Binary Logistic Regression in Python

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Binary Logistic regression is a statistical method used for binary classification, which means it predicts a binary outcome based on one or more predictor variables. In logistic regression, the dependent variable is categorical, representing two classes, and the goal is to determine the probability that a given input belongs to a particular class.

Unlike linear regression, which predicts a continuous outcome, logistic regression models the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function.

Here's how you can implement logistic regression using Python with the help statsmodels library in Python.
Statsmodels provides detailed summary statistics about the model. Here's how you can do it:

1. Import Required Libraries
# import the necessary libraries in your Python script:

import numpy as np
import pandas as pd

2. Load Your Data
Load your dataset into a Pandas DataFrame:

# Load data

data = {'oring' : [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
'temp': [53, 57, 58, 63, 66, 67, 67, 67, 68, 69, 70, 70, 70, 70, 72, 73, 75, 75, 76, 76, 78, 79, 81]}
df_challenger = pd.DataFrame(data)
print(df_challenger)

df_challenger['oring'].value_counts()

y = df_challenger['oring']
X = df_challenger['temp']

Add a constant (intercept) to the independent variables matrix

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3. Fit Logistic Regression Model
Fit the logistic regression model using statsmodels:

# Fit logistic regression model
model = sm.Logit(y, X_const)

# Get the results of the regression

4. Print Summary Statistics
Print the summary statistics of the logistic regression model:

including coefficients, standard errors, z-values, p-values, and more.

#The model equation is
predicted_logit = 15.0429 - 0.2322*X
predicted_logit