Data retrieval¶

import pandas as pd
# turn of warning messages
pd.options.mode.chained_assignment = None  # default='warn'

# get data
df = pd.read_csv('student_records.csv')
df

Data preparation¶

Feature extraction and engineering¶

# get features and corresponding outcomes
feature_names = ['OverallGrade', 'Obedient', 'ResearchScore', 'ProjectScore']
training_features = df[feature_names]

outcome_name = ['Recommend']
outcome_labels = df[outcome_name]

# view features
training_features

# view outcome labels
outcome_labels

# list down features based on type
numeric_feature_names = ['ResearchScore', 'ProjectScore']
categoricial_feature_names = ['OverallGrade', 'Obedient']

Numeric Feature Scaling¶

from sklearn.preprocessing import StandardScaler
ss = StandardScaler()

# fit scaler on numeric features
ss.fit(training_features[numeric_feature_names])

# scale numeric features now
training_features[numeric_feature_names] = ss.transform(training_features[numeric_feature_names])

# view updated featureset
training_features

Engineering Categorical Features¶

training_features = pd.get_dummies(training_features, columns=categoricial_feature_names)
# view newly engineering features
training_features

# get list of new categorical features
categorical_engineered_features = list(set(training_features.columns) - set(numeric_feature_names))

Modeling¶

from sklearn.linear_model import LogisticRegression
import numpy as np

# fit the model
lr = LogisticRegression() 
model = lr.fit(training_features, np.array(outcome_labels['Recommend']))
# view model parameters
model

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

Model Evaluation¶

# simple evaluation on training data
pred_labels = model.predict(training_features)
actual_labels = np.array(outcome_labels['Recommend'])

# evaluate model performance
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report

print('Accuracy:', float(accuracy_score(actual_labels, pred_labels))*100, '%')
print('Classification Stats:')
print(classification_report(actual_labels, pred_labels))

Accuracy: 100.0 %
Classification Stats:
             precision    recall  f1-score   support

         No       1.00      1.00      1.00         5
        Yes       1.00      1.00      1.00         3

avg / total       1.00      1.00      1.00         8

Model Deployment¶

from sklearn.externals import joblib
import os
# save models to be deployed on your server
if not os.path.exists('Model'):
    os.mkdir('Model')
if not os.path.exists('Scaler'):
    os.mkdir('Scaler') 
    
joblib.dump(model, r'Model/model.pickle') 
joblib.dump(ss, r'Scaler/scaler.pickle')

['Scaler/scaler.pickle',
 'Scaler/scaler.pickle_01.npy',
 'Scaler/scaler.pickle_02.npy',
 'Scaler/scaler.pickle_03.npy']

Prediction in Action¶

# load model and scaler objects
model = joblib.load(r'Model/model.pickle')
scaler = joblib.load(r'Scaler/scaler.pickle')

## data retrieval
new_data = pd.DataFrame([{'Name': 'Nathan', 'OverallGrade': 'F', 'Obedient': 'N', 'ResearchScore': 30, 'ProjectScore': 20},
                  {'Name': 'Thomas', 'OverallGrade': 'A', 'Obedient': 'Y', 'ResearchScore': 78, 'ProjectScore': 80}])
new_data = new_data[['Name', 'OverallGrade', 'Obedient', 'ResearchScore', 'ProjectScore']]
new_data

## data preparation
prediction_features = new_data[feature_names]

# scaling
prediction_features[numeric_feature_names] = scaler.transform(prediction_features[numeric_feature_names])

# engineering categorical variables
prediction_features = pd.get_dummies(prediction_features, columns=categoricial_feature_names)

# view feature set
prediction_features

# add missing categorical feature columns
current_categorical_engineered_features = set(prediction_features.columns) - set(numeric_feature_names)
missing_features = set(categorical_engineered_features) - current_categorical_engineered_features
for feature in missing_features:
    # add zeros since feature is absent in these data samples
    prediction_features[feature] = [0] * len(prediction_features) 

# view final feature set
prediction_features

## predict using model
predictions = model.predict(prediction_features)

## display results
new_data['Recommend'] = predictions
new_data

	Name	OverallGrade	Obedient	ResearchScore	ProjectScore	Recommend
0	Henry	A	Y	90	85	Yes
1	John	C	N	85	51	Yes
2	David	F	N	10	17	No
3	Holmes	B	Y	75	71	No
4	Marvin	E	N	20	30	No
5	Simon	A	Y	92	79	Yes
6	Robert	B	Y	60	59	No
7	Trent	C	Y	75	33	No

	OverallGrade	Obedient	ResearchScore	ProjectScore
0	A	Y	90	85
1	C	N	85	51
2	F	N	10	17
3	B	Y	75	71
4	E	N	20	30
5	A	Y	92	79
6	B	Y	60	59
7	C	Y	75	33

	OverallGrade	Obedient	ResearchScore	ProjectScore
0	A	Y	0.899583	1.376650
1	C	N	0.730648	-0.091777
2	F	N	-1.803390	-1.560203
3	B	Y	0.392776	0.772004
4	E	N	-1.465519	-0.998746
5	A	Y	0.967158	1.117516
6	B	Y	-0.114032	0.253735
7	C	Y	0.392776	-0.869179

	ResearchScore	ProjectScore	OverallGrade_A	OverallGrade_B	OverallGrade_C	OverallGrade_E	OverallGrade_F	Obedient_N	Obedient_Y
0	0.899583	1.376650	1	0	0	0	0	0	1
1	0.730648	-0.091777	0	0	1	0	0	1	0
2	-1.803390	-1.560203	0	0	0	0	1	1	0
3	0.392776	0.772004	0	1	0	0	0	0	1
4	-1.465519	-0.998746	0	0	0	1	0	1	0
5	0.967158	1.117516	1	0	0	0	0	0	1
6	-0.114032	0.253735	0	1	0	0	0	0	1
7	0.392776	-0.869179	0	0	1	0	0	0	1

	ResearchScore	ProjectScore	OverallGrade_A	OverallGrade_F	Obedient_N	Obedient_Y
0	-1.127647	-1.430636	0	1	1	0
1	0.494137	1.160705	1	0	0	1