import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from pandas import DataFrame, Series
import seaborn as sns
import pandas as pd
# Load data into a dataframe & print
#
df= pd.read_csv ('admit_data.csv')
df.head()
Englishfloat64
Mathfloat64
0
35
54.625
1
26.5
68.5
2
41
57
3
21.5
42
4
46
84
# Print out feature names to get exact names (some have spaces) using .columns
df.columns
# Get rid of spaces in feature names using df.columns = [ list of new names in quotes]
#
df.columns= ['English', 'Math', 'Outcome']
# Create new feature called 'Admit' using map or apply with values "yes" or "no"
# For this case it's easier to use .map as we did for iris dataset
#
df['Admit'] = df['Outcome'].map( {0:'no',
1: 'yes'})
# Set background grid for Seaborn & plot Math vs English with color indicating whether the student was admitted
# or not
#
sns.set_style ('whitegrid')
sns.relplot (x='Math', y='English', data=df, hue='Outcome')
# Create target 1D array and 2D data array dimensioned 100 by 2
#
# create target array
#
# Get NumPy array for english and math scores, stack them and take transpose
# Remember that np.vstack takes 1 argument so put your arrays in ( ) and remember to use .T to transpose
#create 2d array
df['target'] = admissions.target
df.head()
NameError: name 'admissions' is not defined
# split our data into a training set and a test set; we choose 75-25 split
# We are splitting the entire data set and target
#
# first import command fro scikit-learn
from sklearn.model_selection import train_test_split
#
# split the data
from sklearn.model_selection import train_test_split
target= df.target.values
(X_train, X_test, y_train, y_test) = train_test_split (X, target, test_size+.2)
n_train =len(X_train) ; n_test = len(X_test)
print (n_train, n_test)
#
# Print out length of each set
AttributeError: 'DataFrame' object has no attribute 'target'
# Now use logistic regression on training set; see how well we do
#
#
lr= LogisticRegression (solver = 'lbfgs')
X1 =X_train[:,0]; X1=np.reshape (X1, (n_train,1))
# Create model
#
# Fit with training set
lr.fit(X1, y_train)
#
# Calculate training score using .score(X_train, y_train) & print out percent accuracy
#
print (("The percent accuracy using mean radius on training set is ", \
100*lr.score(X1,y_train)))
NameError: name 'X_train' is not defined
# Now see how well model does on test set using .score which requires 2 arguments
#
print ('¯\_(ツ)_/¯ ')
¯\_(ツ)_/¯
# We want to plot the prediction for each data point so first we add a column to dataframe with prediction
# To do this, predict all data using .predict; print out score
#
# predict all data
#
# Add column to dataframe
print ('¯\_(ツ)_/¯ ')
¯\_(ツ)_/¯
# Add column to dataframe for this prediction as we did before with .map
#
print ('¯\_(ツ)_/¯ ')
¯\_(ツ)_/¯
# Plot Math vs English with color indicating prediction & compare with scatterplot with actual outcome
sns.set_style ('whitegrid')
sns.relplot (x='Math', y='English', data=df, hue='Outcome')