The objective of this project is to analyze a dataset containing information from two solar plants and to uncover relationships among the data features to give recommendations and inferences that would solve a business problem. The dataset consists of power generation data and weather sensor readings collected over a period of 34 days for each of the two solar plants.
The first step will be to perform exploratory data analysis (EDA) to understand the distribution, patterns, and relationships of the data features. This will include visualizing the data, identifying any outliers or anomalies, and calculating summary statistics.
Once the EDA is complete, feature engineering may be necessary to extract additional information from the data, such as time-based features or lagged variables.
Next, machine learning algorithms will be applied to find relationships among the data features and make predictions. This may include regression analysis, time-series analysis, or other appropriate techniques to determine which factors have the most significant impact on power generation.
Finally, recommendations and inferences will be made based on the findings of the analysis. This could include identifying opportunities to optimize power generation based on weather conditions, as well as any other insights that could improve the performance of the solar plants.
Overall, the goal of this project is to turn the data into actionable information that can help solve the business problem and drive operational improvements for the solar plants.