Crop Plantation Recommendation using Feature Extraction and Machine Learning Techniques

India is an agriculture-based economy with 18% of its total Gross Domestic Product (GDP) coming from different agricultural products. Agriculture 4.0 with modern technologies and robots for precision farming is shaping the future of agriculture in many places. In this research latest technologies like data science and machine learning algorithms are applied to understand different factors contributing to a profitable crop in India. These methods are applied on historical data collected from different Indian government web sites and publicly available data sets. This research provides a crop recommendation system with a prime motive of creating economic welfare of farmers. Multiple factors such as cost of planting, cost of harvesting, rainfall, crop demand, cost of seed, cost of fertilizer and yield of crop are considered to generate a more accurate prediction of whether a crop will be profitable or not.