples/samples in the trianing set those as the test examples
+diabetes_X_train = diabetes_X[:-20]
+diabetes_X_test = diabetes_X[-20:]
+diabetes_y_train = diabetes_Y[:-20]
+diabetes_y_test = diabetes_Y[-20:]
+
+# Create linear regression object
+regr = linear_model.LinearRegression()
+
+# Train the model using the training sets
+regr.fit(diabetes_X_train, diabetes_y_train)
+
+# Make predictions using the testing set
+diabetes_y_pred = regr.predict(diabetes_X_test)
+
+# The coefficients
+print('Coefficients: \n', regr.coef_)
+# The mean squared error
+print("Mean squared error: %.2f"
+      % mean_squared_error(diabetes_y_test, diabetes_y_pred))
+# Explained variance score: 1 is perfect prediction
+print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))
+
+# Plot outputs
+plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
+plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)
+
+plt.xticks(())
+plt.yticks(())
+
+plt.show()
+