As we all know the BGMI Loot Crate comes with so many resources for the gamers, this ML Crate will be the hub of various ML projects which will be the resources for the ML enthusiasts! Open Source Program: SWOC 2021 and JWOC 2022.

Abhishek Sharma

Last update: Dec 28, 2022

Related tags

Machine Learning python open-source data-science machine-learning opencode contributions-welcome jwoc swoc swoc2021 swoc21 jwoc2k22 jwoc-2k22 opencode22

Overview

Machine Learning Loot Crate 💻 🧰

🔴 Welcome contributors!

As we all know the BGMI Loot Crate comes with so many resources for the gamers, this ML Crate will be the hub of various ML projects which will be the resources for the 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.

🧮 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!

_{Script Winter of Code 2021}
_{PR Count:
4️⃣

9️⃣}

_{JGEC Winter of Code 2022}
_{PR Count:
3️⃣

3️⃣}

_{OpenCode CSI RAIT 2022}
_{PR Count
0️⃣

0️⃣}

🏆 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".

🗒️ Content List

Serial No.	Project Name	Goal of the Project	Link
01	Credit Card Fraud Detection	The main aim of the project is to make a model that helps to predict credit card fraud based on the given dataset.	Click Here
02	MNIST Dataset Classification	Implement a machine learning classification algorithm on image to recognize handwritten digits from a paper.	Click Here
03	Character Recognition	Implement character recognition in natural languages. Character recognition is the process of automatically identifying characters from written papers or printed texts.	Click Here
04	Height and Weight Prediction	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.	Click Here
05	Fake News Detection	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.	Click Here
06	Spam Email Detection	Build a model that can identify your emails as spam or non-spam.	Click Here
07	Wine Quality Prediction	Perform various different machine learning algorithms like regression, decision tree, random forests, etc and differentiate between the models and analyse their performances.	Click Here
08	Iris Classification	Implement a machine learning classification or regression model on the dataset. Classification is the task of separating items into its corresponding class.	Click Here
09	Titanic Prediction	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.	Click Here
10	Pima Indians Diabetes Prediction	To predict whether a person is diabetic or not.	Click Here
11	Parkinson's Disease Prediction	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.	Click Here
12	Sentiment Analysis on Twitter Data	Analysing the sentiment of the users and creating a prediction model based on the data, which will predict the sentiment of the user..	Click Here
13	Jeopardy Bot	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.	Click Here
14	Breast Cancer Wisconsin (Diagnostic)	To Determine which features of data (measurements) are most important for diagnosing breast cancer and find out if breast cancer occurs or not.	Click Here
15	Restaurant Review Classification	To 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.	Click Here
16	Caption generation from images	To 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.	Click Here
17	Heart Disease Prediction	Use this dataset to predict which patients are most likely to suffer from a heart disease in the near future using the features given.	Click Here
18	Years of experience and Salary dataset	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.	Click Here
19	Banknote Dataset	To predict whether a given banknote is authentic given a number of measures taken from a photograph.	Click Here
20	GTSRB (German traffic sign recognition benchmark) Dataset	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.	Click Here
21	Students Performance in Exams	To understand the influence of the parents background, test preparation etc on students performance. Perform EDA.	Click Here
22	Swedish Auto Insurance	To predict the total payment for all claims in thousands of Swedish Kronor, given the total number of claims. and perform Eda.	Click Here
23	Avocado Prices	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.	Click Here
24	IPL Winning Match Predictor	The goal is to 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.	Click Here
25	Uber Analysis	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.	Click Here
26	Crypto Currency Price Prediction	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.	Click Here
27	House Price Prediction	The goal is to predict 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.	Click Here
28	Vehicle Insurance Claim Fraud Detection	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.	Click Here
29	Mall Customers Segmentation	To classify different customers.	Click Here
30	Body Fat Prediction	Create a ML model, for predicting the body fat.	Click Here
31	Big Mart Sales Prediction	Create a Prediction Model, for the sales prediction of Big Mart	Click Here
32	Air Quality Prediction	Prediction model to predict the air quality	Click Here
33	Stress Detection	Detect the stress among different people	Click Here
34	Bitcoin Price Prediction	Predicting the price of Bitcoin using a ML approach	Click Here
35	UK Favourite Chocolate Analysis	Anlyze the dataset which contains different aspects of chocolates of UK	Click Here
36	Advertisement Click Prediction	Predict the clicking on the advertisement	Click Here
37	FLICKR8k Dataset Analysis (MS COCO)	Analyze the dataset of MS COCO and provide the visualization out of it	Click Here
