Iris Classification using DL

[Sindhu-2004]

The goal of this task is to create a model that can accurately predict the species of an iris flower based on its sepal length, sepal width, petal length, and petal width.

[abhisheks008]

Can you share the dataset and approach for solving this issue?

[Sindhu-2004]

Project Title : Iris Flower Classification Aim : The goal of this task is to create a model that can accurately predict the species of an iris flower based on its sepal length, sepal width, petal length, and petal width. Dataset : [Iris Flower Dataset] https://www.kaggle.com/datasets/arshid/iris-flower-dataset Approach : Logistic Regression, Classification, scikit-learn, TensorFlow, and PyTorch.

[abhisheks008]

One issue at a time.

[Mehaksaini2808]

Hey! If not assigned to anyone I would love to work on this Project Title: Iris Flower Classification Aim: The goal of this task is to create a ML model that aims to predict the species of an Iris flower based on its morphological features. Dataset : https://www.kaggle.com/datasets/uciml/iris Approach :

• Performing EDA to help understand the dataset and to identify patterns, relationships, and potential issues like outliers or missing values.
• Data Processing - To handle missing values, feature scaling, encode categorical data
• Model - Training with the help of Logistic Regression, SVM, and Random forest
• Model Evaluation - Accuracy, Confusion matrix, precision, recall f1 score and ROC-AUC curve.

[Mehaksaini2808]

1. Full name : Mehak Saini
2. Email ID : [email protected]
3. Dataset : https://www.kaggle.com/datasets/uciml/iris
4. Approach : Logistic Regression, SVM and random forest. Perform model Evaluation and select the best performing model. What is your participant role? (Mention the Open Source program)Contributor in GSSOC'24

[Mehaksaini2808]

Please assign this issue to me.

[abhisheks008]

1. Full name : Mehak Saini
2. Email ID : [email protected]
3. Dataset : https://www.kaggle.com/datasets/uciml/iris
4. Approach : Logistic Regression, SVM and random forest. Perform model Evaluation and select the best performing model. What is your participant role? (Mention the Open Source program)Contributor in GSSOC'24

This repo majorly focuses on deep learning methods rather than machine learning techniques.

[Mehaksaini2808]

Sorry for the confusion!

I can implement 3 deep learning model for this dataset and find the best-fitted one by comparing them all based on their accuracy.

[abhisheks008]

This issue is raised by a contributor for personal contribution, I don't think you can work on this issue.

You can check out the issues raised by me and having Up for Grabs labels.

[wildcraft958]

Full name : Animesh Raj Dataset : https://www.kaggle.com/datasets/uciml/iris Techstack: pandas, matplotlib, seaborn, pytorch Approach : 1. Data cleaning 2. EDA 3. Models i will be using are FNN and CNN. 4. Evaluation What is your participant role? KWOC'24

[SupratikB23]

Hello, I would love to work on this project under SWOC'25.

Project Title: Iris Flower Classification Name: Supratik Bhowal Participant role: SWOC'25 Dataset: https://www.kaggle.com/datasets/arshid/iris-flower-dataset

Approach:

1. Models Used:
   • Use MLP (FNN)
   • Use Deep NN applying Dropout for regularization
   • Use CNN having 2D convolutional layers (architechture: LeNet-5 -> for simplicity)
2. All Model Compilation will be done using categorical cross-entropy as the loss func and also will use the Adam optimizer
3. Scatterplot Matrix, Confusion Matrix Heatmap, Distribution Plots along with Training and Validation Accuracy & Loss for each model used.

[abhisheks008]

Hello, I would love to work on this project under SWOC'25.

Project Title: Iris Flower Classification Name: Supratik Bhowal Participant role: SWOC'25 Dataset: https://www.kaggle.com/datasets/arshid/iris-flower-dataset

Approach:

1. Models Used:

   • Use MLP (FNN)
   • Use Deep NN applying Dropout for regularization
   • Use CNN having 2D convolutional layers (architechture: LeNet-5 -> for simplicity)

2. All Model Compilation will be done using categorical cross-entropy as the loss func and also will use the Adam optimizer

3. Scatterplot Matrix, Confusion Matrix Heatmap, Distribution Plots along with Training and Validation Accuracy & Loss for each model used.

It's iris classification, not the flower one :(

[SupratikB23]

for

So, is it this dataset: ↓ https://www.kaggle.com/datasets/naureenmohammad/mmu-iris-dataset

Or others like finding Iris diseases?