WEEK 7: • Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering.

 WEEK 7: Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering.












WEEK 9 - Write a program to implement SVM algorithm to classify the iris data set. Print both correct and wrong predictions.

 WEEK 9 - Write a program to implement SVM algorithm to classify the iris data set. Print both correct and wrong predictions.































WEEK 5: • Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.

 

WEEK 5: • Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.

















WEEK 10: Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.

 WEEK 10: Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.

Locally Weighted Regression Algorithm

Regression:

·       Regression is a technique from statistics that are used to predict values of the desired target quantity when the target quantity is continuous.

o   In regression, we seek to identify (or estimate) a continuous variable y associated with a given input vector x.

§  y is called the dependent variable.

§  x is called the independent variable.


Loess/Lowess Regression:

Loess regression is a nonparametric technique that uses local weighted regression to fit a smooth curve through points in a scatter plot.

Lowess Algorithm:

·       Locally weighted regression is a very powerful nonparametric model used in statistical learning.

·       Given a dataset X, y, we attempt to find a model parameter β(x) that minimizes residual sum of weighted squared errors.

·       The weights are given by a kernel function (k or w) which can be chosen arbitrarily

Algorithm

1. Read the Given data Sample to X and the curve (linear or nonlinear) to Y

2. Set the value for Smoothening parameter or Free parameter say τ

3. Set the bias /Point of interest set x0 which is a subset of X

4. Determine the weight matrix using:



5. Determine the value of model term parameter β using:


6. Prediction = x0*β

Python Program to Implement and Demonstrate Locally Weighted Regression Algorithm














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