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A collection of projects from the IBM Data Science Coursera specialization, showcasing skills in data analysis, machine learning, and visualization using Jupyter Notebooks and Python.

This project is part of the IBM Coursera specialization. It demonstrates core Python skills for data science, including data manipulation with Pandas, data visualization, and basic machine learning workflows. Developed using Jupyter Notebooks and real-world datasets.

Coursera IBM Advanced Data Science Specialization-Capstone Project. In this project, plant power output of a combined cycle power plant was predicted by using linear regression, decision tree and random forest regressors and neural networks. Firstly, the business problem and use case were identified. Then, required data to solve the business problem was specified. The data was extracted from the identified source and prepared for analysis by data wrangling. Exploratory data analysis was performed to have an idea of tackling the business problem. Feature engineering was done based on exploratory data analysis and further correlation recognition. Lastly, Machine learning, hyperparameter tuning and deep learning were used to build and optimize models to solve business problem i.e. coming up with a correlation to estimate plant power output based on ambient conditions data. This report has been generated as the main deliverable for this business problem that also contains the correlation.