FaithMnisi/Maji-Ndogo-EDA-and-Visualization

Performed exploratory data analysis and data validation on agricultural and environmental data using Python. Analyzed climate variables, validated farm measurements against weather station data, and created visualizations to uncover patterns and support data-driven farming decisions. # Maji Ndogo Agricultural Data Exploration & Validation ## Overview This project explores agricultural and environmental data from the fictional country of Maji Ndogo to uncover patterns that influence crop production. Using Python, exploratory data analysis (EDA) and data validation techniques were applied to investigate relationships between environmental variables, assess data quality, and generate insights to support data-driven agricultural decision-making. ## Objectives This project aims to: * Perform exploratory data analysis on agricultural and environmental data. * Investigate the relationships between climate, soil, and crop yield. * Validate farm measurements against nearby weather station data. * Identify trends, patterns, and anomalies using statistical analysis and visualization. * Build confidence in the dataset before conducting further analysis. ## Dataset The dataset contains information on: * Crop types * Crop yield * Rainfall * Temperature * Soil pH * Elevation * Soil fertility * Weather station measurements Farm data is validated using measurements collected from nearby weather stations distributed across Maji Ndogo. ## Analysis Performed ### Data Preparation * Imported and cleaned agricultural data. * Handled missing values and inconsistencies. * Prepared datasets for analysis. ### Exploratory Data Analysis The analysis included: * Univariate analysis to understand variable distributions. * Multivariate analysis to explore relationships between environmental variables. * Analysis of categorical and continuous variables. * Investigation of crop-specific growing conditions. * Yield-focused analysis to identify factors associated with higher crop productivity. ### Data Validation To ensure the reliability of the dataset: * Farm measurements were compared with data from nearby weather stations. * Average climate measurements were analysed across corresponding locations. * Environmental variables were validated before drawing analy …