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!
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.
- 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
 Script Winter of Code 2021 |
| 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 |
| Level of Issues | Points Allocated |
|---|---|
| Beginner | 10 Points 🔰 |
| Easy | 20 Points 🌱 |
| Medium | 30 Points 🌕 |
| Hard | 40 Points 🔥 |
Checkout your Leaderboard here, SWOC 2.0 Leaderboard for ML-Crate
Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀
 Abhishek Sharma |
 Vaibhab Gupta |
 A.S.L.Manasa |
 Harsh Pal |
 Neel Shah |
 Rohan Sharma |
 Shreya Pandey |
If you liked working on this project, do ⭐ and share this repository.
🎉 🎊 😃 Happy Contributing 😃 🎊 🎉
If you want to contact me, you can reach me through social handles.
© 2022 Abhishek Sharma
