Linear Regression Using Python

Click here to download the code.

Prerequisites

The things that you must have a decent knowledge on:

    * Python
    * Linear Algebra
    * Calculus

Installation

• This project is fully based on python. So, the necessary modules needed for computaion are:

    * Numpy
    * Sklearn
    * Matplotlib
    * Pandas

• The commands needed for installing the above modules on windows platfom are:

    pip install numpy
    pip install sklearn
    pip install matplotlib
 

• we can verify the installation of modules by importing the modules. For example:

    import numpy
    from sklearn.decomposition import kernelPCA 
    import matplotlib.pyplot as plt
    

Explanation

• Here were performing linear regression on the Boston house pricing dataset.

• The details of the dataset are:

  1. Title: Boston Housing Data

  2. Sources: (a) Origin: This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. (b) Creator: Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978. (c) Date: July 7, 1993

  3. Past Usage:

  • Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 1980. N.B. Various transformations are used in the table on pages 244-261.
  • Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.

  4. Relevant Information:

     Concerns housing values in suburbs of Boston.

  5. Number of Instances: 506

  6. Number of Attributes: 13 continuous attributes (including "class" attribute "MEDV"), 1 binary-valued attribute.

  7. Attribute Information:

     1. CRIM per capita crime rate by town
     2. ZN proportion of residential land zoned for lots over 25,000 sq.ft.
     3. INDUS proportion of non-retail business acres per town
     4. CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
     5. NOX nitric oxides concentration (parts per 10 million)
     6. RM average number of rooms per dwelling
     7. AGE proportion of owner-occupied units built prior to 1940
     8. DIS weighted distances to five Boston employment centres
     9. RAD index of accessibility to radial highways
     10. TAX full-value property-tax rate per $10,000
     11. PTRATIO pupil-teacher ratio by town
     12. B 1000(Bk - 0.63)^2 where Bk is the proportion of black by town
     13. LSTAT % lower status of the population
     14. MEDV Median value of owner-occupied homes in $1000's

  8. Missing Attribute Values: None.

• Click here to find the program LinearRegression_BOSTON_Dataset.py

Result:

• The above program results a scatter plot showed below:

  

• The output of the program is showed below:

  

click here to see the program LinearRegression_DIABETES_Dataset.py implementing linear regression on Diabetes dataset.

Conclusion

• Performed Linear Regression on BOSTON house pricing and Diabetes dataset.

License

This project is licensed under the MIT License - see the LICENSE.md