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https://github.com/ginga1402/demand-supply-analysis

Demand- Supply Analysis using Python
https://github.com/ginga1402/demand-supply-analysis

data-analysis data-science demand-supply-management driver-rider-relationship

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Demand- Supply Analysis using Python

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# Demand-Supply-Analysis
## Python3 | Numpy,Pandas,Matplotlib,Seaborn

## Introduction

The world of business is constantly evolving, with companies striving to understand and meet the demands of their customers while managing their supply chains effectively.
In this fast-paced and competitive landscape, the ability to analyze demand and supply trends is crucial for making informed decisions and maintaining a competitive edge.
To tackle this challenge, we present a project on Demand & Supply Analysis using Python.

Demand and Supply analysis means analyzing the relationship between the quantity demanded and the quantity supplied.
It helps businesses understand the factors influencing consumer demand to maximize profits.

image

## Objective

The objective of this project is to develop a comprehensive and data-driven approach to analyze the demand and supply dynamics within a given market or industry.
By leveraging the power of Python, a versatile programming language widely used in data analysis and visualization,
we aim to provide actionable insights to businesses, helping them optimize their operations and drive growth.

For the task of Demand and Supply analysis, we need a dataset based on demand for a product or service and supply for a product or service.

## Data Description

Cab services have become an essential part of urban transportation, with people relying heavily on these services for their daily commutes. Understanding the demand and supply patterns of cab services can help optimize their operations and provide a better user experience to customers.

Here is a dataset of the demand for rides and the supply of drivers in a particular city. Below are the features in the dataset:

Drivers Active Per Hour: Number of drivers active per hour.

Riders Active Per Hour: Number of Riders looking for rides.

Rides Completed: Number of rides completed.

![image](https://github.com/Ginga1402/Demand-Supply-Analysis/assets/130181481/5d681123-2e2c-4c7b-ba5c-4a2f6c83c8e3)