Piyal Banik Profile picture
Jun 17, 2021 8 tweets 3 min read Read on X
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 Image
📌Python Assignment Operators:

Assignment operators are used to assign values to variables Image
📌Python Comparison Operators:

Comparison operators are used to compare two values Image
📌Python Logical Operators:

Logical operators are used to combine conditional statements Image
📌Python Identity Operators:

Identity operators are used to compare the objects, not if they are equal, but if they are actually the same object, with the same memory location Image
📌Python Membership Operators

Membership operators are used to test if a sequence is presented in an object Image
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More from @PiyalBanik

Aug 17, 2021
#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…
1. Business Understanding

The goal of this project is to divide customers into groups based on common characteristics in order to maximize the value of each customer to the business.
Read 13 tweets
Aug 15, 2021
3 remote Data Science and Machine Learning Internship opportunities which are open for all.

🧵👇 Image
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
2. Omenda

Omdena AI projects are the best way to build sought-after data science and machine learning skills while solving real-world problems.

Apply 👇
omdena.com/projects/
Read 5 tweets
Aug 12, 2021
3 beginners level Machine Learning projects with code

- Regression
- Classification
- Clustering

🧵👇
Read 5 tweets
Aug 8, 2021
#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…
1. Business Understanding:

The main goal of this project is to collect and analyze data in order to select a location in Melbourne to open a Cafeteria. We want to help a business owner planning to open up a Cafe in a location by exploring better facilities around the Suburb.
Read 17 tweets
Jul 26, 2021
Data Science Pipeline

🧵👇
Acknowledgment:

- John Rollins, @IBM

- Data Science Methodology, @coursera
coursera.org/learn/data-sci…
1. Business Understanding: What is the problem that we are trying to solve?

- We should have clarity of what is the exact problem we are going to solve.

- Asking the right questions as a Data Scientist starts with understanding the goal of the business.
Read 13 tweets
Jul 25, 2021
#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…
1. Business Understanding

Given a passenger's information, how can we predict whether he/she survived the Titanic disaster?

2. Analytical Approach:

Our target variable is categorical [survived / not survived], and hence we need classification models for this task.
Read 15 tweets

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