grohith327

import pandas as pd

df = pd.read_csv('/Users/rohith/Documents/Datasets/Iris_dataset/iris.csv')
df = df.drop(['Id'],axis=1)
target = df['Species']
s = set()
for val in target:
    s.add(val)
s = list(s)
rows = list(range(100,150))

grohith327

from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression

clf = LogisticRegression()
clf.fit(x_train,y_train)
y_pred = clf.predict(x_test)
print(accuracy_score(y_test,y_pred))

grohith327

import matplotlib.pyplot as plt 

cost_func = np.array(cost_func)
cost_func = cost_func.reshape(10000,1)
plt.plot(range(len(cost_func)),cost_func)

grohith327

from sklearn.metrics import accuracy_score

test_x_1 = x_test[:,0]
test_x_2 = x_test[:,1]
test_x_3 = x_test[:,2]
test_x_4 = x_test[:,3]

test_x_1 = np.array(test_x_1)
test_x_2 = np.array(test_x_2)
test_x_3 = np.array(test_x_3)

grohith327

## Logistic Regression 
import numpy as np

def sigmoid(x):
    return (1 / (1 + np.exp(-x)))

m = 90
alpha = 0.0001

theta_0 = np.zeros((m,1))

grohith327

from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
import numpy as np

X, Y = shuffle(X,Y)

x_train = []
y_train = []
x_test = []
y_test = []

grohith327

import pandas as pd

df = pd.read_csv('/Users/rohith/Documents/Datasets/Iris_dataset/iris.csv') ## Load data
df = df.drop(['Id'],axis=1)
rows = list(range(100,150))
df = df.drop(df.index[rows])  ## Drop the rows with target values Iris-virginica
Y = []
target = df['Species']
for val in target:
    if(val == 'Iris-setosa'):

grohith327

import matplotlib.pyplot as plt 

y_prediction = a_0 + a_1 * x_test
print('R2 Score:',r2_score(y_test,y_prediction))

y_plot = []
for i in range(100):
    y_plot.append(a_0 + a_1 * i)
plt.figure(figsize=(10,10))
plt.scatter(x_test,y_test,color='red',label='GT')

grohith327

## Linear Regression 
import numpy as np

n = 700
alpha = 0.0001

a_0 = np.zeros((n,1))
a_1 = np.zeros((n,1))

epochs = 0

grohith327

from sklearn.linear_model import LinearRegression 
from sklearn.metrics import r2_score

clf = LinearRegression(normalize=True)
clf.fit(x_train,y_train)
y_pred = clf.predict(x_test)
print(r2_score(y_test,y_pred))