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https://github.com/quantum-software-development/statistical-measures

This repository contains a statistical analysis of customer service ratings for Biscobis Ltd
https://github.com/quantum-software-development/statistical-measures

machine-learning mathematics mathp mathplotlib measurements numpy pandas puthon3 seaborn statisctics

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This repository contains a statistical analysis of customer service ratings for Biscobis Ltd

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README 

        
Statistical Measures: Calculation in Excel and Python for Data Science



## Introduction



Welcome to the "Comprehensive Statistical Analysis of Biscobis Dataset" repository. This repository aims to conduct a detailed statistical analysis of the biscobis-statistical-measures.csv dataset, covering various measures such as mean, median, mode, minimum, maximum, range, variance, standard deviation, and coefficient of variation.



## Project Overview



This repository presents a statistical analysis of customer service ratings for Biscobis Ltd., based on a survey of 100 customers who evaluated seven different aspects of the company's services.



## Setup

To use this repository, ensure you have Python installed on your system along with the Pandas and NumPy libraries. Clone this repository and place your biscobis-statistical-measures.csv file in the root directory.



## Dataset 



### [Click here to download the dataset](dataset/bicobbis-statistical-measures.csv)



The dataset `biscobis-statistical-measures.csv` contains customer ratings for the following categories:



1. Shipping speed

2. Price level

3. Negotiation flexibility

4. Image

5. Services provided

6. Sales force

7. Product quality



## Python Code for Statistical Analysis



This project provides three Python scripts for analyzing Biscobis customer service data: a concise version for quick analysis, a comprehensive script for calculating statistical measures, and a detailed version for in-depth insights..



## Concise Python Code for Quick Analysis



This concise code quickly calculates and outputs the main statistical measures and is perfect for quick analyses or when you need a rapid overview of the data's statistical properties.



```python

import pandas as pd



# Load the dataset

data = pd.read_csv('biscobis-statistical-measures.csv', skiprows=2, encoding='latin1')



# Calculate statistical measures

statistics = data.describe().T

statistics['mode'] = data.mode().iloc[0]

statistics['coefficient_of_variation'] = (statistics['std'] / statistics['mean']) * 100



# Save the results to a CSV file

statistics.to_csv('statistical_measures.csv')



print(statistics)

```



### Comprehensive Python Code for Calculating Statistical Measures



Here is the Python script to calculate a comprehensive set of statistical measures:



```python

import pandas as pd



# Load the dataset

data = pd.read_csv('biscobis-statistical-measures.csv')



# Calculate comprehensive statistics

stats = {

    "Mean": data.mean(),

    "Median": data.median(),

    "Q1": data.quantile(0.25),

    "Q2": data.quantile(0.50),

    "Q3": data.quantile(0.75),

    "Mode": data.mode().iloc[0],  # Simplified mode; first mode only

    "Minimum": data.min(),

    "Maximum": data.max(),

    "Range": data.max() - data.min(),

    "Variance": data.var(),

    "Standard Deviation": data.std(),

    "Coefficient of Variation": data.std() / data.mean()

}

stats_df = pd.DataFrame(stats)



# Format the results for easy Excel import

formatted_stats = stats_df.applymap(lambda x: f"{x:.2f}")

formatted_stats.to_csv('formatted_statistical_data.csv', index=True)

print(formatted_stats)

