#DataScience Project 4

Customer Segmentation

- Use Machine Learning to create a model that performs Customer Segmentation

Libraries Used
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Scikit learn

Models Trained
- KMeans Clustering
- Hierarchical Clustering Code for this project can be found here 👇

[Please do consider giving an upvote if you find this notebook to be useful 😀]

kaggle.com/piyalbanik/seg…

3 remote Data Science and Machine Learning Internship opportunities which are open for all.

🧵👇 1. Graduate Rotational Internship Program - The Sparks Foundation

The Graduate Rotational Internship Program is a unique offer for students and recent graduates to experience and join The Sparks Foundation.

Apply 👇
internship.thesparksfoundation.info

3 beginners level Machine Learning projects with code

- Regression
- Classification
- Clustering

🧵👇 1. Regression

https://twitter.com/PiyalBanik/status/1422577878126845963?s=20

#DataScience Project 3

Best Suburb to Open a Cafeteria in Melbourne 🇦🇺

- Create a Machine Learning model which suggests a location to open a Cafe.

Libraries Used
- Numpy
- Pandas
- Matplotlib
- Scikit Learn
- BeautifulSoup
- Geocoder
- Folium

Model Used:
- K Means Clustering Please Note: the main focus of this project was on data collection, visualization, and training a model. Did not involve data cleaning.

Code for this project 👇
github.com/Piyal-Banik/Me…

Data Science Pipeline

🧵👇 Acknowledgment:

- John Rollins, @IBM

- Data Science Methodology, @coursera
coursera.org/learn/data-sci…

#DataScience Project 1

Titanic – Machine Learning from Disaster

Use Machine Learning to create a model that predicts which passengers survived the Titanic shipwreck.

Libraries Used
- Numpy
- Pandas
- Seaborn
- Sickit-Learn

Final Model Chosen
- Decision Tree: 93.03% accuracy🔥 The data science methodology followed has been outlined by John Rollins, IBM

- Business Understanding
- Analytical Approach
- Data requirements
- Data collection
- Data Understanding
- Data Preparation
- Modeling
- Evaluation

Project Code 👇
github.com/Piyal-Banik/Ti…

Data Science Books 📚 you should start reading

🧵👇 1. Data Science from Scratch

You’ll learn how many of the most fundamental DS tools and algorithms work by implementing them from scratch. Includes:

- Python basics
- Linear algebra, statistics, & probability
- Data collection & EDA
- Basic ML Algo

learning.oreilly.com/library/view/d…

"People need to know Maths to become Data Scientists or Machine Learning Engineer"

- True! 😀

But, how much do we need to know? 🤔⁉️

This thread 🧵 is an outline of the concepts we should know 1. Let's start with Linear Algebra

You can start working on Data Science or ML without knowing them.

But at some time you may wish to dive deeper.

If you ask me, if there was 1 area of Maths that I would suggest you improve before the other, it would be Linear Algebra.

Here are this week's Data Science Interview Questions along with the correct answer

Thread 🧵👇

#MachineLearning #Python #100DaysOfCode Answer by @josh_ko_naman

1) SL has a feedback mechanism.
UL has no feedback mechanism.

2) Supervised learning involves building a model for predicting, or estimating.
In unsupervised learning, we can learn relationships and structures from data

https://twitter.com/PiyalBanik/status/1412400894105198592?s=20

15 Days roadmap to master #Python basics for #DataScience & #MachineLearning without having any Prior Experience.

[ Join the #100DaysOfCode & #66daysofdata challenge to keep yourself motivated ]

Thread 🧵👇 Few things to keep in mind before starting
- Learn By Doing, Practicing & Not Just Reading
- Code By Hand [very effective]
- Share, Teach, Discuss and Ask For Help
- Use Online Resources
- Be consistent
- Learn to Use Debugger

Want to learn Data Science but confused about where to start and what to follow?

Here are the ultimate 12 months Learning path to becoming a Data Scientist 👨‍🎓

Note: I'm personally following this roadmap

🧵👇

#DataScience #MachineLearning #100DaysOfCode #66DaysOfCode #Python Since we're currently in July, so start from this month.

Understanding Data Science and getting started with Python
- what is data science?
- what does a data scientist do?
- find out various resources
- Set up the system
- Learn Python basics
- Introduction to Pandas & Numpy

Best Data Science blogs to follow in 2021

🧵👇

#DataScience #66daysofdata #100DaysOfCode 1. Towards Data Science

TDS is a Medium publication having audience-oriented content about Data Science, along with blogs on related fields such as Machine Learning, Programming, Visualization, and Artificial Intelligence.

towardsdatascience.com

NumPy 🔥

It is a Linear Algebra Library for #Python, the reason it is so important for Data Science is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks👨‍🏫.

Here's everything you need🧵👇

#DataScience #100DaysOfCode 1⃣Numpy Arrays

NumPy arrays are the main way we use Numpy. Numpy arrays essentially come in two flavors: vectors and matrices. Vectors are strictly 1-d arrays and matrices are 2-d (but you should note a matrix can still have only one row or one column).

Ever wondered how a Data Scientist thinks about a problem? Here are the major steps involved in tackling a data science problem.

Thread 🧵👇

#DataScience #MachineLearning #100DaysOfCode 1. Business Understanding: We should have clarity of what is the exact problem we are going to solve.

What is the problem that we are trying to solve? - Asking the right questions as a Data Scientist starts with understanding the goal of the business.

Top 7 interesting careers related to Data Science to explore. Pick one and start learning.

Thread 🧵👇

#DataScience #ArtificialIntelligence #MachineLearning #BigData 1. Data Scientist

Data scientist use their analytical and technical capabilities to extract meaningful insight from data.

Everything you need to know about Strings in Python for Data Science

Thread 🧵👇

#DataScience #Python #100daysofcodechallenge 📌Looping Through a String

Since strings are arrays, we can loop through the characters in a string, with a for loop.

Python operators are easy and every aspiring Data Scientist need to know the common ones.

Thread 🧵👇

#Python #DataScience #100DaysOfCode #code #CodeNewbie 📌Python Arithmetic Operators:

Arithmetic operators are used with numeric values to perform common mathematical operations

There are 8 built in classes (variable types) in Python. A thread🧵👇

#Python #100DaysOfCode #CodeNewbie 1. Boolean- The Boolean data type is a truth value, either True​ ​ or Fals​e.

Top 5 things I am currently following to boost my learning curve in Data Science as a beginner 🧵👇

#DataScience #Python #100DaysOfCode 1. Trying to implement large projects from start to finish🧑‍💻: Well I am believer of "Learn Best by Doing". As I implement a project from scratch, I do get a lot of errors which ultimately teach me even more.