Deep dive analysis of Binary Classification, Multi-class classification, and Multi-label classification
Classification in machine learning refers to a supervised approach of learning target class function that maps each attribute set to one of the predefined class labels. In other words, classification refers to predictive modeling where a target class is predicted given a set of input data.
There are various types of Classification problems, such as:
- Binary Classification
- Multi-class Classification
- Multi-label Classification
In the further article, you can read about a deep-dive understanding of the above-mentioned classification types along with their evaluation metrics and examples.
1. Binary Classification:
Binary Classification is a type of supervised classification problem where the target class label has two classes and the task is to predict one of the classes. …
Wonder how a language detection and translation system works, Use open-sourced Python libraries to develop the same in few lines of code
Text Language Identification refers to the process of predicting the language of a given text, whereas Text Translation refers to the process of translating a given text from one language to another. Often in Natural Language Processing projects, we come across a situation where the language of the text is unknown or the language of the given text document is varied according to our needs. Hence detecting the text and translating it to another language is a task in itself.
In this article, we have used some open-sourced libraries from google to detect the language of the text and translate it to our desired language. …
Perform Operations on a large dataset using vaex data frame
Pandas is one of the most popular libraries used for data science case studies. It is one of the best tools for exploratory data analysis and data wrangling. Pandas works efficiently well with small or medium-size datasets which fit best into the memory. For out of core dataset or large dataset pandas is inefficient to perform operations. One needs to spend a lot of time performing exploratory data analysis for a large size dataset using a pandas data frame.
Here Vaex comes into rescue that has a similar API that of pandas and is very efficient to perform out of memory datasets. …
What are the types of Missing Data with Examples?
The real-world dataset often has a lot of missing values. The cause of the presence of missing values in the dataset can be loss of information, disagreement in uploading the data, and many more. Missing values need to be imputed to proceed to the next step of the model development pipeline. Before imputing the missing values, it's important to understand the type of missing value present in the dataset.
Why missing data is a problem
Missing values present in the dataset can impact the performance of the model by creating a bias in the dataset. This bias can create a lack of relatability and trustworthiness in the dataset. …
Deep dive analysis of k-Means and k-Medoids clustering
Clustering is an unsupervised machine learning technique that separates or divides data points into several clusters or groups in such a way that points in the same cluster are similar to each other and points in different clusters are different from each other. There are various clustering algorithms such as:
- k-Means Clustering
- DBSCAN
- Hierarchical Clustering (Agglomerative and Divisive)
- OPTICS
- Fuzzy Clustering
and many more. k-Means clustering is one of the popular unsupervised clustering algorithms. In this article, you can read about the mathematical background of the k-Means algorithm and how to improve its interpretability using the k-Medoids algorithm.
k-Means Clustering:
k-Means clustering is a centroid based clustering algorithm. It clusters data points into k-clusters in such a way that points in the same cluster are similar to each other and points in different clusters are different. Each of the clusters formed has an equal distribution of data points. Each cluster is represented by its centroid. …
Custom Image Dataset using a Python Library
Data is the basic requirement for any data science projects. Dataset can be available in any type depending on the project. Data can be present in the form of audio, video, text, images, etc. A good amount of dataset is required to train a robust machine learning/deep learning model.
Many times we are not able to search for the appropriate image dataset required for a particular project. Searching and downloading images from the web and annotating it manually requires a lot of time and manpower. …
Overview and Implementation of Sweetviz library
Exploratory data analysis (EDA) is an approach to analyze the data and summarize its main characteristics, often with visual methods. A data scientist spends most of the time understanding data and getting insights. EDA is an essential and time-consuming step in the end-to-end machine learning pipeline.
EDA involves a lot of steps including some statistical tests, quantitative tests, visualization of data, and many more. Some of the key steps for EDA are:
- Data Quality Check: It refers to the analysis of each feature such as data types, duplicate values, missing values, etc. …
Interact with Kaggle using the command-line tool
Kaggle is the world’s largest data science community with powerful tools, datasets, and other resources to help you achieve your data science goals. Kaggle contains tons of freely available datasets used for educational purposes. It also hosts competitions and has freely available notebook to explore and run data science and machine learning models.
To use Kaggle resources and participate in Kaggle competitions you need to log in to the Kaggle website and search correspondingly. To download a dataset from Kaggle one needs to search for the dataset and download it manually and move to the desired folder to further explore.
All interactions with Kaggle can be done using a Kaggle API via the command-line tool (CLI) implemented in Python. …
Implement an animated racing bar chart in python
Python has achieved popularity in a very short span of time, due to the presence of a large number of open-source libraries, still, it falls short when coming to create dynamic plots. Exploratory Data Analysis is one of the essential key components for an end-to-end machine learning project.
Development of plots for data visualization is required for the EDA of the dataset. There are various libraries such as seaborn, matplotlib, ggplot, etc that create great plots for data visualization. When it comes to dynamic plots or animated plots, these libraries fail to develop animated plots. …
Artificial Intelligence-based code documentation assistant
Artificial Intelligence is extensively used for developing models to help developers write better source code. Various AI models are used to accelerate the work of developers, such as code autocompletion, autosuggestions, unit test assistance, bug detection, code summarization, etc.
Documentation of code is essential while delivering the project. For large code snippets having thousands of lines of code, it's very difficult to understand the code functionality for the next set of developers, testers, or clients, and hence code documentation of comments is essential. Most of the developers find it difficult or forget to write documentation or comments.
It is a big challenge for the developers to cope up with writing code, testing the code, and keeping up with its documentation. Docly comes to the rescue, as it automatically generates documentation for python code using state-of-the-art natural language processing. …
