Top Foreign Languages Analysis

[abhisheks008]

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Top Foreign Languages Analysis
🔴 Aim : The aim of this project is to analyze the top foreign languages based on the given dataset.
🔴 Dataset : https://www.kaggle.com/datasets/timmofeyy/top-foreign-languages-preply-tutors
🔴 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.

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📍 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.

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🔴🟡 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.

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✅ 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.)

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Happy Contributing 🚀

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

[Yuvika-14]

hi abhisekh
Full name: Yuvika Singh
git hub profile link:https://github.com/Yuvika-14/Yuvikademo.git
approch: ensemble methods, gradient boosting, neural networks.
Take care of the missing data if there are any categorical values then will use one hot encoder or label encoder depending upon the issue.

[ghousiya47]

Can you please me assign this issue?
Full name : Ghousiya Begum
GitHub Profile Link : github.com/ghousiya47
Participant ID (If not, then put NA) : NA
Approach for this Project : I will use different supervised machine leaening algorithms such as randomforest, svm to predict the data and matplotlib and seaborn for analysis, and we can check accuracy using skleanr.metrics package. in order to increase accuracy i will do hyperparameter tuning.
What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) : JWoC

[abhisheks008]

Issue assigned to you @ghousiya47

[ghousiya47]

Issue assigned to you @ghousiya47

Sir may i know till when i can submit this project.

[abhisheks008]

Hi @ghousiya47 as this a machine learning project with proper implementation, I know it requires time to put forward the highest quality in the project. You can take as much time as you, but make sure you are creating something which should be of highest quality.

[ghousiya47]

okay thank you sir and i will try my best.

[ghousiya47]

@abhisheks008 Greetings sir, i apologise for being inactive this days as i was busy with my final year 7th sem btech exams. i am having a doubt, as my dataset consist of a zip files with more than one csv file, should i need to concatenate all csv files, or i should perform analysis for each csv file separately? what actually i have to find accuracy for?

[abhisheks008]

No issues with the semester exams, I hope you have done well in that. You can start contributing now.

You need to find out the best csv file which directly suggests the problem statement given to you. You can use all the csv files, or you can use one of them, or even you can use 2-3 of them, it depends on you and your requirement.

Model creation is for implementing machine learning methods to a certain parameter which you might think is matching with the problem statement. You need to explore the dataset and try to find out that.

@ghousiya47