abhisheks008 / ml-crate Goto Github PK

ML-Crate stands as the ultimate hub for a multitude of exciting ML projects, serving as the go-to resource haven for passionate and dedicated ML enthusiasts!🌟💫 Devfolio URL, https://devfolio.co/projects/mlcrate-98f9

Home Page: https://quine.sh/repo/abhisheks008-ML-Crate-409463050

License: MIT License

Jupyter Notebook 93.75% Python 0.03% HTML 6.22% CSS 0.01%

open-source machine-learning data-science python contributions-welcome swoc21 jwoc jwoc2k22 jwoc-2k22 opencode22

ml-crate's Introduction

ML-Crate 💻🧰

Website for ML-Crate Project Repo: Click Here!

🔴 Welcome contributors!

ML Crate stands as the ultimate hub for a multitude of exciting ML projects, serving as the go-to resource haven for passionate and dedicated ML enthusiasts!

Structure of the Projects 📝

This repository consists of various machine learning projects, and all of the projects must follow a certain template. I wish the contributors will take care of this while contributing in this repository.

Dataset - This folder stores the dataset used in this project. If the Dataset is not being able to uploaded in this folder due to the large size, then put a README.md file inside the Dataset folder and put the link of the collected dataset in it. That'll work!
Images - This folder is used to store the images generated during the data analysis, data visualization, data segmentation of the project.
Model - This folder would have your project file (that is .ipynb file) be it analysis or prediction. Other than project file, it should also have a 'README.md' using this template and 'requirements.txt' file which would be enclosed with all needed add-ons and libraries that are included in the project.
Web App - This folder consists of the web application built using the best model of the project folder. Majorly deployed in Flask or Streamlit.

Project Folder
|- Dataset
   |- dataset.csv (dataset used for the particula project)
   |- README.md (brief about the dataset)
|- Images
   |- img1.png
   |- img2.png
   |- img3.png
|- Model
   |- project_folder.ipynb
   |- README.md
|- Web App
   |- templates
   |- static
   |- app.py
   |- demo.mp4
   |- README.md
|- requirements.txt

🧮 Workflow

Fork the repository
Clone your forked repository using terminal or gitbash.
Make changes to the cloned repository
Add, Commit and Push
Then in Github, in your cloned repository find the option to make a pull request

🥳 Open Source Programs!

_{SWOC 2021}

_{JWOC 2022}

_{OpenCode 2022}

_{PSOC 2022}

_{KWOC 2022}

_{DWOC 2023}

_{KWOC 2023-24}

_{JWOC 2024}

_{IWOC 2.0}

_{SSOC 24}

🏆 Achievements of this Project Repo 🎉

1️⃣ Recognized as the "🥇 TOP PROJECT" for SWOC 2.0 for the year 2021-22. (49 Pull Requestes have been merged!)
2️⃣ Recognized as the "TOP MENTOR" and "TOP PA" for the project 'ML-Crate' in SWOC 2.0.
3️⃣ Recognized as the "🥇 BEST MENTOR" of JGEC Winter of Code 2022, for mentoring students to contribute in the project repo "ML-Crate".
4️⃣ Recognized as the "🥇 BEST MENTOR" of CSI RAIT OpenCode Open Source Program 2022, for mentoring students to contribute in the project repo "ML-Crate".
5️⃣ Recognized as the "🥇 TOP PROJECT ADMIN" of Hack Club RAIT Summer of Code 2022, for mentoring students to contribute in the project repo "ML-Crate".
6️⃣ Special Mention from JWOC Team ✍️ : Abhishek is one of the most skillful and talented open source developers I have come across at JGEC Winter of Code 2K22. He exhibited commendable performance as a Project Admin and Mentor throughout the event. It was a pleasure to see him interact with and guide budding student developers to put forward their first steps towards open source contribution. The numerous mentorship sessions he facilitated for the participants reflect his domain expertise and technical proficiency. Abhishek is an inspiration for many young engineers who wish to build a career in Machine Learning and Data Science. His project ML-Crate was an essential gateway for ML enthusiasts to break the ice and start contributing.
On behalf of the entire Organizing Team of JWOC 2K22, I highly appreciate Abhishek's efforts to foster the spirit of community bonding and cooperation among the aspiring developers. His imaginative prowess and enthusiasm will be a great asset to any organization.

✨Top Contributors

Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀

✔Project Admin

_{Abhishek Sharma}

⭐Give this Project a Star

If you liked working on this project, do ⭐ and share this repository.

