Piyal Banik Profile picture
Jun 18, 2021 8 tweets 3 min read Read on X
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.
📌String Length
To get the length of a string, use the len() function.

📌Check String
To check if a certain phrase or character is present in a string, we can use the keyword in.
📌Slicing

we can return a range of characters by using the slice syntax.
- Slice From the Start
- Slice To the End
- Negative Indexing
📌Modify Strings

Python has a set of built-in methods that we can use on strings.
- upper()
- lower()
- strip()
- replace()
📌String Concatenation

We can join two strings using the + operator
📌Format Strings

We can combine strings and numbers by using the format() method!

The format() method takes the passed arguments, formats them, and places them in the string where the placeholders {} are:
📌Additional Useful String Methods
- isalpha()
- isdigit()
- find()

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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 👇
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Aug 12, 2021
3 beginners level Machine Learning projects with code

- Regression
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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
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- Data requirements
- Data collection
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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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