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Showing posts from August, 2020

Tuples in Python

 A tuple is an assortment of items which requested and permanent. Tuples are successions, very much like records. The contrasts among tuples and records are, the tuples can't be changed not normal for records and tuples use enclosures, though records utilize square sections.  Making a tuple is pretty much as straightforward as putting diverse comma-isolated qualities. Alternatively you can put these comma-isolated qualities between enclosures moreover. For instance −  tup1 = ('material science', 'science', 1997, 2000);  tup2 = (1, 2, 3, 4, 5 );  tup3 = "a", "b", "c", "d";  The void tuple is composed as two enclosures containing nothing −  tup1 = ();  To compose a tuple containing a solitary worth you need to incorporate a comma, despite the fact that there is just one worth −  tup1 = (50,);  Like string files, tuple records start at 0, and they can be cut, linked, etc.  Getting to Values in Tuples  To get to values in tuple, u...

How to Train Neural Network?

One of the most important part of deep learning is training the neural networks. So, let's learn how it actually works. In this article, we will try to learn how a neural network gets to train. We will also learn about feedforward method and backpropagation method in Deep Learning. Why training is needed? Training in deeplearning is the process which helps machines to learn about the function/equation. We have to find the optimal values of the weights of a neural network to get the desired output. To train a neuralnetwork , we use the iterative method using gradient descent. Initially we start with random initialization of the weights. After random initialization of the weights, we make predictions on the data with the help of forward-propagation method, then we compute the corresponding cost function C , or loss and update each weight w by an amount proportional to dC/dw , i.e., the derivative of the cost functions w.r.t. the weight. The proportionality constant is kn...

Data mining and its data functionalities

A Detailed explanation In this article, I will try to explain you what are some of the data mining functionalities involved in any data mining process. So before going into much detail about data mining functionalities. Lets first try to understand what is Data Mining? What is Data Mining?  Data mining is a process where we try to find out hidden patterns, insights, or information from a large data set. Nowadays data mining is used by most companies to turn their raw data into some useful information. So that businesses can learn more about their customers and their behaviors to develop more effective marketing strategies , which helps the company to increase sales and decrease the costs. Data mining depends on effective data collection, warehousing, and computer processing power. Data mining process involves five main stages: 1. Understanding your project goal 2. Understanding the data sources 3. Preparing the data 4. Data Analysis 5. Results reviews Understanding your ...

Real world examples of data mining

In this article, I will try to give you some of the real-world examples of data mining. ·       Data mining and its data functionalities Datamining,  predictive analysis or knowledge discovery all of these terms are used in different places by different people but all of these terms mean one and the same. Let’s try to understand it in a simpler word, these terms refer to a set of techniques for discovering hidden patterns or insights from a large dataset. These patterns help in creating a  predictive model  to stay on top of future behaviors. Today, almost all of the organizations irrespective of whatever their domain is looking to capitalize on their  BigData  and are hence favoring using sophisticated analytical methods to derive some  hidden insights from the data  which can help the organization to stand-in this competitive world. As the consumption of Big Data grew, so did the need for data mining. Today, we can see applic...

Verified Data Mining Functionalities

What are Data Mining functionalities? Data mining functionalities are used to specify what kind of pattern are present in our data during data mining tasks. We can further divide data mining tasks into two different categories. 1.     Descriptive mining task 2.     Predictive mining task Descriptive mining task In descriptive mining tasks we try to find out the general properties present in our data. For example, we find data describing patterns and come up with new and significant information present in our available dataset. Predictive mining task In predictive mining tasks we try to find out some inference on the current data in order to make some predictions from the available data for the future. What is Characterization and Discrimination? When we try to summarize some general characteristics or features present in our target class of data then it's known as  Data Characterization.  Whereas when we try to compare general features...

Reindexing in Python Pandas

Reindexing is used to change the row labels and column labels of a DataFrame. It means to conform the data to match a given set of labels along a particular axis. It helps us to perform Multiple operations through indexing like – ·         To insert a missing value (NaN) markers in label locations where no data for the label existed before. ·         To reorder the existing data to match a new set of labels .   Example : import pandas as pd import numpy as np N=20 data = pd.DataFrame({    'A': pd.date_range(start='2016-01-01',periods=N,freq='D'),    'x': np.linspace(0,stop=N-1,num=N),    'y': np.random.rand(N),    'C': np.random.choice(['Low','Medium','High'],N).tolist(),    'D': np.random.normal(100, 10, size=(N)).tolist() }) #reindexing the DataFrame data_reindexed = data.reindex(index=[0,2,5], columns=['A', 'C', 'B']) pr...