🎉 🎊 😃 Happy Contributing 😃 🎊 🎉

📬 Contact

If you want to contact me, you can reach me through social handles.

ml-crate's People

Contributors

Stargazers

Watchers

Forkers

vaibhavgupta2499 jeetsarkar19 pradyyadav sanyamgoel10 vaidehisinha1 rajnish-488 ashish-reddy-20-08 aslmanasa sankalp-srivastava karishni rahul-netizen shivangisehgal deepthi1107 anshkaushik05 divya-gupta12 venkatakrishnan-ramesh anshyyy shreyapy krishna-nit vanshikagoel0012 codehackerone awan2002 vibhutijindal rik-21 nishtha2405 adi9235 mithil-sri-sai vishnubhaarath mrunal736 snega16 godofgeeks23 suryageeks guptapriya-83900 jahnavibattu02 srini047 aryashahi aryacodez atigpurohit aryasoni98 das-ankur tihsrah debjitpal5040 suvodeep-das soumyonathtripathy scorpionmfk27 aka9ksha ahananupam33 kuwarkapur lahariboni prathimacode-hub techthiyanes piyushbl45t vikas-kmr1 nirvik07 surjyaneeh ilhamansari juandasuarez asttttha parker-x a-biao96 curriee11 itz-ankur sugam-mahajan eshani-r-sawant sem-5-mini-project-delicious-recipes skartikc tabishmomin aryanag7 karanshelke shubhammore45 gokul-gopalakrishnan khansumaiyas diksha2602 ayaankhatib heetraichura12 suyogsm imrajans dhruvkarotra var69 snehal1024 raghavvvvv prasad669 nupur2471 deva2412 saurabhbangar0 khatibnaqi ruchit1111 guneshm11 072arushi jasontodd101 thadurivamshi bhavani-monu han9128 p1utoze ssheersh dhruv-kapri pranjalrkgp thedarkparalda naveensani sanchitgarg2204

ml-crate's Issues

Titanic Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Titanic Prediction
🔴 Aim : Build a fun model to predict whether a person would have survived on the Titanic or not. You can use linear regression for this purpose.
🔴 Dataset : https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/problem12.html
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Pima Indians Diabetes Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Pima Indians Diabetes Prediction
🔴 Aim : To predict whether a person is diabetic or not.
🔴 Dataset : https://www.kaggle.com/uciml/pima-indians-diabetes-database
🔴 Approach : Full model building process to be explained from Exploratory data analysis to evaluation metrics.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

MS COCO dataset

ML-Crate Repository (Proposing new issue)

🔴 Project Title : MS COCO dataset
🔴 Aim : TTo detect objects from the image and then generate captions for them. LSTM (Long short term memory) network is responsible for generating sentences in English and CNN is used to extract features from image. To build a caption generator we have to combine these two models.
🔴 Dataset : https://www.kaggle.com/awsaf49/coco-2017-dataset
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

ImageNet dataset

ML-Crate Repository (Proposing new issue)

🔴 Project Title : ImageNet dataset
🔴 Aim : To implement image classification on this huge database and recognise objects. CNN model (Convolutional neural networks) are necessary for this project to get accurate results.
🔴 Dataset : https://www.kaggle.com/c/imagenet-object-localization-challenge
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Sentiment Analysis on Twitter Data

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Sentiment Analysis on Twitter Data
🔴 Aim : Analysing the sentiment of the users and creating a prediction model based on the data, which will predict the sentiment of the user.
🔴 Dataset : https://www.kaggle.com/arkhoshghalb/twitter-sentiment-analysis-hatred-speech
🔴 Approach :Try to implement more than three machine learning algorithms and make a comparison between them, then conclude about the best fit algorithm to create the model. All the algorithms must be checked through the accuracy scores to determine the best fitted model

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Next Word Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Next Word Prediction
🔴 Aim : To predict the next word according to the data present and collected in our database.
🔴 Dataset : https://drive.google.com/file/d/1GeUzNVqiixXHnTl8oNiQ2W3CynX_lsu2/view
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Breast Cancer Wisconsin (Diagnostic)

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Breast Cancer Wisconsin (Diagnostic)
🔴 Aim : To Determine which features of data (measurements) are most important for diagnosing breast cancer and find out if breast cancer occurs or not.
🔴 Dataset : https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Product Recommendation System

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Product Recommendation System
🔴 Aim : Build a product recommendation system like Amazon. A recommendation system can suggest you products, movies, etc based on your interests and the things you like and have used earlier.
🔴 Dataset : https://cseweb.ucsd.edu/~jmcauley/datasets.html
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Students Performance in Exams

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Students Performance in Exams
🔴 Aim : To understand the influence of the parents background, test preparation etc on students performance. Perform EDA.
🔴 Dataset : https://www.kaggle.com/spscientist/students-performance-in-exams
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Fork the Repo 🍴

Fork this main repository before contributing to it. Without forking the repo don't create the pull request.

