features.py

# -*- coding: utf-8 -*-
"""Features.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/18uLPiducb7uDtXEG5TvEnc2TVT-s20Ki
"""

!wget https://raw.githubusercontent.com/mdhabeebvulla/samples/master/datasetsiris.csv

import pandas as pd
import numpy as np

import seaborn as sns

df = pd.read_csv('datasetsiris.csv')
df.head()

df1 = df.iloc[:,1:6]
df1['Species'].unique()

df1['Species'] = df1['Species'].replace(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'],[0,1,2])

df1.head()

sns.heatmap(df1.iloc[:,0:5].corr().abs(),annot=True)

import matplotlib.pyplot as plt

X = df1.iloc[:,1:4]
y = df1.iloc[:,4]

from sklearn.feature_selection import SelectKBest, chi2

X.shape

X_new = SelectKBest(chi2,k='all').fit_transform(X, y)

X_new.shape

X_new

df2 = df1.iloc[0:100,:]

df2.shape

df2.head()

df2['Species'].unique()

import matplotlib.pyplot as plt
sns.FacetGrid(df2,hue='Species',size=10)\
.map(plt.scatter,'SepalLengthCm','SepalWidthCm')\
.add_legend()
pass

xfit = np.linspace(-1, 7.0)
plt.scatter(P.iloc[:, 0], P.iloc[:, 1], c=q, s=50, cmap='viridis')
plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)
for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:
    plt.plot(xfit, m * xfit + b, '-k')
plt.xlim(4, 7.2);

from sklearn.svm import SVC # "Support vector classifier"
model = SVC(kernel='linear', C=1E10)
model.fit(P, q)

import matplotlib.pyplot as plt
import seaborn as sns
#plt.figure(figsize=(10, 8))# Plotting our two-features-space
sns.scatterplot(x=P.iloc[:, 0],y=P.iloc[:, 1],hue=q,s=50)
# Constructing a hyperplane using a formula.
w = model.coef_[0]           
b = model.intercept_[0]     
x_points = np.linspace(-1, 4)    
y_points = -(w[0] / w[1]) * x_points - b / w[1] 
plt.plot(x_points, y_points, c='r');