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15 Aug, 5 tweets, 2 min read
3 remote Data Science and Machine Learning Internship opportunities which are open for all.

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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 πŸ‘‡
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 πŸ‘‡
omdena.com/projects/
3. iNeuron

A platform where you will explore, experiment, learn, participate and build a project based on an industry-defined approach.

Apply πŸ‘‡
internship.ineuron.ai
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More from @PiyalBanik

Piyal Banik Profile picture
17 Aug
#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 πŸ‘‡

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

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
Piyal Banik Profile picture
12 Aug
3 beginners level Machine Learning projects with code

- Regression
- Classification
- Clustering

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1. Regression
2. Classification
Read 5 tweets
Piyal Banik Profile picture
8 Aug
#DataScience Project 3

Best Suburb to Open a Cafeteria in Melbourne πŸ‡¦πŸ‡Ί

- 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 πŸ‘‡
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
Piyal Banik Profile picture
26 Jul
Data Science Pipeline

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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
Piyal Banik Profile picture
25 Jul
#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% accuracyπŸ”₯
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 πŸ‘‡
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
Piyal Banik Profile picture
22 Jul
Data Science Books πŸ“š you should start reading

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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…
2. Python for Data Analysis

This book deals with manipulating, processing, cleaning, and crunching data in Python. It is about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems.

learning.oreilly.com/library/view/p…
Read 11 tweets

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