- Contians code of the various projects done during IBM Data Science specialization
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The main focus areas of this capstone project include:
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Web Scraping with BeautifulSoup: Extracting data from websites using Python and BeautifulSoup library.
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Data Accessing with SQL: Leveraging SQL queries along with Python to access and manipulate data from databases.
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Working with APIs using Python: Interacting with Application Programming Interfaces (APIs) to retrieve data from external sources.
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Data Visualization: Visualizing data using libraries like Matplotlib and Seaborn to gain insights and present findings effectively.
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Data Preprocessing and Cleaning: Cleaning and preparing data for analysis by handling missing values, outliers, and other data quality issues.
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Exploratory Data Analysis (EDA): Conducting in-depth exploratory analysis to understand the underlying patterns and relationships within the data.
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Machine Learning Algorithms Implementation: Implementing various machine learning algorithms for classification, regression, clustering, and other tasks.
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Model Evaluation and Selection: Evaluating the performance of machine learning models using appropriate metrics and selecting the best model for the given problem.
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Predictive Analytics: Applying predictive modeling techniques to make accurate predictions and forecast future outcomes.
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Deep Learning Models: Building and training deep learning models using frameworks like TensorFlow and Keras.
The following technologies and libraries are commonly used throughout the projects:
- Python: The primary programming language for data manipulation, analysis, and machine learning.
- Jupyter Notebook: An interactive development environment for executing code, visualizing data, and documenting the analysis process.
- Pandas: A powerful library for data manipulation and analysis, providing easy-to-use data structures and data analysis tools.
- NumPy: A fundamental library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices.
- Matplotlib: A plotting library for creating static, animated, and interactive visualizations in Python.
- Seaborn: A data visualization library based on Matplotlib, providing additional statistical and aesthetic enhancements.
- Scikit-learn: A comprehensive machine learning library for Python, offering a wide range of algorithms and tools for data modeling and analysis.
- TensorFlow: An open-source deep learning framework for building and training neural networks.
- Keras: A high-level neural networks API that runs on top of TensorFlow, simplifying the process of building deep learning models.
- SQL: A standard language for accessing and manipulating relational databases.
- BeautifulSoup: A Python library for web scraping and extracting data from HTML and XML documents.
- Requests: A library for making HTTP requests and interacting with web APIs.
Feel free to explore the individual project folders to learn more about each project and the specific tools and techniques utilized.
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To get started with the projects in this repository, follow these steps:
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Clone the repository to your local machine
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Install the necessary dependencies and libraries by running the appropriate commands or setting up a virtual environment.
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Open the Jupyter Notebook files (.ipynb) in your preferred environment (e.g., Jupyter Notebook, JupyterLab).
Follow the instructions within each project notebook to execute the code, analyze the data, and explore the results.
Please refer to the specific project folders and notebooks for additional instructions and details on how to run and utilize each project.
Here are some potential areas for future enhancements:
- Explore advanced machine learning techniques and algorithms to further improve model performance and accuracy.
- Incorporate more extensive data preprocessing and feature engineering techniques to enhance the quality of input data.
- Utilize cloud-based solutions and distributed computing frameworks for handling and processing larger datasets efficiently.
- Experiment with different hyperparameter tuning strategies to optimize model performance.
- Integrate the projects with web applications or create interactive dashboards for easy data exploration and visualization.
- Incorporate more real-world datasets and problem scenarios to gain practical experience in various domains.
- Collaborate with other data science practitioners and researchers to enhance the projects and explore new methodologies.
- Continuously update and enhance the documentation to ensure clarity, reproducibility, and ease of understanding for future users.
- Feel free to contribute to this repository by implementing new projects, improving existing code, or suggesting enhancements. Open-source collaboration is always welcome!