Data Acquisition

Overview

This section imports data on mushrooms comprised of the attributes listed below and the target that classifies the mushrooms as edible or poisonous. The data and documentation were downloaded from UCI Machine Learning Repository.

The analysis in the subsequent sections reduces the dimensionality of the feature space to two principal components and evaluates performance of several classifiers, including Logistic Regression, KNN, Decision Tree, Random Forest, SVC, Naive Bayes and Neural Network, in predicting whether a mushroom with particular attributes is edible or not.

Data cleaning and processing is described in the next section.

This project is based on materials from Applied Machine Learning in Python by University of Michigan on Coursera

The analysis for this project was performed in Python.

Data

The target and features are described below.

Attribute Information: (classes: edible=e, poisonous=p)
     1. cap-shape:                bell=b,conical=c,convex=x,flat=f,
                                  knobbed=k,sunken=s
     2. cap-surface:              fibrous=f,grooves=g,scaly=y,smooth=s
     3. cap-color:                brown=n,buff=b,cinnamon=c,gray=g,green=r,
                                  pink=p,purple=u,red=e,white=w,yellow=y
     4. bruises?:                 bruises=t,no=f
     5. odor:                     almond=a,anise=l,creosote=c,fishy=y,foul=f,
                                  musty=m,none=n,pungent=p,spicy=s
     6. gill-attachment:          attached=a,descending=d,free=f,notched=n
     7. gill-spacing:             close=c,crowded=w,distant=d
     8. gill-size:                broad=b,narrow=n
     9. 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
    10. stalk-shape:              enlarging=e,tapering=t
    11. stalk-root:               bulbous=b,club=c,cup=u,equal=e,
                                  rhizomorphs=z,rooted=r,missing=?
    12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
    13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
    14. stalk-color-above-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o,
                                  pink=p,red=e,white=w,yellow=y
    15. stalk-color-below-ring:   brown=n,buff=b,cinnamon=c,gray=g,orange=o,
                                  pink=p,red=e,white=w,yellow=y
    16. veil-type:                partial=p,universal=u
    17. veil-color:               brown=n,orange=o,white=w,yellow=y
    18. ring-number:              none=n,one=o,two=t
    19. ring-type:                cobwebby=c,evanescent=e,flaring=f,large=l,
                                  none=n,pendant=p,sheathing=s,zone=z
    20. spore-print-color:        black=k,brown=n,buff=b,chocolate=h,green=r,
                                  orange=o,purple=u,white=w,yellow=y
    21. population:               abundant=a,clustered=c,numerous=n,
                                  scattered=s,several=v,solitary=y
    22. habitat:                  grasses=g,leaves=l,meadows=m,paths=p,
                                  urban=u,waste=w,woods=d

The following function imports the raw data:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pylab

def read_mushroom_data():
    
    df = pd.read_csv('/Users/eagronin/Documents/Data Science/Portfolio/Project Data/mushrooms.csv')
    
    return df

Next step: Data Preparation