Predictive Modeling: Predicting-Wind-Power

Tech Stack: Python, Pandas, Numpy, Matplotlib, Bokeh, Scikitlearn, Statsmodels

Objective:

Apply machine learning techniques in Python to forecast wind power production.

Technical Specs:

Cleaned and organized 44,000-row, 10-column dataset with Pandas.

Conducted exploratory data analysis and visualization for insights.

Employed feature selection and train/test split for predictive modeling.

Utilized Scikit-learn and Statsmodels for multiple linear regression, yielding MAE of 0.14.

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Motivation:

Harnessing data science principles to bolster sustainable energy production initiatives.

Full Report:

https://hkmiyala52.wixsite.com/hmds/wind-project

Data Source:

https://www.kaggle.com/datasets/mubashirrahim/wind-power-generation-data-forecasting/data?select=Location1.csv