In [1]:

    

# Load CSV
import pandas as pd
import numpy as np

filename = 'ex2data1.csv'
data = pd.read_csv(filename)

feature_names = data.columns.values
target_names = data['Admission'].astype('category').cat.categories

print(data.dtypes)
#pd.options.display.max_rows=100
data


    

    




Exam 1       float64
Exam 2       float64
Admission      int64
dtype: object






    
Out[1]:







  

    

      

      
Exam 1
      
Exam 2
      
Admission
    
  
  

    

      
0
      
34.623660
      
78.024693
      
0
    
    

      
1
      
30.286711
      
43.894998
      
0
    
    

      
2
      
35.847409
      
72.902198
      
0
    
    

      
3
      
60.182599
      
86.308552
      
1
    
    

      
4
      
79.032736
      
75.344376
      
1
    
    

      
...
      
...
      
...
      
...
    
    

      
95
      
83.489163
      
48.380286
      
1
    
    

      
96
      
42.261701
      
87.103851
      
1
    
    

      
97
      
99.315009
      
68.775409
      
1
    
    

      
98
      
55.340018
      
64.931938
      
1
    
    

      
99
      
74.775893
      
89.529813
      
1
    
  

100 rows × 3 columns

In [2]:

    

X = data[['Exam 1', 'Exam 2']].values   #Feature Matrix
y = data['Admission'].values     #Target vector


    

In [4]:

    

import matplotlib.pyplot as plt

x_index = 0
y_index = 1

# this formatter will label the colorbar with the correct target names
formatter = plt.FuncFormatter(lambda i, *args: target_names[int(i)])
plt.scatter(X[:, x_index], X[:, y_index], c=y, cmap=plt.cm.get_cmap('Paired', 2))
plt.colorbar(ticks=[0, 1], format=formatter)
plt.clim(-0.5, 1.5)
plt.xlabel(feature_names[x_index])
plt.ylabel(feature_names[y_index]);
plt.show()


    

In [7]:

    

from sklearn.linear_model import LogisticRegression

# Logistic regression
model = LogisticRegression(solver='liblinear', tol=1e-6, max_iter=int(1e6))
model.fit(X, y)    

X_test = [[45, 55],]
y_forecast = model.predict(X_test).astype(int)

print("Exams:", X_test[0], "=>",  y_forecast)


    

    




Exams: [45, 55] => [0]

In [8]:

    

from matplotlib.colors import ListedColormap

h = 0.1  # step size in the mesh

# Create color maps
cmap_light = ListedColormap(['cyan', 'red'])
cmap_bold = ListedColormap(['blue', 'orange'])

# Get '1: Lifetime' and '2: Country' attributes only 
x_index = 0
y_index = 1

# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, x_index].min() - 1, X[:, x_index].max() + 1
y_min, y_max = X[:, y_index].min() - 1, X[:, y_index].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                     np.arange(y_min, y_max, h))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

# Plot also the training points
plt.scatter(X[:, x_index], X[:, y_index], c=y, cmap=cmap_bold, edgecolor='k', s=20)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("Decision boundary")
plt.xlabel(feature_names[x_index])
plt.ylabel(feature_names[y_index]);

plt.show()