38	US Household Income Distribution Analysis	Analyze different aspects of US household from the given dataset and find out the pattern among them.	Click Here
39	Engineering Placements Prediction	Predict the placements of the engineering students after being graduated from any engineering college/university	Click Here
40	Digit Recognizer Project	Recognize the digits using a Machine Learning Model, where the digits are in the handwritten form.	Click Here
41	Concrete Strength Calculation	Create a ML model which will calculate the strength of concrete and provides the outcome	Click Here
42	Ethereum Fraud Detection	Create a ML model which detect the real/fake eth while purchasing/selling it.	Click Here
43	Indian School Education Statistics	Visualize and analyze the condition of the Indian school education system with the help of data analysis	Click Here
44	Real/Fake Job Posting Prediction	Create a ML model which will predict the real/fake job postings in different websites	Click Here
45	Eye Disease Prediction	Create a prediction model which will predict the affected eye from the given images	Click Here
46	Birds Image Classification	Classify the images of the birds using deep learning methods	Click Here
47	Identify the images of Cats and dogs	Identifying the images of cats and dogs.Algorithm used for this purpose was CNN.	Click Here
48	IMDB Review Analysis	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.	Click Here
49	Enron Email Dataset	Classify the emails from the given dataset and visualize the contents of the email	Click Here
50	Netflix Movies and Shows Analysis	Analyze the shows on the Netflix platform and find out the visualization of the data in different aspects.	Click Here
51	Bangladesh Premier League Analysis	Analyze different aspects of Bangladesh Premier League for the season 2021-22.	Click Here
52	Top Programming language in GitHub	Identify the top programming language in GitHub using data analysis	Click Here
53	Entrepreneurial Capacity in Student	Create a ML model which will be identifying the entrepreneurial capacity in student.	Click Here
54	NYPD Shooting Data Analysis	Analyze and visualize the shooting data registered by NYPD.	Click Here
55	Sonar Dataset Analysis	Analyze the Sonar dataset in different perspectives and visualize different patterns among them.	Click Here
56	Resume Classification	Classify the resumes and identify the useful ones for the company.	Click Here
57	Legends of League Analysis	Analyze the aspects of League of Legends.	Click Here
58	Hand Pose Detection	Detect the hand poses from the camera input.	Click Here
59	Classify the Emoji	Classify the emoji using deep learning techniques.	Click Here
60	Rihanna Lyrics Analysis	Analyze the contents of the albums of Rihanna based on the lyrics of the songs produced.	Click Here
61	Face Mask Detection	Detect the mask on the faces of the people with an ML approach.	Click Here
62	Billboard "The Hot 100" Songs	Analyze different aspects of the song genre and identify different features among them.	Click Here
63	Vegetable Classification and Recognition	Classify the images of the vegetables and recognize the images using machine learning models.	Click Here
64	Brain Tumor Detection	Detect and identify the brain tumors images from the dataset provided using a ML approach.	Click Here
65	Quora Insincere Questions Classification Analysis & Prediction	Identify the miss-informations in the website of Quora and classify them using a ML approach.	Click Here
66	Amazon Alexa Reviews	Analyse the reviews of the various products of the Amazon website.	Click Here
67	Body Parts Classification	Classify different body parts using a ML approach.	Click Here
68	Women's E-commerce Clothings Reviews	Analyse the reviews of the women's e-commerce clothings in different platforms.	Click Here
69	FIFA 19 Dataset Analysis	Analyze the dataset of the FIFA 19 football dataset and visualize the different factors of it.	Click Here
70	Pneumonia Disease Prediction	Create a prediction model which will predict the disease from the user input.	Click Here
71	Data Analytics Salary Prediction	Create a prediction model which will predict the salary of the Data Analytics and visualize them.	Click Here
72	Udacity Course Analysis	Analyze different aspects of the Udacity courses depending on various situations.	Click Here
73	Rice Type Classification	Classify the types of the rice using a ML approach.	Click Here
74	Named Entity Recognition (NER) Corpus	Analyze and create a model using machine learning approach for NER dataset.	Click Here
75	Lumpy Skin Disease Prediction	Predict the lumpy skin disease using machine learning approach.	Click Here
76	Amazon Books Analysis	Analyze the books of the e-commerce platform Amazon using a ML approach.	Click Here
77	Pets Images Classification	Classify the images of different pets and then create a ML model based on these.	Click Here
78	Number Plate Prediction	An OpenCV approach for predicting the correct number plate among the duplicate ones.	Click Here
79	GATE Examination Analysis	A machine learning approach for classification of different aspects of GATE examinations.	Click Here
80	Crime Analysis of India	Analyze different aspects of India regarding the crime using ML approach.	Click Here
81	Confused student EEG brainwave data	Analyze different aspects of the EEG Brainwave data and create a deep learning model in order to predict the scenario.	Click Here
82	CS:GO Round Winner Clasification	Classify the winners of the CS:GO video game using a ML approach.	Click Here
83	Predicting Pulsar Star	Create a ML model which will predict the timings of Pulsar star.	Click Here

