cv/101_Chi_Square_Test.py at master · igleekr/cv · GitHub

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun  2 06:54:29 2025

@author: kingsleylee
"""


# AB Testing - Our Task for ABC Grocery

import pandas as pd
from scipy.stats import chi2_contingency, chi2


# IMPORT DATA

campaign_data = pd.read_excel("grocery_database.xlsx", sheet_name = "campaign_data")


# FILTER OUR DATA

campaign_data = campaign_data.loc[campaign_data["mailer_type"] != "Control"]


# SUMMARISE TO GET OUR OBSERED FREQUENCIES

observed_values = pd.crosstab(campaign_data["mailer_type"], campaign_data["signup_flag"]).values


# To see in table format with labels:
observed_values = pd.crosstab(campaign_data["mailer_type"], campaign_data["signup_flag"])
print(observed_values)


mailer1_signup_rate = 123/ (252 + 123)
mailer2_signup_rate = 127/ (209 + 127)
print(mailer1_signup_rate, mailer2_signup_rate)


# STATE HYPOTHESIS & SET ACCEPTANCE CRITERIA

null_hypothesis = "There is no relationship between mailer type and signup rate. They are independent."
alternate_hypothesis = "There is a relationship between mailer type and signup rate. They are not independent."
acceptance_criteria = 0.05


# CALCULATE EXPECTED FREQUENCIES & CHI SQUARE STATISTIC

chi2_statistic, p_value, dof, expected_values = chi2_contingency(observed_values, correction = False)
print(chi2_statistic, p_value)


# FIND THE CRITICAL VALUE FOR OUR TEST

critical_value = chi2.ppf(1 - acceptance_criteria, dof)
print(critical_value)


# PRINT THE RESULTS (Chi Square Statistic)

if chi2_statistic >= critical_value:
    print(f"As our chi-square statistic of {chi2_statistic} is higher than our critical value of {critical_value} - we reject the null hypothesis, and conclude that: {alternate_hypothesis}")
else:
    print(f"As our chi-square statistic of {chi2_statistic} is lower than our critical value of {critical_value} - we retain the null hypothesis, and conclude that: {null_hypothesis}")


# PRINT THE RESULTS (p-value)

if p_value <= acceptance_criteria:
    print(f"As our p-value of {p_value} is lower than our acceptance criteria of {acceptance_criteria} - we reject the null hypothesis, and conclude that: {alternate_hypothesis}")
else:
    print(f"As our p-value of {p_value} is higher than our acceptance criteria of {acceptance_criteria} - we retain the null hypothesis, and conclude that: {null_hypothesis}")