Fraud Email Detection

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Fraud Email Detection
🔴 Aim : Build a model to detect fraudulent activities.
🔴 Dataset : https://www.cs.cmu.edu/~enron/
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Years of experience and Salary dataset

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Years of experience and Salary dataset
🔴 Aim : The purpose of this project is to use data transformation and machine learning to create a model that will predict a salary when given years of experience, job type.
🔴 Dataset : https://www.kaggle.com/rohankayan/years-of-experience-and-salary-dataset
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Enron Email Dataset

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Enron Email Dataset
🔴 Aim : To detect fraudulent activities and perform EDA.
🔴 Dataset : https://www.kaggle.com/wcukierski/enron-email-dataset
🔴 Approach : ry to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Credit Card Fraud Detection

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Credit Card Fraud Detection
🔴 Aim : To identify suspicious events and report them to an analyst while letting normal transactions be automatically processed
🔴 Dataset : https://www.kaggle.com/mlg-ulb/creditcardfraud
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Heart Disease Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Heart Disease Prediction
🔴 Aim : Use this dataset to predict which patients are most likely to suffer from a heart disease in the near future using the features given.
🔴 Dataset : https://www.kaggle.com/rishidamarla/heart-disease-prediction
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Jeopardy Bot

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Jeopardy Bot
🔴 Aim : We Build a question answering system and implement in a bot that can play the game of jeopardy with users. The bot can be used on any platform like Telegram, discord, reddit, etc.
🔴 Dataset : https://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

House

ML-Crate Repository (Proposing new issue)

🔴 Project Title #7
🔴 Aim :
🔴 Dataset :
🔴 Approach :

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name : Vanshika goel
GitHub Profile Link :https://github.com/Vanshikagoel0012
Participant ID :1093
Approach for this Project #7
Are you a participant of SWOC 2.0?
YES

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

IMDB Review Analysis

ML-Crate Repository (Proposing new issue)

🔴 Project Title : IMDB Review Analysis
🔴 Aim : Perform Sentiment analysis on the data to see the statistics of what type of movie do users like. Sentiment analysis is the process of analysing the textual data and identifying the emotion of the user, Positive or Negative.
🔴 Dataset : http://ai.stanford.edu/~amaas/data/sentiment/
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Mall Customers Segmentation

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Mall Customers Segmentation
🔴 Aim : To classify different customers.
🔴 Dataset : https://www.kaggle.com/shwetabh123/mall-customers
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Spam Email Detection

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Spam Email Detection
🔴 Aim : You can build a model that can identify your emails as spam or non-spam.
🔴 Dataset : https://archive.ics.uci.edu/ml/datasets/Spambase
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Height and Weight Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Height and Weight Prediction
🔴 Aim : Build a predictive model for determining height or weight of a person. Implement a linear regression model that will be used for predicting height or weight.
🔴 Dataset : http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_Dinov_020108_HeightsWeights
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Number Plate Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Number Plate Prediction
🔴 Aim : To get the number plate of the vehicle which is on a quiet high speed and breaking the traffic.
🔴 Dataset :https://www.kaggle.com/dataturks/vehicle-number-plate-detection/download
🔴 Approach :Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Vehicle Insurance Claim Fraud Detection

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Vehicle Insurance Claim Fraud Detection
🔴 Aim : Vehicle insurance fraud involves conspiring to make false or exaggerated claims involving property damage or personal injuries following an accident so, It will Detect fraud claims and will help Insurance Firms to verify them properly again.
🔴 Dataset : https://www.kaggle.com/shivamb/vehicle-claim-fraud-detection
🔴 Approach : Starting with cleaning and EDA I'll be going with Classification & SGD Classifier and will try to finalize the one with the highest accuracy

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name : Shrikrushna Bhagwat
GitHub Profile Link : https://github.com/krishna-NIT
Participant ID : 930
Approach for this Project : Starting with cleaning and EDA I'll be going with Classification & SGD Classifier and will try to finalize the one with the highest accuracy
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

