Epython Lab

#KeyNote #UnsupervisedMachineLearning #Clustering #k-means Clustering is one of unsupervised machine learning algorithm. There are many models for clustering out there. Despite its simplicity, the K-means is vastly used for clustering in many data science applications, especially useful if you need to quickly discover insights from unlabeled data. Some real-world applications of k-means: - Customer segmentation - Understand what the visitors of a website are trying to accomplish - Pattern recognition - Machine learning - Data compression

String Manipulation in Python 3. For beginners #Subscribe to receive new topic. https://youtu.be/6Ey9bQ-KJuk

String Operations in Python 3. For beginners #Subscribe to receive new topic https://www.youtube.com/watch?v=MKtAA4ZnmkQ

#InternshipOpportunity @epythonlab https://docs.google.com/forms/d/e/1FAIpQLSfWyYpbGq-b96SO1f2MM5zlMwpq_QkJwqyaucDgCFs654IG6g/viewform

This is for absolute beginners. Expressions in Python. In this lab you will get to know about:- What are the expressions in Python? How to construct expressions in Python? #Subscribe #Share https://youtu.be/KOA3j2tbr4M

#KeyNote #DataScience #datanalytics #modeltrain #futureprediction Data Analytics, we often use Model Development to help us predict future observations from the data we have. A Model will help us understand the exact relationship between different variables and how these variables are used to predict the result. @epythonlab

QUESTION OF THE DAY ARE DATA NORMALIZATION AND DATA STANDARDIZATION THE SAME? EXPLAIN? WITH EXAMPLE? #DataScience #datatrnasformation #datacleansing #datapreprocessing SEND YOUR ANSWER TO @PYDISCUSSION

Operators in Python Don't forget to subscribe to YouTube to receive more tutorials. https://youtu.be/HhTdMVRNO6E

#Datascience #database #sql #python SQL is one of the most common computer languages in use for working with data today. It is a standardized language for accessing and manipulating relational databases. While it is relatively limited compared to a general programming language such as Python, it is highly optimized for efficient retrieval and aggregation of data from database tables. Its broad support and use virtually guarantees that any professional data scientist or analyst will encounter SQL eventually. Furthermore, SQL is often the paradigm used to discuss the relational data model, which has implications that apply beyond SQL compliant databases. Relational data model The relational data model for the most part corresponds with our intuitive notion of a table. Each row is a relation, usually representing some object, event, or idea. Each column corresponds with an attribute which characterizes the relation. In order to reduce redundancy in a database, when creating at able we typically include the minimum amount of attributes required to fully define a relation. This (admittedly vague) guideline is formalized in the idea of database normalization.

#Database #SQL #Datascience #python #DP_API A Python code to connect to the database using #DB-API

#KeyNote #SQL #Database #DataAnalyzes #RDMS #Python Benefits of Python for Database Programming - Python is a popular scripting language to connect to the database and analyzes the data. - Python ecosystem: - NumPy, pandas, matplotlib, SciPy - Ease of use - Python supports relational database systems - Python database API's to connect to the database - Detailed documentation: The python is easily available @Epythonlab

#DataScience #ML @epythonlab

Types of Errors in Python Subscribe to receive new topics. Thanks for watching! https://youtu.be/xZBPHQLJ4Ag

Analyze Your Personal Netflix Data @epythonlab https://www.dataquest.io/blog/python-tutorial-analyze-personal-netflix-data/