Population Statistic in Malaysia is a Python Project which also include some Machine Learning technique.
Data presents Age-Specific Fertility Rates (ASFR) and Total Fertility Rates (TFR) for the years 1953 to 1969 in Semenanjung Malaysia/Peninsular Malaysia and the years 1970 to 2018 in Malaysia as a whole. The ASFR refers to the number of live births per 1,000 women in specific age groups, while the TFR represents the average number of children a woman would have during her reproductive years. The data is divided into two periods. The first period, from 1953 to 1969, focuses on Semenanjung Malaysia/Peninsular Malaysia, while the second period, from 1970 to 2018, covers the entire country of Malaysia.
What is the trend in total fertility rate and age-specific fertility rates among different age groups in Malaysia over the years?
Read the dataset: Load the dataset into a data structure (e.g., pandas DataFrame) for further analysis. Check for missing values: Identify if there are any missing values in the dataset and decide how to handle them (e.g., replacing with mean/median, dropping rows/columns). Rename columns, Handle data types and Remove unnecessary columns: Modify the column names to be more descriptive if needed. Ensure that the columns have the appropriate data types (e.g., numeric columns should be numeric, dates should be in the correct format). Remove any columns that are not relevant to the analysis.
Group the data by year: Group the dataset by year to analyze the trends over time Average Fertility Rates by Age Group, Grouping by time periods and calculating the maximum fertility rate, Group by time periods and calculate the minimum fertility rate.
Trends in Age-Specific Fertility Rates, Trends in Total Fertility Rate, The average fertility rates for each age group in Malaysia.
Linear Regression for Predicting Total Fertility Rate Based on Age Groups,
Cross-Validation,
Evaluation Metrics,
Residual Analysis,
Feature Importance
Dataset related to Malaysia population: The dataset contains information about Malaysia's population over the years, including demographic breakdowns by gender and ethnicity.
What are the trends and patterns in the population distribution of different ethnic groups by gender in Malaysia from 1966 to 2018 and predict 2019?
Read the dataset: Load the dataset into a data structure (e.g., pandas DataFrame) for further analysis, Check for missing values: Identify if there are any missing values in the dataset and decide how to handle them (e.g., replacing with mean/median, dropping rows/columns), Rename columns, Handle data types and Remove unnecessary columns: Modify the column names to be more descriptive if needed. Ensure that the columns have the appropriate data types (e.g., numeric columns should be numeric, dates should be in the correct format). Remove any columns that are not relevant to the analysis.
Group by specific columns: Group the data based on relevant columns (e.g., year, ethnicity) to aggregate and summarize the data.
Calculate statistics: Compute statistics such as mean, median, or sum within each group to gain insights into population trends or demographic characteristics.
Insights into the dataset growth rate comparison male and female through each ethnicity with specific year(1970,2003,2008 and 2018).
Show the trend in population growth over time or the change in demographic composition by gender or ethnicity, Compare population sizes or proportions between different ethnic groups or genders, Illustrate the distribution of the population among different ethnic groups, Population Distribution of Ethnic Groups by Gender, Comparing the life expectancy trends for different ethnic groups and genders in Malaysia over time, To compare the population distribution between males and females for each ethnic group, Chart provides insights into the most populous ethnicities within each category.
The Vector Autoregression (VAR) model in machine learning to predict the population distribution for different ethnic groups in the year 2019. The VAR model is trained using historical data and evaluated by comparing the predicted values with actual data.
Steps:
1.Import the required libraries, including statsmodels, sklearn.metrics, and sklearn.model_selection.
2.Perform feature engineering by extracting relevant features for prediction from the dataset.
3.Split the data into training and testing sets using the train_test_split function.
4.Select the VAR model and train it on the training data.
5.Forecast the population distribution for the testing data.
6.Evaluate the model's performance by calculating the Mean Squared Error (MSE).
7.Make predictions for the population distribution in 2019 using the trained model.
8.Compare the predicted values with the actual data for 2019 and calculate evaluation metrics such as MSE, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient.
How have demographic indicators, such as crude birth rate, crude death rate, total fertility rate, neonatal mortality rate, infant mortality rate, toddler mortality rate, and maternal mortality rate, changed over time from 1911 to 2020?
Read the dataset: Load the dataset into a data structure (e.g., pandas DataFrame) for further analysis, Check for missing values: Identify if there are any missing values in the dataset and decide how to handle them (e.g., replacing with mean/median, dropping rows/columns), Rename columns, Handle data types and Remove unnecessary columns: Modify the column names to be more descriptive if needed. Ensure that the columns have the appropriate data types (e.g., numeric columns should be numeric, dates should be in the correct format). Remove any columns that are not relevant to the analysis.
Calculate the average values for each column; Group the data by year and calculate the sum of each column for each year; Calculate the maximum and minimum values for each column; Group the data by decade and calculate the average values for each column within each decade; Calculate the annual percentage change in the Crude Birth Rate; Group the data by decade and calculate the average values for each column, including the percentage change in the Crude Birth Rate; Group the data by decade and calculate the percentage of years with a decreasing Crude Birth Rate within each decade; Find the year with the highest Crude Birth Rate and the year with the lowest Crude Death Rate; Calculate the total fertility rate for each year by summing the values from the columns 'Crude Birth Rate' and 'Crude Rate of Natural Increase'.
How have demographic indicator Crude Birth Rate changed over time from 1911 to 2019; How have demographic indicator crude death rate changed over time from 1911 to 2019; How have demographic indicator total fertility rate changed over time from 1911 to 2019; How have demographic indicators neonatal mortality rate, infant mortality rate, toddler mortality rate, and maternal mortality rate changed over time from 1911 to 2019
Evaluation of a Multivariate Regression Model for Predicting Maternal Mortality Rate
Time Series Plot of Demographic Indicators Over Time
Multivariate Regression Analysis of Maternal Mortality Rate
Cross-validation by manually splitting the data and fitting the OLS model multiple times.
What are the population trends across different age groups over time, and how does the average annual population growth rate vary for each age group and make prediction?
Read the Dataset; Check the structure and summary of the DataFrame; Check for missing values; Handle data types and Rename the columns;
Group by Year and Calculate the Sum of Total Population; Group by Year and Calculate the Average Annual Population Growth Rate; Group by Year and Calculate the Sum of Population in each Age Group; Group by Age Group and Calculate the Sum of Average Annual Population Growth Rate.
The population growth rate over time; Total Population Over Time; Population Composition by Age Groups; Population Growth Rate vs. Total Population;
Steps:
1.Import the required libraries, including statsmodels.tsa.holtwinters for Exponential Smoothing, and sklearn.metrics for evaluation metrics.
2.Convert the 'Year' column in the Population_Age dataset to datetime type and set it as the index.
3.Define the age groups of interest.
4.Loop over each age group and perform the following steps:
Extract the population data for the specific age group;
Fit the exponential smoothing model to the population data;
Predict the population for the next 'n' years using the fitted model;
Print the predicted population values.
5.Loop over each age group again for evaluation purposes and perform the following steps:
Extract the population data for the specific age group;
Fit the exponential smoothing model to the population data;
Predict the population for the next 'n' years using the fitted model;
Extract the actual values for the forecast period;
Calculate evaluation metrics such as MAE, MSE, and RMSE;
Print the evaluation metrics.