syamkakarla98 / Linear-Regression

Implementation of Linear regression on Boston House Pricing and Diabetes data sets using python.

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