Exploratory Data Analysis Framework for Agricultural and Rental Data Using Machine Learning Techniques

Exploratory Data Analysis (EDA) plays a crucial role in understanding complex datasets and improving decision-making processes. This paper presents a comprehensive EDA framework designed to analyze agricultural and rental-related datasets. The proposed system focuses on transforming raw, unstructured data into meaningful insights through systematic data cleaning, statistical analysis, and visualization techniques. The dataset includes key parameters such as soil nutrients, weather conditions, crop types, yield, and rental usage patterns. To address real-world data challenges, the system incorporates methods for handling missing values, removing duplicates, detecting outliers, and normalizing data. Various visualization techniques, including histograms, scatter plots, box plots, and correlation heatmaps, are employed to identify patterns and relationships among variables. The implementation utilizes Python along with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn. The results demonstrate that the system effectively enhances data quality and provides clear insights into agricultural productivity and equipment demand trends. This approach not only improves data interpretation but also supports efficient preprocessing for machine learning applications. The proposed framework serves as a scalable and reliable solution for data-driven analysis in agriculture and related domains.