IPL Winning Match Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : IPL Winning Match Prediction
🔴 Aim : Predict the winning Team made by a different player with different bowlers, batsmen, and captains. Will be finalizing the best method to be used on behalf of accuracy. Predicting some outcomes of upcoming matches
🔴 Dataset : https://bit.ly/34SRn3b
🔴 Approach : Initially cleaning & EDA with ungrouping players from Team to individual players and then applying various techniques to find the best accuracy

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name : Shrikrushna Bhagwat
GitHub Profile Link : https://github.com/krishna-nit
Participant ID : 930
Approach for this Project : Initially cleaning & EDA with ungrouping players from Team to individual players and then applying various techniques to find the best accuracy
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Cityscapes Dataset

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Cityscapes Dataset
🔴 Aim : To perform image segmentation and detect different objects from a video on the road. Image segmentation is the process of digitally partitioning an image into various different categories like cars, buses, people, trees, roads, etc.
🔴 Dataset : https://www.kaggle.com/dansbecker/cityscapes-image-pairs
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Object 365 Dataset

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Object 365 Dataset
🔴 Aim : To classify images captured from the camera and detect objects present in the image. Object detection deals with recognizing which object is present in the image along with the coordinates of the object.
🔴 Dataset : https://www.kaggle.com/c/open-images-2019-object-detection/discussion/94334
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

MNIST Dataset Classification

ML-Crate Repository (Proposing new issue)

🔴 Project Title : MNIST Dataset Classification
🔴 Aim : Implement a machine learning classification algorithm on image to recognize handwritten digits from a paper.
🔴 Dataset : http://yann.lecun.com/exdb/mnist/
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Stress Detection

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Stress Detection
🔴 Aim : Stress detection is a challenging task, as there are so many words that can be used by people on their posts that can show whether a person is having psychological stress or not. So we would find if the person is in stress or not .
🔴 Dataset : https://www.kaggle.com/ruchi798/stress-analysis-in-social-media
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Uber Pickup Analysis

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Uber Pickup Analysis
🔴 Aim : To analyze the data of the customer rides and visualize the data to find insights that can help improve business. Data analysis and visualization is an important part of data science. They are used to gather insights from the data and with visualization you can get quick information from the data.
🔴 Dataset : https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Sonar Dataset

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Sonar Dataset
🔴 Aim : To predict whether or not an object is a mine or a rock given the strength of sonar returns at different angles.
🔴 Dataset : https://www.kaggle.com/ypzhangsam/sonaralldata
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Photo sketching dataset

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Photo sketching dataset
🔴 Aim : To build a model that can develop sketches automatically from the images. This will take an image as an input and generate a sketch image using computer vision techniques.
🔴 Dataset : https://www.kaggle.com/wanghaohan/imagenetsketch
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Restaurant Review Classification

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Restaurant Review Classification
🔴 Aim : You can build a model which can detect whether a restaurant’s review is fake or real. With text processing and additional features in dataset you can build a SVM model that can classify reviews as fake or real.
🔴 Dataset : https://www.yelp.com/dataset
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Netflix Movies and TV Shows

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Netflix Movies and TV Shows
🔴 Aim : To understand what content is available in different countries ,Identify similar content by matching text-based features ,Network analysis of Actors / Directors and find interesting insights ,Does Netflix has more focus on TV Shows than movies in recent years?. Perform EDA.
🔴 Dataset : https://www.kaggle.com/shivamb/netflix-shows
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Parkinson's Disease Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Parkinson's Disease Prediction
🔴 Aim : The model can be used to differentiate healthy people from people having Parkinson’s disease. The algorithm that is useful for this purpose is XGboost which stands for extreme gradient boosting, it is based on decision trees.
🔴 Dataset : https://archive.ics.uci.edu/ml/datasets/parkinsons
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

GTSRB (German traffic sign recognition benchmark) Dataset

ML-Crate Repository (Proposing new issue)

🔴 Project Title : GTSRB (German traffic sign recognition benchmark) Dataset
🔴 Aim : To build a model using a deep learning framework that classifies traffic signs and also recognises the bounding box of signs. The traffic sign classification is also useful in autonomous vehicles for identifying signs and then take appropriate actions.
🔴 Dataset : https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Iris Classification

