search

adithya-s-k / World-of-AI

WORLD OF AI : An open-source repository for AI-based projects 🚀, from beginner to expert level, helping contributors start their journey in Artificial Intelligence and Deep Learning. Our projects provide hands-on experience to real-world problems👨‍💻. Join our community and contribute to the development of AI-based solutions 👥.
https://www.cognitivelab.tech/
MIT License
91 stars 86 forks source link

[PROJECT PROPOSAL]:Water Quality Analysis #223

Open Venkatapranay opened 1 year ago

Venkatapranay commented 1 year ago

Project Request

---Perform EDA on water quality dataset to determine whether the water is drinkable or not

| About | performing data analysis to determine water quality | | Github | Venkatapranay | | Email | pranaykarakavalasa@gmail.com | | Label | GSSOC'23 |

https://github.com/Venkatapranay

Define You

Project Name

Water Quality Analysis

Description

performing data analysis to determine water quality

Scope

can be used in water supply

Timeline

August 6 2023

Video Links or Support Links

dataset : https://www.kaggle.com/datasets/adityakadiwal/water-potability

Venkatapranay commented 1 year ago

Hi @miraj0507 @adithya-s-k ! I have raised this issue under GSSOC'23. Can you assign this issue to me.

Kyouma45 commented 6 months ago

Hello @adithya-s-k, I would like to work on this issue. Can you please assign this issue to me?

SaiBhaskar0987 commented 5 months ago

Full name : Sai Bhaskar Kandula GitHub Profile Link: https://github.com/SaiBhaskar0987 Email ID : saibhaskark05@gmail.com

Approach for this Project :

Conducting exploratory data analysis (EDA) on a water quality dataset to evaluate drinkability entails several essential steps. Initially, comprehending the dataset's structure and rectifying it by addressing missing values and ensuring uniformity is imperative. Descriptive statistics and visual exploration aid in comprehending data distributions and detecting any anomalies or trends. Correlation analysis unveils interrelationships among water quality parameters, while hypothesis testing validates assumptions. Optionally, machine learning models can prognosticate drinkability based on quality parameters. Ultimately, interpreting findings and meticulously documenting the process ensures transparency and facilitates informed decision-making concerning the water's potability.

Dataset: https://www.kaggle.com/datasets/adityakadiwal/water-potability

What is your participant role: GSSOC 2024 Contributor

If you are ok with all sir or any changes you want me to include, please tell me and please @Venkatapranay sir assign me this issue

SaiBhaskar0987 commented 5 months ago

Sir as I reqested you earlier, Can you please assign me the issue @Venkatapranay.