Model Implementation

[malhar2460]

Objective

Implement the following Classification Models for classification of whether the 'mushroom' is 'poisonous' or 'edible'.

Tasks

• Data Cleaning & Transformation, encoding categorial data which has to be used in the model.
• Implement the below given models of Classification.

Models

• Logistic Regression
• K Nearest Neighbours
• Random Forest
• Support Vector Classifier

Hyperparameter tuning for all using GridSearchCV.

Contributors are expected to submit pull requests which satisfies needs of each task. And also the objective of the issue, which is to implement Classification Models for determining whether the 'mushroom' is 'poisonous' or 'edible'.

The task has to be done in .ipynb format in a folder Classification Models.

The code should be well commented explaining the reason behind steps where necessary.

[Abhicoder03]

@malhar2460 Hey I find this issue interesting. I want to work on this. Will you please assign this to me.

[venilv912]

@Abhicoder03 You can work on this issue, you have to implement any one of the models, which model are you going to implement?

[eric157]

hi @malhar2460 @venilv912

I've submitted a pull request implementing Logistic Regression, KNN, Random Forest, and SVC models for the Mushroom Classification project. The key features include: Feature Engineering: Applied categorical encoding and interaction features. Hyperparameter Tuning: GridSearchCV across all models. Results: Achieved precision, recall, and f1-score of 1.00 on both training and test sets. The code is well-documented. Please review and merge if it meets the requirements.

Thanks!

[Abhicoder03]

@Abhicoder03 You can work on this issue, you have to implement any one of the models, which model are you going to implement?

Thanks for assigning me this issue. I'll be implementing Random Forest.