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Iris Classification
🔴 Aim : Implement a machine learning classification or regression model on the dataset. Classification is the task of separating items into its corresponding class.
🔴 Dataset : https://archive.ics.uci.edu/ml/datasets/Iris
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Fake News Detection

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Fake News Detection
🔴 Aim : Build a fake news detection model with Passive Aggressive Classifier algorithm. The Passive Aggressive algorithm can classify massive streams of data, it can be implemented quickly.
🔴 Dataset : https://www.kaggle.com/c/fake-news/data
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Swedish Auto Insurance

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Swedish Auto Insurance Dataset.
🔴 Aim : To predict the total payment for all claims in thousands of Swedish Kronor, given the total number of claims. and perform Eda.
🔴 Dataset : https://www.kaggle.com/sunmarkil/auto-insurance-in-sweden-small-dataset
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Character Recognition

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Character Recognition
🔴 Aim : Implement character recognition in natural languages. Character recognition is the process of automatically identifying characters from written papers or printed texts.
🔴 Dataset : http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Intention of Chatbots

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Intention of Chatbots
🔴 Aim : Tweak and expand the data with your observations to build and understand the working of a chatbot in organizations. A chatbot requires you to understand Natural language processing concepts.
🔴 Dataset : https://github.com/katanaml/katana-assistant/blob/master/mlbackend/intents.json
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Star the repository ⭐

Consider starring this repository if you found this project helpful. And also share the repository with other contributors so that they can also get to know about it!

Banknote Dataset

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Banknote Dataset
🔴 Aim : To predict whether a given banknote is authentic given a number of measures taken from a photograph.
🔴 Dataset : https://www.kaggle.com/ritesaluja/bank-note-authentication-uci-data
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Caption Generation from Images

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Caption Generation from Images
🔴 Aim : Detect objects from the image and then generate captions for them. LSTM (Long short term memory) network is responsible for generating sentences in English and CNN is used to extract features from image. To build a caption generator we have to combine these two models.
🔴 Dataset : http://cocodataset.org/#home
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Crypto Currency Price Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title :Crypto Currency Price Prediction
🔴 Aim :Buying and selling result in a change in the price of any cryptocurrency, but buying and selling trends depend on many factors. Using machine learning for cryptocurrency price prediction can only work in situations where prices change due to historical prices that people see before buying and selling their cryptocurrency. So we need to find the price relation here.
🔴 Dataset :https://www.kaggle.com/sudalairajkumar/cryptocurrencypricehistory
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Contribute to this repository 💡

Contribute to this respository to shower your love towards this project and getting started with Open Source/Github.

Avocado Prices

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Avocado Prices
🔴 Aim : The goal is to predict the average price which is continuous in nature of the different type of avocado and using the region that in which region they are lying.
🔴 Dataset : https://www.kaggle.com/neuromusic/avocado-prices/code
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

House Price Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : House Price Prediction
🔴 Aim : Predict the housing prices of a new house using linear regression. Linear regression is used to predict values of unknown input when the data has some linear relationship between input and output variables.
🔴 Dataset : https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Credit Card Fraud Detection

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Credit Card Fraud Detection
🔴 Aim : Implement different algorithms like decision trees, logistic regression, and artificial neural networks to see which gives better accuracy. Compare the results of each algorithm and understand the behavior of models.
🔴 Dataset : https://www.kaggle.com/mlg-ulb/creditcardfraud
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Heart Disease Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Heart Disease Prediction
🔴 Aim : Heart disease can be predicted based on various symptoms such as age, gender, heart rate, etc. and reduces the death rate of heart patients.
🔴 Dataset :https://raw.githubusercontent.com/amankharwal/Website-data/master/heart.csv
🔴 Approach :Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Wine Quality Prediction

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Wine Quality Prediction
🔴 Aim : Perform various different machine learning algorithms like regression, decision tree, random forests, etc and differentiate between the models and analyse their performances.
🔴 Dataset : https://archive.ics.uci.edu/ml/datasets/wine+quality
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

📍 Follow the Guidelines to Contribute in the Project :

You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
- Images - To store the required images.
- Dataset - To store the dataset or, information/source about the dataset.
- Model - To store the machine learning model you've created using the dataset.
- requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

🔴🟡 Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'SWOC' contributors of 'ML-Crate' project.

✅ To be Mentioned while taking the issue :

Full name :
GitHub Profile Link :
Participant ID :
Approach for this Project :
Are you a participant of SWOC 2.0?
- YES
- No

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