MAchine Learning Day 1

In [1]:

# we will start with supervised learning or we can say regression. 
#Simple linear equation and multiple regression will be covered

In [2]:

#Simple Linear equation

In [3]:

#y=mx+b. b is the intercept and m is the slope. in DS, the formula would bez F(x1)=w0+w1x1

In [4]:

#multiple regression

In [5]:

#F(x1)=w0+w1x1....so  F(x1,x2,x3..xn)=w0+w1x1 +w2x2.....wnxn

In [6]:

#we weed values for W in the above example from the algorithm

In [7]:

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

In [8]:

from sklearn import datasets,linear_model

In [9]:

diabetes=datasets.load_diabetes()
print(diabetes.DESCR)

.. _diabetes_dataset:

Diabetes dataset
----------------

Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after baseline.

**Data Set Characteristics:**

  :Number of Instances: 442

  :Number of Attributes: First 10 columns are numeric predictive values

  :Target: Column 11 is a quantitative measure of disease progression one year after baseline

  :Attribute Information:
      - Age
      - Sex
      - Body mass index
      - Average blood pressure
      - S1
      - S2
      - S3
      - S4
      - S5
      - S6

Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).

Source URL:
https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html

For more information see:
Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) "Least Angle Regression," Annals of Statistics (with discussion), 407-499.
(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)

In [10]:

from sklearn.metrics import mean_squared_error

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diabetes_X=diabetes.data[:,np.newaxis,2]

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#np.newaxis command formats he data into the desire form

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diabetes_X_test=diabetes_X[-30:]
diabetes_X_training=diabetes_X[:-30]

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#[-30:] means first 30% data

In [18]:

diabetes_y_test=diabetes.target[-30:]
diabetes_y_training=diabetes.target[:-30]

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#y ius the taget column

In [19]:

model=linear_model.LinearRegression()

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model.fit(diabetes_X_training,diabetes_y_training)

Out[20]:

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

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diabetes_y_predict=model.predict(diabetes_X_test)

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plt.scatter(diabetes_X_training,diabetes_y_training)
plt.show()

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plt.scatter(diabetes_X_training,diabetes_y_training)
plt.plot(diabetes_X_test,diabetes_y_predict)
plt.show()

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print("coef",model.coef_)

coef [941.43097333]

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print("Intercept=",model.intercept_)

Intercept= 153.39713623331698

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print("Mean Squared Error is ",mean_squared_error(diabetes_y_test,diabetes_y_predict))

Mean Squared Error is  3035.0601152912695

In [28]:

type(diabetes)

Out[28]:

sklearn.utils.Bunch

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