#Step 1: import required libraries

import pandas as pd

import numpy as np

import seaborn as sns

import statsmodels.api as sm

import matplotlib.pyplot as plt

from sklearn.metrics import confusion_matrix, accuracy_score

#Step 2: Import Dataset from Web Storage url "https://stats.idre.ucla.edu/stat/data/binary.csv"

data pd.read csv(url)

print("First 5 Records of Dataset")

print(data.head())

First 5 Records of Dataset

admit gre gpa rank

0

0 380 3.61

3

1

660 3.67

3

2

1 800 4.00

1

E

1

640 3.19

4

4

4

0520 2.93

4

#Step 3: Dataset Information print("\nDataset Information")

print(data.info())

print("\nStatistical Summary") print(data.describe())

print("\nMissing Values")

print(data.isnull().sum())

Dataset Information

pandas.core.frame.DataFram

Range Index: 400 entries, e to columns (total al 4 columns):

Colum Non-Null Count Dtype

rank

-null

100 non-nul 400 поп-null

dtypes: float64(1), int64(3) memory usage: 12.6 KB

None

Statistical Summary

floate

intoa

admit

count

400.000000 17500

0.466687

15.516536

0.380567

miri

0.000000 0.000000

0.000000

580.000

400.000000 700000

20. 520.000000

660.00000

400.000000

000

2.260000 3.130000

25%

.000

50%

3.395000

1.000

75%

Missing Values

nk

3.670000

1.000

ype: int64#Step 4: Convert Rank to Dummy Variables

rank_dummies pd.get_dummies (data['rank'], prefix='rank', drop_first-True)

data pd.concat([data, rank dummies], axis=1)

data data.drop('rank, axis=1)

print("\nDataset after creating dummy variables")

print(data.head())

Dataset after creating dummy variables

admit gre gpa

rank 2

rank 3

rank 4

e

0 380 3.61

False

True

False

1

1 660

3.67

False

True

False

2

1 800 4.00

False

False

False

1 640 3.19

False False

False

True

4

0520 2.93

False

True

#Step 5: Define Dependent and Independent Variables

X = data[[gre", "gpa', 'rank 2", "rank 3', 'rank_4"]]

X sm.add_constant(X)

data['admit']

Add constant

#Convert boolean columns to integers (e and 1) for statsmodels

X[['rank 2', 'rank 3', 'rank_4']] X[['rank_2', 'rank_3', 'rank_4']].astype(int)

#Step 6: Build Logistic Regression Mode

logit model sm. Logit(y, x)

result logit model.fit()

print("\nLogistic Regression Summary")

print(result.summary())

Optimization terminated successfully.

Current function value: 0.573147

Iterations 6

Logistic Regression Summary

Logit Regression Results

Dep. Variable:

Model:

Logit

Method:

MLE

Date:

Wed, 11 Mar 2026

Time:

13:55:19

converged:

True

admit

No. Observations:

Df Residuals:

Df Model:

Pseudo R-squ.:

Log-Likelihood:

LL-Null:

400

394

5

0.08292

-229.26

-249.99

Covariance Type:

nonrobust

LLR p-value:

7.578e-08

coef

std err

Z

P>z

[0.025

0.975]

const

-3.9900

1.140

-3.500

0.000

-6.224

-1.756

gre

0.0023

0.001

2.070

0.038

0.000

0.004

gpa

0.8040

0.332

2.423

0.015

0.154

1.454

rank 2

-0.6754

0.316

-2.134

0.033

-1.296

-0.055

rank 3

-1.3402

0.345

-3.881

0.000

-2.017

-0.663

rank 4

-1.5515

0.418

-3.713

0.000

-2.370

-0.733#Step 7: Predict Admission Probability predictions result.predict(x)

data['predicted_probability'] = predictions

print("\nPredicted Probabilities")

print(data[['admit', predicted_probability']].head())

Predicted Probabilities

admit

predicted_probability

0.172627

0.292175

0.738408

0.178385

0.118354

#Step 8: Convert Probability to Binary Class

data['predicted_admit'] = (data['predicted_probability'] >= 0.5).astype(int)

print("\nPredicted Admission")

print(data[['admit', 'predicted_admit']].head())

Predicted Admission

admit

predicted admit

1

1

2

1

3

1

4

0

1

0

#Step 9: Evaluate Model Performance

cm confusion_matrix(y, data['predicted_admit"])

accuracy accuracy_score(y, data['predicted_admit*])

print("\nConfusion Matrix")

print(cm)

print("\nModel Accuracy:", accuracy)

Confusion Matrix

[[254 19]

[97 30]]

Model Accuracy: 0.71

# Step 10: Visualization

plt.figure()

sns.scatterplot(x='gre', y='admit', data=data)

plt.title("GRE Score vs Admission")

plt.show()

plt.figure()

sns.scatterplot(x='gpa', y='admit', data=data)

plt.title("GPA vs Admission")

plt.show()