ICP 4 - PrakashRavella/Python_DeepLearning GitHub Wiki

Welcome to the Python_DeepLearning wiki!

PYTHON DEEP LEARNING

IN CLASS PROGRAMMING 4

NAME : Jaya Prakash Ravella

CLASS ID : 28

**APPROACH : **

In The Machine Leaning Algorithm

we have certain Steps to follow we will
1.Collect Data
2.Divide the data into two parts Trained Data and test data
3.Train the Model
4.After training the data check whether we the model is correct or not with test data
5.If the Model is working good we will follow the Model Otherwise Change the Model Parameters and Predict the outcomes from the model

Objective: To implement different algorithms using the iris dataset

Program 1: Implementing Naïve Bayes method using scikit-learn library. Here we divide the data into training set and Test set. We will fit the model with the training set and test it using the test set. We will then calculate the accuracy. I have used the Gaussian model. we can also use Multinomial and Bernoulli.

**Code : **

Naive Bayes

OutPut:

Naive Bayes Output

**Program 2 : ** Implement SVM algorithm using the same Data Set and library.Here we use the SVM linear parameter.

Code :

Output:

**Program 3 : ** use the SVM with RBF kernel on the same dataset. How the result changed ?

Code : RBF Kernel

**Output : **

In This my Result is not changed for the data set with two different approaches

Conclusion :

In this we have learned about the classification regression and different algorithms to solve deep learning problems .