Data Science Applications in Python

The following list presents some of the Data Science Applications in Python.

Important Data Science Applications in Python and Their Solutions

Since, a data science application rely on analyzing the data, first of all we need to extract it from a data source. Furthermore, it could be a CSV file. So, create a python program to extract the data from a dataset and display it.
After that implement linear and logistic regression in python.
Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a CSV file. Further, compute the accuracy of the classifier, considering few test data sets.
In order to perform classification, we can use any one of the inbuilt classifier in python. You need to create two separate programs in python. First of all, implement KNN algorithm. Another program should implement SVM.
After that, using the same dataset implement random forest algorithm.
Further, write separate programs for implementing ANN as well as BPN.
Similarly, we can use unsupervised learning also. So, apply K-Means algorithm on a dataset.
Further, write a program to implement Self – Organizing Map (SOM)
Since, several supervised learning algorithms are available in python, we need to compare them for their respective accuracy. Hence, create a program for empirical comparison of these algorithms
Similarly, write a program for empirical comparison of different unsupervised learning algorithms. Furthermore, emphasize on different parameters for comparison.
Create python programs to process data in different formats like CSV, JSON, NoSQL, XML, and database.