Leaderboard 📊

_{SWOC Leaderboard 2.0}

_{JWOC Leaderboard 2022}

✨ 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.

Comments

Engineering Graduate Salary Prediction
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Engineering Graduate Salary Prediction :red_circle: Aim : Predict The Salary of An Indian Engineering Graduate :red_circle: Dataset : https://www.kaggle.com/manishkc06/engineering-graduate-salary-prediction :red_circle: 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 and JWOC '22 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.

:red_circle::yellow_circle: 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' and 'JWOC' contributors of 'ML-Crate' project.

:white_check_mark: To be Mentioned while taking the issue :

Full name :

GitHub Profile Link :

Participant ID :

Approach for this Project :

What is your participant role?

[ ] SWOC 2.0 Participant.

[ ] JWOC 2022 Participant.

[ ] Contributor

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎
Assigned :computer: Contributors
opened by abhisheks008 24
Electricity Consumption Forecasting
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Electricity Consumption Forecasting :red_circle: Aim : This project will help us in forecasting the electricity consumption from the provided dataset. :red_circle: Dataset: https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption :red_circle: 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.

📍 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.

:red_circle::yellow_circle: 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' and 'JWOC' contributors of 'ML-Crate' project.

:white_check_mark: To be Mentioned while taking the issue :

Full name :

GitHub Profile Link :

Participant ID (If not, then put NA) :

Approach for this Project :

What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎
Assigned :computer: Contributors
opened by abhisheks008 17
FIDE Chess Ranking Analysis
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : FIDE Chess Ranking Analysis :red_circle: Aim : Analyze the dataset with different parameters. :red_circle: Dataset : https://www.kaggle.com/datasets/surajjha101/fide-chess-rankings-updated :red_circle: 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.

📍 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.

:red_circle::yellow_circle: 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' and 'JWOC' contributors of 'ML-Crate' project.

:white_check_mark: To be Mentioned while taking the issue :

Full name :

GitHub Profile Link :

Participant ID (If not, then put NA) :

Approach for this Project :

What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎
Assigned :computer: HSOC22-Level-2
opened by abhisheks008 17
Heart Failure Prediction
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Heart Failure Prediction :red_circle: Aim : Create a prediction model for heart failure of the patients. :red_circle: Dataset : https://www.kaggle.com/datasets/asgharalikhan/mortality-rate-heart-patient-pakistan-hospital :red_circle: 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.

📍 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.

:red_circle::yellow_circle: 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' and 'JWOC' contributors of 'ML-Crate' project.

:white_check_mark: To be Mentioned while taking the issue :

Full name :

GitHub Profile Link :

Participant ID (If not, then put NA) :

Approach for this Project :

What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎
Assigned :computer: Contributors
opened by abhisheks008 16
MS COCO dataset
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : MS COCO dataset :red_circle: 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. :red_circle: Dataset : https://www.kaggle.com/awsaf49/coco-2017-dataset :red_circle: 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

:red_circle::yellow_circle: 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.

