MushroomClassification

Detect the poisonous mushroom using its physical characteristics.

Table of Contents

• General Info
• Data Description
• Libraries
• Screenshots
• Setup
• Usage
• Contact
• License

General Information

The goal is to build a machine learning model which predicts whether a mushroom is edible or poisonous based on the characteristics. There are two classes: ‘e’ and ‘p’.

• ‘e’ means that the mushroom is edible.
• ‘p’ means that the mushroom is poisonous.

Data Description

This dataset describes mushrooms in terms of their physical characteristics

• cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s
• cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s
• cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y
• bruises?: bruises=t,no=f
• odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s
• gill-attachment: attached=a,descending=d,free=f,notched=n
• gill-spacing: close=c,crowded=w,distant=d
• gill-size: broad=b,narrow=n
• gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y
• stalk-shape: enlarging=e,tapering=t
• stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?
• stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
• stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
• stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y
• stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y
• veil-type: partial=p,universal=u
• veil-color: brown=n,orange=o,white=w,yellow=y
• ring-number: none=n,one=o,two=t
• ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z
• spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y
• population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y
• habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d

Data is available as multiple sets of files. Each file will contain physical characteristics and a column to indicate whether it is edible [‘e’] or not [‘p']. Apart from data files, schema files are provided as a part of Data Sharing Agreement which contains all the relevant information about both train and test data such as:

• File name convention
• No of columns in each file
• Data type of each column
• Name of the columns

Libraries

• Application Framework - flask, wsgiref
• Database operations - sqlite3
• Data processing and ML - numpy, pandas, matplotlib, sklearn, xgboost, kneed, pickle, seaborn
• General operations - os, shutil, csv, json, re, datetime, time

Screenshots

Setup

Clone this repo using

git clone https://github.com/Anil-45/MushroomClassification.git

Install the required modules using

pip install -r requirements.txt

Usage

Run the following command to start the application

python app.py

Open the application

• Upload Train CSV use this option to upload custom training files
• Train trains the model the using uploaded training files
• Default Train trains the model using default files. Make sure the data files are present in data/raw/train to train the model. Trained models are saved to models folder
• Default Predict predicts the output using saved models. Make sure the data files are present in data/raw/test for prediction
• Upload Test CSV use this option to upload custom test files
• Predict predicts the outcome of custom files using saved models

You can find the logs in logs folder

Contact

Created by @Anil_Reddy

License

This project is available under the MIT.