IBM Data Science Course Projects

This repository contains my work and progress throughout the IBM Data Science course. It covers a variety of topics, from data visualization and regression modeling to web scraping and data engineering. Each notebook contains code, explanations, and results for different projects I’ve worked on.

Notebooks

• DA0101EN-5-Review-Model-Evaluation-and-Refinement.ipynb
  Review of model evaluation and refinement techniques.

• IBM Data Science - Extracting and Visualizing Stock Data.ipynb
  Project involving extraction and visualization of stock market data.

• IBM Data Science - Træning af polynomial regression - modeltraining og visualisering.ipynb
  Training and visualization of polynomial regression models.

• IBM Data Science - model development, sklearn.ipynb
  Model development using the sklearn library.

• IBM Data Science - regplot, boxplot, heatmat, pearson cofficient, ANOVA.ipynb
  Recap of various data visualization techniques including regplots, boxplots, and statistical tests.

• IBM Data Science - sklearn - polynomial regression - housing prices.ipynb
  Polynomial regression model applied to housing price prediction.

• IBM Data Science - webscraping, making ned dict from html .ipynb
  Web scraping and converting HTML data into dictionaries.

• IBM Data Science - Analyzing-a-real-world-data-set-with-SQL-and-Python.ipynb
  Analyzing a real-world dataset using SQL and Python.

• IBM data science - Data engineering - arbejde med forskellige datatyper og lave dataframes.ipynb
  Working with different data types and creating dataframes in data engineering.

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• SQL
• Web Scraping

Learning Objectives

• Gain hands-on experience with Python libraries for data science.
• Work with different types of datasets and perform data engineering tasks.
• Build and evaluate machine learning models.
• Visualize data using various plotting techniques.
• Apply web scraping to collect and process data.

License

This repository is licensed under the MIT License. See the LICENSE file for more information.