:white_check_mark: 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. 😎
Assigned :computer:
opened by ASLManasa 15
House Price Prediction
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : House Price Prediction :red_circle: 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. :red_circle: Dataset : https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html :red_circle: 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

:red_circle::yellow_circle: 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.

:white_check_mark: 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. 😎
Assigned :computer:
opened by abhisheks008 15
Uber Pickup Analysis
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Uber Pickup Analysis :red_circle: 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. :red_circle: Dataset : https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city :red_circle: 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

:red_circle::yellow_circle: 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.

:white_check_mark: 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. 😎
Assigned :computer:
opened by abhisheks008 14
Top Cryptocurrency Analysis and Prediction
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Top Cryptocurrency Analysis and Prediction :red_circle: Aim : This project will analyze the data and identify the top crypto of 2022. :red_circle: Dataset : https://www.kaggle.com/majyhain/top-100-cryptocurrency-2022 :red_circle: 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 and JWOC '22 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.

:red_circle::yellow_circle: 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' and 'JWOC' contributors of 'ML-Crate' project.

:white_check_mark: To be Mentioned while taking the issue :

Full name :

GitHub Profile Link :

Participant ID :

Approach for this Project :

What is your participant role?

[ ] SWOC 2.0 Participant.

[ ] JWOC 2022 Participant.

[ ] Contributor

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎
Assigned :computer: HSOC22-Level-3
opened by abhisheks008 13
Mall Customers Segmentation
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Mall Customers Segmentation :red_circle: Aim : To classify different customers. :red_circle: Dataset : https://www.kaggle.com/shwetabh123/mall-customers :red_circle: 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

:red_circle::yellow_circle: 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.

:white_check_mark: 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. 😎
Assigned :computer:
opened by abhisheks008 13
Disease Prediction
ML-Crate Repository (Proposing new issue)

:red_circle: Disease prediction model with GUI (Machine Learning) : :red_circle: predicting diseases from the symptoms provided by the user : :red_circle: Approach : Using - Random Forest, descision tree and naive bayes.

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.

📍 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.

:red_circle::yellow_circle: 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' and 'JWOC' contributors of 'ML-Crate' project.

:white_check_mark: To be Mentioned while taking the issue :

Full name :

GitHub Profile Link :

Participant ID (If not, then put NA) :

Approach for this Project :

What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎
Assigned :computer: HSOC22-Level-3
opened by vickyrules 12
NIFTY Analysis (Jan 22)
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : NIFTY Analysis (Jan 22) :red_circle: Aim : Analyze the NIFTY Indices of the stock market for the time period of Jan 22. :red_circle: Dataset : https://www.kaggle.com/atrisaxena/nifty-indices-data :red_circle: 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 and JWOC '22 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.

:red_circle::yellow_circle: 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' and 'JWOC' contributors of 'ML-Crate' project.

:white_check_mark: To be Mentioned while taking the issue :

Full name :

GitHub Profile Link :

Participant ID :

Approach for this Project :

What is your participant role?

[ ] SWOC 2.0 Participant.

[ ] JWOC 2022 Participant.

[ ] Contributor

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎
Assigned :computer: HSOC22-Level-2
opened by abhisheks008 12
Alpaca Identification
Pull Request for ML-Crate 💡

Issue Title: Alpaca Identification

Info about the related issue (Aim of the project) : To identify whether the given image is of an Alpaca or not

Name: Evan Joshy Chittilappilly

GitHub ID: TheDarkParalda

Idenitfy yourself: Participant

Closes: #93

Describe the add-ons or changes you've made 📃

I added a Rescaling Layer to the VGGCNN Model which increased the validation accuracy to 90.77%

Type of change ☑️

What sort of change have you made:

[x] Breaking change (fix or feature that would cause existing functionality to not work as expected)

How Has This Been Tested? ⚙️

It was tested on 20 percent of the given data. The accuracy was checked and loss was calculated using binary cross entropy loss.

Checklist: ☑️

[x] My code follows the guidelines of this project.

[x] I have performed a self-review of my own code.

[x] I have commented my code, particularly wherever it was hard to understand.

[x] I have made corresponding changes to the documentation.

[x] My changes generate no new warnings.

[x] I have added things that prove my fix is effective or that my feature works.