```



### Comprehensive Python Code for Detailed Analysis



This comprehensive code provides detailed statistics and creates visualizations for deeper insights.



```python

import pandas as pd

import numpy as np

from scipy import stats

import matplotlib.pyplot as plt

import seaborn as sns



def calculate_statistics(data):

    return pd.Series({

        'Mean': np.mean(data),

        'Median': np.median(data),

        'Mode': stats.mode(data)[0][0],

        'Standard Deviation': np.std(data),

        'Variance': np.var(data),

        'Range': np.ptp(data),

        'Minimum': np.min(data),

        'Maximum': np.max(data),

        'Q1': np.percentile(data, 25),

        'Q3': np.percentile(data, 75),

        'Skewness': stats.skew(data),

        'Kurtosis': stats.kurtosis(data),

        'Coefficient of Variation': (np.std(data) / np.mean(data)) * 100

    })



# Load the dataset

df = pd.read_csv('biscobis-statistical-measures.csv', skiprows=2, encoding='latin1')



# Calculate statistics for each column

statistics = df.apply(calculate_statistics)



# Transpose the results for better readability

statistics_transposed = statistics.transpose()



# Display and save the results

print(statistics_transposed)

statistics_transposed.to_csv('biscobis_detailed_statistics.csv')



# Create visualizations

plt.figure(figsize=(12, 6))

sns.boxplot(data=df)

plt.title('Distribution of Ratings by Category')

plt.xticks(rotation=45)

plt.tight_layout()

plt.savefig('boxplot_biscobis.png')

plt.close()



plt.figure(figsize=(10, 8))

sns.heatmap(df.corr(), annot=True, cmap='coolwarm')

plt.title('Correlation Heatmap of Categories')

plt.tight_layout()

plt.savefig('heatmap_correlation_biscobis.png')

plt.close()



def create_histogram(data, column, bins=10):

    plt.figure(figsize=(8, 6))

    sns.histplot(data[column], bins=bins, kde=True)

    plt.title(f'Distribution of {column}')

    plt.xlabel('Value')

    plt.ylabel('Frequency')

    plt.savefig(f'histogram_{column.lower().replace(" ", "_")}.png')

    plt.close()



for column in df.columns:

    create_histogram(df, column)



print("Analysis complete. Results saved in CSV and PNG files.")

```



```python

import pandas as pd

import numpy as np

from scipy import stats

import matplotlib.pyplot as plt

import seaborn as sns



def calculate_statistics(data):

    # [Previous statistics calculation remains the same]



# Load the dataset

df = pd.read_csv('biscobis-statistical-measures.csv', skiprows=2, encoding='latin1')



# Calculate statistics for each column

statistics = df.apply(calculate_statistics)



# Transpose the results for better readability

statistics_transposed = statistics.transpose()



# Display and save the results

print(statistics_transposed)

statistics_transposed.to_csv('biscobis_detailed_statistics.csv')



# Create visualizations

plt.figure(figsize=(12, 6))

sns.boxplot(data=df)

plt.title('Distribution of Ratings by Category')

plt.xticks(rotation=45)

plt.tight_layout()

plt.savefig('boxplot_biscobis.png')

plt.show()  # Added to display the boxplot

plt.close()



plt.figure(figsize=(10, 8))

sns.heatmap(df.corr(), annot=True, cmap='coolwarm')

plt.title('Correlation Heatmap of Categories')

plt.tight_layout()

plt.savefig('heatmap_correlation_biscobis.png')

plt.show()  # Added to display the heatmap

plt.close()



def create_histogram(data, column, bins=10):

    plt.figure(figsize=(8, 6))

    sns.histplot(data[column], bins=bins, kde=True)

    plt.title(f'Distribution of {column}')

    plt.xlabel('Value')

    plt.ylabel('Frequency')

    plt.savefig(f'histogram_{column.lower().replace(" ", "_")}.png')

    plt.show()  # Added to display each histogram

    plt.close()



for column in df.columns:

    create_histogram(df, column)



print("Analysis complete. Results saved in CSV and PNG files.")

```



## Running the Analysis



To run either version of the code, follow these steps:

1. Ensure you have Python installed on your system.

2. Install the required libraries:

   - For the concise version: `pip install pandas`

   - For the comprehensive version: `pip install pandas numpy scipy matplotlib seaborn`

3. Place the `biscobis-statistical-measures.csv` file in the same directory as the Python script.

4. Run the script using Python.



## Note on Displaying Graphs



When running the comprehensive analysis script, you will now see the graphs displayed on your screen in addition to having them saved as PNG files. If you're running the script in a non-interactive environment (like a server or automated pipeline), you may want to comment out the plt.show() lines to prevent the script from hanging.