Closed abhisheks008 closed 10 months ago
@abhisheks008 can I work on this issue?
Can you share your approach for solving this issue? @ankana2113
can I be assigned this issue?
@Cgarg9 please share your approach for solving this issue.
Full name : Chirag Garg GitHub Profile Link : https://github.com/Cgarg9 Participant ID (If not, then put NA) : Approach for this Project : Would plot few graphs to visualise the data trends. Then would try different algorithms like linear regression, random forest, XGBoost. What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) KWOC
i don't know where to find my participant id. Could you tell?
Don't worry about the participant ID, it's of no use in KWOC. Anyways it's a nice approach for this issue. Issue assigned to you. You can start working on it. @Cgarg9
Hi @Cgarg9 any update on this issue?
Almost done with this. Will make the pr by Saturday
@abhisheks008 i would like to work on this issue. Could you please assign it to me?
Full name : Avdhesh Varshney GitHub Profile Link : https://github.com/Avdhesh-Varshney 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.) KWOC
@abhisheks008 please assign this project to me to contribute under KWOC'23
Hi @Avdhesh-Varshney this is already assigned to someone.
Hi @abhisheks008 , I would like to take up this issue!! Full name : Aniket Ray GitHub Profile Link : https://github.com/raysofani Participant ID (If not, then put NA) : NA Approach for this Project :
Can you share the models you want to implement for this project?
Full name: Pranjali Pilankar GitHub Profile Link: https://github.com/pranjalipilankar Participant ID (If not, then put NA) : Approach for this Project: 1) Preprocessing the data to remove outliers and reduce dimensions by PCA 2) Plotting data using Matplotlib to identify distinct trends and also make assessments regarding the suitable model to apply 3) Apply algorithms like linear regression, polynomial regression, and random forest algorithms. Also, explore other algorithms which may be suitable according to the distribution of the data 4) Analyse the accuracy of the models using RMSE error and also explore other methods. What is your participant role?: IWOC '24
@abhisheks008 Please assign this issue to me under JWOC'24 :)
@abhisheks008 Please assign this issue to me under JWOC'24 :)
This issue is part of IWOC2024.
Can you share the models you want to implement for this project?
Will use common ones like Linear regression, decision tree, random forest and support vector machine. Will also apply Gradient boosting that may offer improved accuracy. Further the choice of model will depend on the complexity of the dataset , which we can come upon once analysing the dataset.
@abhisheks008 Please assign this issue to me under IWOC2024
Full name : Pawas Pandey GitHub Profile Link : github.com/pawaspy Participant ID (If not, then put NA) : NA Approach for this Project : Linear regression, decision tree, random forest and support vector machine. Will also apply Gradient boosting that may offer improved accuracy. Further the choice of model will depend on the complexity of the dataset , which we can come upon once analysing the dataset. What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) : JWoC
@abhisheks008 please assign me the issue.
Sorry for the typo mistake. I want to work on this issue as a part of IWOC '24.
Full name : Bhavya Kumar GitHub Profile Link : https://github.com/bhavya-06darshnik Participant ID (If not, then put NA) : NA
@abhisheks008 please assign me this issue as IWOC2024
Full name : Md Aamir Ansari GitHub Profile Link : https://github.com/aamiransari072 Participant ID :NA Approach for this Project : we first do the eda on data set trying to figure out the insides of data set then we apply diffrent diffrent algorithms like linear regression , svm, decison tree and deep learning then we evaluate our model according to roc curve and mae What is your participant role? IWOC 2.O
Issue assigned to @raysofani on FCFS basis. Those who have commented apart from @raysofani, you can check out other issues present in this repo (check out the page 2 of the Issues section, lots of open issues are present there to work on).
ML-Crate Repository (Proposing new issue)
:red_circle: Project Title : House Rent Analysis and Prediction of Mumbai :red_circle: Aim : Create a ML model for analyzing and predicting the house rents of Mumbai based on the given dataset. :red_circle: Dataset : https://www.kaggle.com/datasets/lokeshgupta2020/house-rent-in-mumbai :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 :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.:red_circle::yellow_circle: Points to Note :
:white_check_mark: To be Mentioned while taking the issue :
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