[x] Any dependent changes have been merged and published in downstream modules.
opened by TheDarkParalda 1
Arabic handwritten dataset
Pull Request for ML-Crate 💡

Issue Title:

Info about the related issue (Aim of the project) :

Name:

GitHub ID:

Idenitfy yourself:

Closes: #issue number that will be closed through this PR

Describe the add-ons or changes you've made 📃

Give a clear description of what have you added or modifications made

Type of change ☑️

What sort of change have you made:

[x] Bug fix (non-breaking change which fixes an issue)

[x] New feature (non-breaking change which adds functionality)

[x] Code style update (formatting, local variables)

[x] Breaking change (fix or feature that would cause existing functionality to not work as expected)

[x] This change requires a documentation update

How Has This Been Tested? ⚙️

Describe how it has been tested Describe how have you verified the changes made

Checklist: ☑️

[x] My code follows the guidelines of this project.

[x] I have performed a self-review of my own code.

[x] I have commented my code, particularly wherever it was hard to understand.

[x] I have made corresponding changes to the documentation.

[x] My changes generate no new warnings.

[x] I have added things that prove my fix is effective or that my feature works.

[x] Any dependent changes have been merged and published in downstream modules.
opened by Han9128 2
Stanford Cars Analysis
Pull Request for ML-Crate 💡

Issue Title:

Info about the related issue (Aim of the project) :

Name:

GitHub ID:

Idenitfy yourself:

Closes: #issue number that will be closed through this PR

Describe the add-ons or changes you've made 📃

Give a clear description of what have you added or modifications made

Type of change ☑️

What sort of change have you made: Removed the dataset files and created readme file

[ ] Bug fix (non-breaking change which fixes an issue)

[ ] New feature (non-breaking change which adds functionality)

[ ] Code style update (formatting, local variables)

[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)

[ ] This change requires a documentation update

How Has This Been Tested? ⚙️

Describe how it has been tested Describe how have you verified the changes made

Checklist: ☑️

[ ] My code follows the guidelines of this project.

[ ] I have performed a self-review of my own code.

[ ] I have commented my code, particularly wherever it was hard to understand.

[ ] I have made corresponding changes to the documentation.

[ ] My changes generate no new warnings.

[ ] I have added things that prove my fix is effective or that my feature works.

[ ] Any dependent changes have been merged and published in downstream modules.

Requested Changes :gear:
opened by rising-star2712 1
Top Animation Movies and TV Shows Analysis
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Top Animation Movies and TV Shows Analysis :red_circle: Aim : Create a data analysis project which will analyze the data from the given dataset. :red_circle: Dataset : https://www.kaggle.com/datasets/faisaljanjua0555/top-50-animation-movies-and-tv-shows :red_circle: 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.

📍 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.

:red_circle::yellow_circle: 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.

:white_check_mark: To be Mentioned while taking the issue :

Full name :

GitHub Profile Link :

Participant ID (If not, then put NA) :

Approach for this Project :

What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎
Assigned :computer: KWOC 2022
opened by abhisheks008 2
Eurovision Song Contest Analysis
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Eurovision Song Contest Analysis :red_circle: Aim : Create a data analysis project which will analyse the data given in the dataset. :red_circle: Dataset : https://www.kaggle.com/datasets/latifahmakuyi/eurovision-winners-1956-2022 :red_circle: 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.

📍 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.

:red_circle::yellow_circle: 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.

:white_check_mark: To be Mentioned while taking the issue :

Full name :

GitHub Profile Link :

Participant ID (If not, then put NA) :

Approach for this Project :

What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎
Assigned :computer: KWOC 2022
opened by abhisheks008 2
Credit Card Approval Prediction
ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Credit Card Approval Prediction :red_circle: Aim : Create a prediction model based project which will predict whether a credit card is going to be approved or, not. :red_circle: Dataset : https://www.kaggle.com/datasets/devzohaib/predicting-credit-card-approvals :red_circle: 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.

📍 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.

:red_circle::yellow_circle: 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.

:white_check_mark: To be Mentioned while taking the issue :

Full name :

GitHub Profile Link :

Participant ID (If not, then put NA) :

Approach for this Project :

What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎
Assigned :computer: KWOC 2022
opened by abhisheks008 2