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Piyal Banik Profile picture
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12 Aug, 5 tweets, 2 min read
3 beginners level Machine Learning projects with code

- Regression
- Classification
- Clustering

πŸ§΅πŸ‘‡
1. Regression
2. Classification
3. Clustering
That's it for this tread πŸ‘‹

A retweet for the first one would really mean a lot πŸ™

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More from @PiyalBanik

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

πŸ§΅πŸ‘‡
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

πŸ§΅πŸ‘‡
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
Piyal Banik Profile picture
18 Jul
"People need to know Maths to become Data Scientists or Machine Learning Engineer"

- True! πŸ˜€

But, how much do we need to know? πŸ€”β‰οΈ

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.
If I could convince you to learn a minimum of Linear Algebra for Machine Learning, it would be the followingπŸ‘‡:

- Systems of Linear Equations & Solving them
- Matrices
- Vector Spaces
- Linear Independence
- Basis & Rank
- Linear Mappings / Projections
Read 19 tweets
Piyal Banik Profile picture
11 Jul
Here are this week's Data Science Interview Questions along with the correct answer

Thread πŸ§΅πŸ‘‡

#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

Answer by @ammaryh92 & @arunkumarai

-regularization
-simpler model architecture
-more training data
-reduce noise in the data
-reduce the number of input attributes
-shorter training cycles

Read 7 tweets

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