-
Notifications
You must be signed in to change notification settings - Fork 0
/
iris.py
187 lines (115 loc) · 455 KB
/
iris.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
# -*- coding: utf-8 -*-
"""EMILY- Participant_Hackathon.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/19VA714nm7k5lqs2IueU8RqBhXBrKBEbC
<font color = "#5D3FD3"><h1><b> Welcome to HackBI VI AI/Machine Learning Workshop!!🌺🌷 </b></h1>
##**Overview**:
<font color = "white">
The UCI iris dataset is one of the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
Predicted attribute: class of iris plant.
Dataset from UCI Machine Learning Archive: https://archive.ics.uci.edu/ml/datasets/Iris
### **About Iris Dataset**
The UCI Iris data set is a well-known dataset in the field of machine learning and data science. It contains 150 observations of iris flowers, each with four attributes: sepal length, sepal width, petal length, and petal width. The data set is often used as a beginner's dataset for machine learning classification problems as it contains 3 classes of 50 instances each, where each class refers to a type of iris plant. The three classes in the Iris dataset are: *Iris-setosa, Iris-versicolor, and Iris-virginica*.
The four variables involved in the dataset are:
- ***Sepal Length***: the length of the sepal of the iris flower, measured in centimeters.
- ***Sepal Width***: the width of the sepal of the iris flower, measured in centimeters.
- ***Petal Length***: the length of the petal of the iris flower, measured in centimeters.
- ***Petal Width***: the width of the petal of the iris flower, measured in centimeters.
The Iris dataset is commonly used for Exploratory Data Analysis (**EDA**) and classification or clustering. It is a great dataset for beginners to practice data visualization, data cleaning, and data preparation techniques.
____________________________________________________________________________________________
____________________________________________________________________________________________
### EDA (Exploratory Data Analysis)
During EDA, data scientists use a variety of techniques to visualize and explore the data. Some common techniques include plotting histograms, scatter plots, and box plots to visualize the distribution of different variables and identify patterns. They also use statistics such as mean, median, and standard deviation to understand the central tendency and spread of the data.
EDA is a *crucial* step in machine learning projects, as it allows data scientists to understand the structure and characteristics of the data, identify any issues or problems, and gain insights that can inform the development of the machine learning model.
____________________________________________________________________________________________
#### Import Libraries
"""
# importing packages
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import datasets, model_selection, metrics
"""____________________________________________________________________________________________
#### Load in data
**note**: We will be categorizing by `Type` in this case. The numbers correspond to these types of irises:
0 = Iris Setosa
1 = Iris Versicolour
2 = Iris Virginica
"""
iris_dataset = datasets.load_iris()
df = pd.DataFrame(data=iris_dataset.data, columns=iris_dataset.feature_names)
df['Type'] = iris_dataset.target
df.head()
"""Check out the pandas.DataFrames API for more!
Link: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html
____________________________________________________________________________________________
#### Checking for possible errors in data
"""
df.isnull().sum()
data = df.drop_duplicates(subset ="Type",)
data #show data
df.value_counts("Type")
"""____________________________________________________________________________________________
#### Deciding classification features
Let's compare the columns/variables we have that will help us predict the class of flower
"""
sns.scatterplot(x='sepal length (cm)', y='sepal width (cm)',
hue='Type', data=df, )
# Placing Legend outside the Figure
plt.legend(bbox_to_anchor=(1, 1), loc=2)
plt.show()
sns.scatterplot(x='petal length (cm)', y='petal width (cm)',
hue='Type', data=df, )
# Placing Legend outside the Figure
plt.legend(bbox_to_anchor=(1, 1), loc=2)
plt.show()
"""**Question: Based on the two scatterplots above which would be the best to use for classifying the type of iris?What model would be best to use based on the choosen scatterplot?**
____________________________________________________________________________________________
### Splitting data: Training and Testing
"""
inputs = df[["petal length (cm)","petal width (cm)"]]
output = df[["Type"]]
X_train, X_test, y_train, y_test = model_selection.train_test_split(inputs, output, test_size = 0.2, random_state = 42)
"""____________________________________________________________________________________________
### Import and Initialize model
"""
# import the KNN algorithm
from sklearn.neighbors import KNeighborsClassifier
# initialize
model = KNeighborsClassifier(n_neighbors = 3)
"""____________________________________________________________________________________________
### Fit model to training set
"""
# fit
model.fit(X_train, y_train)
"""____________________________________________________________________________________________
### Testing the model
"""
# predict
pred = model.predict(X_test)
# visualize these predictions
petal_length = X_test['petal length (cm)']
petal_width = X_test['petal width (cm)']
iris_class = pred
plt.figure(figsize=(10,6))
plt.scatter(petal_length, petal_width, c = iris_class)
plt.title("Petal length vs. Petal Width colored by Iris Type")
plt.xlabel("petal length (cm)")
plt.ylabel("petal width (cm)")
plt.show()
"""____________________________________________________________________________________________
### Making Predictions
1. Predict the type of an iris with petal length 4.5 cm and petal width 3.2 cm.
"""
pred1 = pd.DataFrame([[4.5,3.2]], columns = ["petal length (cm)", "petal width (cm)"])
print(model.predict(pred1))
""" 2. Predict the type of an iris with petal length 1.4 cm and petal width 0.7 cm."""
pred1 = pd.DataFrame([[1.4,0.7]], columns = ["petal length (cm)", "petal width (cm)"])
print(model.predict(pred1))
""" 3. Predict the type of an iris with petal length 1.7 cm and petal width 3.4 cm."""
pred1 = pd.DataFrame([[1.7,3.4]], columns = ["petal length (cm)", "petal width (cm)"])
print(model.predict(pred1))