We've Predicted the Energy production using Regression models. Used Python (Jupyter Notebook). We had used libraries like Pandas , Numpy , Matplotlib , Sea-born for Visualizations, and sklearn etc.
Did Exploratory data analysis (EDA) where we checked for null values, duplicate values, checked for correlation using pairplot and correlation matrix, scaled down the data. Built Different Regression models like Linear Regression, Random forest classifier, Decision tree Classifier, KNN, lasso and ridge Regression, XGBoost. Chose the best for using GridsearchCV with Highest accuracy Model.
Created a Web Page application using Stream-lit for the Energy production Prediction.We've Predicted the Energy production using Regression models. Used Python (Jupyter Notebook). We had used libraries like Pandas Numpy Matplotlib Sea-born for Visualizations, and sklearn etc.
Did Exploratory data analysis (EDA) where we checked for null values, duplicate values, checked for correlation using pairplot and correlation matrix, scaled down the data. Built Different Regression models like Linear Regression, Random forest classifier, Decision tree Classifier, KNN, lasso and ridge Regression, XGBoost.
Chose the best for using GridsearchCV with Highest accuracy Model. Created a Web Page application using Stream-lit for the Energy production Prediction.