Build a Machine Learning Web App for Binary Classification of Mushroom with Streamlit and Python

This interactive ML web application will allows users to choose what classification algorithm they want to use and let them interactively set hyper-parameter values, all without them knowing to code. This web app will be hosted on localhost.

Dataset Description :

In this project we are goint to be working eith Mushroom dataset from UCI Machine Learning Repository. This dataset contains hypothetical samples of 23 various kind of mushrooms and a lo tof different characteristics about them.And this dataset is often used for binary classification tasks. The objective is to classify mushroom as either Edible or Poisonous using the features given as tabular data.

Aim of this project

Instead of building best possible classification model or predictive model, our aim is to built the web application that allows user

1. to intercatively choose their classification algorithm such as SVM, Logistic Regression and Random Forest Classifier.
2. and then set their own values of hyperparameters so that the users can learn which alogortihms fits best by adjustinf the hyperparameters.
3. Users can also choose Evaluation matrix.

Implementation Workflow:

1. Project Overview and Demo

2. Turn Simple Python Scripts into Web Apps

3. Load the Mushrooms Data Set

4. Creating Training and Test Sets

5. Plot Evaluation Metrics

6. Training a Support Vector Classifier

7. Training a Support Vector Classifier (Part 2)

8. Train a Logistic Regression Classifier

9. Training a Random Forest Classifier