In this repository, a linear regression model is built with numpy, matplotlib and pandas to classify four features of Iris Plants. The attribute information is as below:

1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica

Two of classes are chosen to be classified: Iris_setosa and Iris_versicolor

Numpy, Matplotlib andPandas

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

1. Load the database

df = pd.read_csv('iris.data', header=None)

2. extract data of four features
3. Divide the database to train and test data with 1 or -1 label
4. Calculate the accuracy percentage before the nodel is trained
5. Train the model with function PercentCorrect
6. Calculate the accuracy percentage after the nodel is trained

This is still under construction. More features will be presented in the near feature.

1.Accuracy: train 100%, test 96%

2.Learning curve

http://archive.ics.uci.edu/ml/datasets/Iris

Note that the kit is still under construction. Pull requests are welcome. For major changes, please contact me via the mail [email protected]