Crop cultivation is crucial in the farming sector. Farmers strive hard in upbringing the yield by making their land fertile to meet the needs of the country. Due to years of farming, the soil has lost its fertility and results in low productivity. In order to overcome this factor chemical fertilizers came into lime light. Fertilizer is a natural or artificial substance that is applied on soil containing the chemical components that enhance plant growth and productivity. Monitoring the soil fertility and growth of the crop mainly depends on the pH of the soil, temperature and the moisture content in the soil. The proposed system utilizes the data set called Fertilizer Recommendation dataset in which all the above-mentioned factors are available. The class label considered in this dataset consists categorical values. As our class label contains categorical values Categorical Boosting algorithm is used in the proposed recommendation system. The Categorical Boosting Algorithm enhances the activity of recommendation. Our main aim is to help the farmers with a better and timely guidance by recommending the reasonable fertilizer for the land and yield considering the respective factors with the help of recommendation system. In this project the data of different parameters regarding the proper growth and their relative fertilizers pattern were collected, organized and summarized.