Capstone #2: K-Means and Random Forest Regression | Nidhi
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📊 Project Description: In this project, Nidhi conducts an analysis on power generation data from two plants, focusing on Plant 1. Using k-means clustering and random forest regression, she evaluates daily and total yields, DC power, and AC power. First, Nidhi scales the data, determines the optimal number of clusters using the elbow method, and visualizes the clustering results. She then employs random forest regression to predict DC power, splitting the data into training and testing sets (80/20). Performance is assessed using mean squared error and R-squared metrics, with results visualized through scatter plots to show the relationship between actual and predicted values.