Wind energy, being clean and sustainable, has a significant potential to generate large-scale renewable energy. However, the intermittency and randomness of wind cause a huge barrier to the safety and control of the energy generation grid. Thus, constant forecasting of the wind data is crucial for efficient wind power generation. Lately, the support of machine learning (ML) models has been exploited in wind forecasting because of their quick and reliable predictions. However, reliable data is a primary requirement in the training of ML models. Since raw wind data is obtained from various sensors, the elimination of abnormal data and in-depth data analysis is essential to improve the quality of the data, thereby improving the model’s performance. This study, therefore, focuses on performing exploratory data analysis (EDA) on the ENGIE wind data to get deeper insights from the data and eliminate any futile information. Further, this study aims to identify the significant parameters in wind power prediction. From the results, it can be concluded that the performance of random forest regressor (RFR) and extra trees regressor (ETR) in the wind power prediction is excellent and that the wind speed is the only considerable parameter in the wind power prediction.