Python - Regression - Logistic - The Wahyudiharto's Blog

Python - Regression - Logistic

Friday, November 27, 2020

Data:

Employees when they sent job applicant (40 rows)

Mission:

How to predict the probability of someone will accepted from given gpa, gmat & work_experience

Library used:

Pandas
Matplotlib
Seaborn
Scikit

Code:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
import seaborn as sn
import matplotlib.pyplot as plt

url = 'https://raw.githubusercontent.com/kokocamp/vlog119/main/vlog119.csv'
vlog119 = pd.read_csv(url)
vlog119.describe()

X = vlog119[['gpa', 'gmat','work_experience']]
y = vlog119['admitted']
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)

logistic_regression= LogisticRegression()
logistic_regression.fit(X_train,y_train)
y_pred=logistic_regression.predict(X_test)

confusion_matrix = pd.crosstab(y_test, y_pred, rownames=['Actual'], colnames=['Predicted'])
sn.heatmap(confusion_matrix, annot=True)

from sklearn import metrics
print('Accuracy: ',metrics.accuracy_score(y_test, y_pred))
plt.show()

print (X_test) #test dataset
print (y_pred) #predicted values

new_candidates = {'gpa': [2,3.7,3.3,2.3,3],
                  'gmat': [590,740,680,610,710],
                  'work_experience': [3,4,6,1,5]
                  }

df2 = pd.DataFrame(new_candidates,columns= ['gpa','gmat','work_experience'])
y_pred=logistic_regression.predict(df2)

print (df2)
print (y_pred)

I wrapped the scenario in a Youtube video below.

Click this link (http://paparadit.blogspot.com/2020/11/the-algorithms-of-machine-learning.html), if you want to check out for other algorithms. Thank you for for visiting this blog & subs my channel.

Labels: Programming, Python