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()

# 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()

# 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

# 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)))

# Now see how well model does on test set using .score which requires 2 arguments # print ('¯\_(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)_/¯ ')

# Add column to dataframe for this prediction as we did before with .map # print ('¯\_(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')