Data Science
Published: 2024-04-01 18:16:41

# Exploring Population Trends in Saudi Arabia: A Data Science Journey

## Introduction:

In this notebook, we embark on a data science journey to explore population trends in different regions of Saudi Arabia. Using Python, we will analyze a dataset containing demographic information over several years. Our goal is to uncover insights that can help us understand population dynamics and trends in the country.

## Business Questions:

1. How has the population of Saudi Arabia evolved over the years?
2. What are the gender distribution patterns across different regions?
3. Which regions have experienced the highest population growth rates?
4. Can we identify any notable demographic shifts or trends?

## Data Understanding:

To begin our analysis, let's first understand the structure of our dataset. It contains information about the population categorized by gender, nationality, region, and year. Each record represents the population count for a specific demographic group in a particular region and year.

## Data Preparation:

Before diving into analysis, we need to prepare our data. This involves handling missing values, encoding categorical variables, and ensuring data consistency. Once our data is clean and ready, we can proceed with exploration.

## Data Exploration and Analysis:

Now, let's address our business questions through data visualization and analysis.

## Question 1: Population Evolution

We start by visualizing the overall population trend in Saudi Arabia over the years. A line graph will showcase the population growth or decline from 2010 to 2022.
  Cell In[3], line 5
    In this notebook, we embark on a data science journey to explore population trends in different regions of Saudi Arabia. Using Python, we will analyze a dataset containing demographic information over several years. Our goal is to uncover insights that can help us understand population dynamics and trends in the country.
       ^
SyntaxError: invalid syntax
import pandas as pd
import matplotlib.pyplot as plt

# Load data
population_data = pd.read_csv('/home/mac/Downloads/population2010-2022byregionnationalityandgendercsv-1.csv')

# Group data by year and calculate total population
population_evolution = population_data.groupby('Year')['Population'].sum()

# Plot population evolution
plt.figure(figsize=(10, 6))
population_evolution.plot(kind='line', marker='o', color='b')
plt.title('Population Evolution in Saudi Arabia (2010-2022)')
plt.xlabel('Year')
plt.ylabel('Population')
plt.grid(True)
plt.show()
No description has been provided for this image
Question 2: Gender Distribution
Next, we explore the gender distribution across different regions. A bar chart will display the proportion of males and females in each region.
  Cell In[6], line 1
    Question 2: Gender Distribution
             ^
SyntaxError: invalid syntax
# Code for gender distribution analysis
gender_distribution = population_data.groupby(['Region', 'Gender'])['Population'].sum().unstack()

# Plot gender distribution
gender_distribution.plot(kind='bar', stacked=True, figsize=(12, 8))
plt.title('Gender Distribution Across Regions')
plt.xlabel('Region')
plt.ylabel('Population')
plt.xticks(rotation=45)
plt.legend(title='Gender')
plt.show()
No description has been provided for this image
Question 3: Population Growth Rates
To identify regions with the highest population growth rates, we calculate the percentage change in population from 2010 to 2022 for each region.

Question 4: Demographic Trends
Finally, we analyze the dataset to uncover any notable demographic shifts or trends. This could include changes in the age distribution or shifts in population density across regions.

Conclusion:
Through our data exploration, we've gained valuable insights into population trends in Saudi Arabia. From understanding overall population evolution to analyzing gender distribution and growth rates across regions, we've uncovered important patterns that can inform decision-making and policy planning. By leveraging data science techniques, we can continue to explore and understand the complex dynamics of population demographics.
Special thanks to SAUDI OPEN DATA -data.gov.sa- for making this analysis possible.