Commodity Prediction: ML-Based Commodity Suggestion

The increasing unpredictability of climate conditions and the dynamic nature of global markets pose significant challenges to traditional agricultural practices. This study aims to develop a predictive model using machine learning and artificial intelligence to assist farmers in selecting profitable exotic crops. The model focuses on optimizing agricultural profitability by analyzing key factors such as weather conditions and region. Through the use of data-driven techniques, including a suggestion matrix and confusion matrix for recommendation, the model predicts the most suitable crops for specific regions. To ensure robust predictions, the pre-processing steps address critical issues such as missing values, outlier detection due to inconsistencies in state-scrapped data, and normalization to align with seasonal and harvesting cycles. Reliable weather data sources are also integrated to account for variance in climate conditions, which directly impacts crop yield. The proposed model, validated across various scenarios, aims to enhance agricultural efficiency by providing farmers with actionable insights, ultimately leading to higher yields and increased income through the cultivation of less-known, high-demand crops. This paper discusses the data pre-processing techniques, model validation strategies, and the practical implementation of machine learning algorithms in the agricultural sector, contributing to the advancement of smart farming practices.