IMF Neural Dashboard &MacroVision By Prof.Eldirdiri F.Ibrahim et Dr.Afaf Babiker

Description of the Dataset The dataset used in this study was derived from publicly available IMF data, representing a snapshot of selected macroeconomic indicators across multiple countries. It was compiled to include recent historical data, covering variables that reflect economic health and government fiscal stability. The key features of the dataset include: Country – Name of the country to which the data point belongs. This enables cross-country comparisons. Year – The specific year for which the indicator values were recorded, allowing for longitudinal (time-series) analysis. gdp_usd_billion – Gross Domestic Product measured in billions of US dollars. This is a standard measure of economic output and is essential for comparing the relative size of economies. inflation_rate – Annual inflation rate expressed as a percentage, reflecting the rate at which the general level of prices for goods and services is rising, and subsequently, the erosion of purchasing power. unemployment_rate – Percentage of the labor force that is unemployed. This is a critical social and economic indicator of labor market health. government_debt_pct_gdp – Government debt as a percentage of GDP, which indicates the fiscal leverage of a country and its potential vulnerability to financial crises. The dataset was gathered from IMF reports and databases using structured downloads in CSV format, ensuring a clean, machine-readable dataset suitable for analysis in Python or other data analysis tools. Data cleaning steps included: Removing or imputing missing values to prevent modeling bias. Standardizing column names for consistency. Converting data types to numerical formats for statistical and neural network analysis. Scaling numeric features to ensure comparability for machine learning models. <class 'pandas.core.frame.DataFrame'> RangeIndex: 50 entries, 0 to 49 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 50 non-null int64 1 country 50 non-null object 2 year 50 non-null int64 3 gdp_usd_billion 50 non-null float64 4 inflation_rate 50 non-null float64 5 unemployment_rate 50 non-null float64 6 government_debt_pct_gdp 50 non-null float64 dtypes: float64(4), int64(2), object(1) memory usage: 2.9+ KB None أول 5 صفوف: id country year gdp_usd_billion inflation_rate unemployment_rate \ 0 8 Brazil 2015 1524.14 5.08 4.46 1 6 China 2015 1769.84 6.50 8.11 2 4 France 2015 2236.61 1.10 11.71 3 3 Germany 2015 1094.90 8.76 3.48 4 7 India 2015 2174.82 6.88 13.48 government_debt_pct_gdp 0数据集说明 本研究使用的数据集源自公开可获取的国际货币基金组织（IMF）数据，涵盖多国若干精选宏观经济指标的截面观测数据。该数据集收录了近期历史数据，覆盖反映经济运行状况与政府财政稳定性的各类变量。 本数据集的核心特征如下： 1. 国家（Country）：对应数据点所属国家的名称，可支持跨国对比分析。 2. 年份（Year）：指标数值对应的记录年份，支持纵向（时间序列）分析。 3. 国内生产总值（gdp_usd_billion）：以十亿美元为单位计量的国内生产总值，作为经济产出的标准衡量指标，是对比各国经济相对规模的核心依据。 4. 通货膨胀率（inflation_rate）：以百分比形式表示的年度通货膨胀率，反映商品与服务的总体价格上涨速率，以及由此带来的购买力缩水程度。 5. 失业率（unemployment_rate）：失业劳动力占总劳动力的百分比，是衡量劳动力市场健康状况的关键社会经济指标。 6. 政府债务占GDP比重（government_debt_pct_gdp）：政府债务占国内生产总值的比例，用于反映一国的财政杠杆水平及其面临金融危机的潜在脆弱性。 本数据集通过结构化下载方式从IMF报告及数据库中获取，格式为逗号分隔值（CSV），可生成干净且可被机器读取的数据集，适配Python或其他数据分析工具开展分析。数据清洗步骤包括： - 移除或插补缺失值，以避免建模过程产生偏差； - 统一标准化列名，保证数据集一致性； - 将数据类型转换为数值格式，适配统计分析与神经网络模型的训练需求； - 对数值型特征进行标准化缩放，确保机器学习模型可公平对比各特征的影响。 <class 'pandas.core.frame.DataFrame'> 范围索引：共50条数据，索引范围为0至49 数据列（共7列）： # 列名 非空计数 数据类型 --- ------ -------------- ----- 0 id 50 non-null int64 1 country 50 non-null object 2 year 50 non-null int64 3 gdp_usd_billion 50 non-null float64 4 inflation_rate 50 non-null float64 5 unemployment_rate 50 non-null float64 6 government_debt_pct_gdp 50 non-null float64 数据类型分布：float64类型4列，int64类型2列，object类型1列 内存占用：2.9+ KB 前5行数据： id country year gdp_usd_billion inflation_rate unemployment_rate government_debt_pct_gdp 0 8 巴西 2015 1524.14 5.08 4.46 1 6 中国 2015 1769.84 6.50 8.11 2 4 法国 2015 2236.61 1.10 11.71 3 3 德国 2015 1094.90 8.76 3.48 4 7 印度 2015 2174.82 6.88 13.48