ML work to predict the percentage of a student based on the number of study hours. Also, obtain a heat map and other plots for a better understanding of the result. issue number #190

I want to contribute to your project under DCP'21 I want to add Face_eye and nose Detection part to your repository with the help of open cv and Python

@Ayush7614 Kindly assign this issue to me :)

Description

Create a Code of Conduct which the Mentors and Participants must abide by.

Additional Details

• Mention the Standards which everyone should abide by on welcoming and inclusive language including constructive criticism.
• Add the required responsibilities for Project Maintainers.
• Mention the Scope of this Code of Conduct.

Image Compression using Clustering

• The main objective of this project is to use clustering algorithm like K-Means to compress the image RGB channels into k-clusters.

• [x] DCP'21 Participant

Closes: #53

Description

Compressed images into k-colors using K-Means algorithm. Created an Interactive Control to toggle between different images and get their compressed form displayed.

Good afternoon

This is Priyanka Shahane. I'm DevIncept Participant. My issue number is 82.

I have contributed to the 'Automated Essay Grading ' project by generating graphs for comparing Kappa Score of different algorithms. PFA of the code for same.

https://github.com/Priyanka333/Soomvaar/blob/91d210969cae61120b7557ea6744baeed847116a/Automated%20Essay%20Grading/Model/Automated_Essay_Grading%20(2).ipynb

I want to add model of Olympic Medal Prediction to predict whether a given athlete will win the medal or not at the Olympics based on their characteristics. I will different classification machine learning algorithms and compare their accuracy.

I will use 120 years of Olympic history: athletes and results kaggle dataset for predictions.

Dataset Link: https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results

Please assign me this issue as a part of DCP'21.

fixes #29

About the data:

• Let's visualize the time-series elements of the data to understand the patterns.

• The input dimension of the model learned with Train data should be well thought out and predicted with Test data.

• The task is to forecast the total amount of products sold in every shop for the test set. Note that the list of shops and products slightly changes every month. Creating a robust model that can handle such situations is part of the challenge.

Accuracy of different models used

By using Linear Regression I got

   Test set Accuracy:   0.42878283190980937

By using Random Forest Regressor I got

   Test set Accuracy:  0.9324485724217306

CONCLUSION

• Performance of random forest regressor is better in terms of accuracy as compared to linear regression.

Crop Diseases Classification is a project to assist people in identify the disease that they are affected. The project build with CNN to predict the disease.

Fixes #36

The main objective for this dataset:

• Using machine learning techniques to predict loan payments.

Accuracy of different models used

By using Logistic Regression I got

   test accuracy score of  Logistic Regression =  83%

By using Random Forest Classifier I got

   test accuracy score of Random Forest =  78.38%

By using Decision Tree Classifier I got

   test accuracy score of Decision Tree =  70.27%

By using XGBoost Classifier I got

   test accuracy score of XGBoost =  70.27%

Conclusion

• Credit_History is a very important variable because of its high correlation with Loan_Status therefore showing high Dependancy for the latter.
• The Logistic Regression algorithm is the most accurate: approximately 83%.

fixes #10

About the data:

• This is the sentiment140 dataset. It contains 1,600,000 tweets extracted using the twitter api . The tweets have been annotated (0 = negative, 4 = positive) and they can be used to detect sentiment .

Accuracy of different models used

By using Linear Regression I got

   Train set Accuracy:   0.5191175930606196

By using Random Forest Classifier I got

   Train set Accuracy:   0.9894093667260677

By using Decision Tree Classifier I got

   Train set Accuracy:   0.9894132729791195

@Ayush7614 please assign this issue to me. I want to contribute an Anime Recommendation model based on 2 different methods(correlation and text vectorisation with cosine similarity) as a part of my contributions for HACKTOBERFEST 2021

Datasets can be found on kaggle

Libraries used: numpy pandas sklearn warnings

About Project Liveness detection will validate and verify whether an individual is actually present or using any means to spoof the Face Authentication system.

@Ayush7614 kindly assign this issue to me, under hacktoberfest 2021 label.

The current tag labels everyone's status as project not participating in our Hacktoberfest progress.

It'd be great if you could change it to the hacktoberfest2021 tag.

About

• Given data about the composition and age of different concretes, try to predict the compressive strength of a given concrete.

Dataset: https://www.kaggle.com/maajdl/yeh-concret-data

Can I work on this issue?