Skip to content

Latest commit

 

History

History
147 lines (114 loc) · 4.47 KB

README.md

File metadata and controls

147 lines (114 loc) · 4.47 KB
Raw

Python Projects

Welcome to my python_projects repository! This repository showcases various projects and exercises completed during different Data Science courses. Each project is organized in its own directory by topic and includes Jupyter Notebooks with all the necessary code and explanations.

Table of Contents

Introduction

This repository contains a collection of Python projects developed during Data Science courses. The projects cover a wide range of topics and complexity levels, from basic scripts to advanced applications, all documented in Jupyter Notebooks.

Repository Structure

The repository is organized as follows:

/python_projects
|-- /Pandas
|   |-- boxplot.ipynb
|   |-- cleaning_duplicate.ipynb
|   |-- combining_dataframes.ipynb
|   |-- dataframe.ipynb
|   |-- groupby.ipynb
|   |-- index.ipynb
|   |-- limpeza_duplicata.ipynb
|   |-- melt.ipynb
|   |-- merge_dataframe.ipynb
|   |-- missing_values.ipynb
|   |-- new_column.ipynb
|   |-- open_file.ipynb
|   |-- plot_graf.ipynb
|   |-- reshaping_data.ipynb
|   |-- selection_data.ipynb
|   |-- sort_data.ipynb
|   |-- sort_data.ipynb
|   |-- summarying_data.ipynb
|-- /TensorFlow
|   |-- Project1_ Celsius and Fahrenheit.ipynb
|   |-- Project2_ Forecasting of revenue (ice creams).ipynb
|   |-- Project3_Prediction of rent bike.ipynb
|   |-- Project4_ House Sales Prediction.ipynb
|   |-- Project5_ Sentiment Analysis.ipynb
|   |-- Project6_ Diabetes Classification.ipynb
|   |-- Project7_ CNN classifier.ipynb
|   |-- Project8_Traffic sign classification.ipynb
|   |-- Project9__Object_Classification.ipynb
|   |-- Project10_ Transfer learning classifier cat and dogs.ipynb
|   |-- Project11__Transfer_Learning_to_object_identification.ipynb
|   |-- Project12_Remove_noise_from_images(autoencoders).ipynb
|   |-- Project13_ Compress Images.ipynb
|   |-- Project14_ Deep_Dream.ipynb
|   |-- Project15__Image_Generation_(GAN).ipynb
|   |-- Project16__Text_Generation.ipynb

|-- .gitignore
|-- LICENSE
|-- README.md

Installation

To run the projects locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/eduardoafonso1089/python_projects.git

  2. Navigate to the project directory you want to explore:

    cd python_projects/Pandas

  3. (Optional) Create and activate a virtual environment:

    python -m venv env
source env/bin/activate  # On Windows, use `env\Scripts�ctivate`

  4. Install the project dependencies (if there is a requirements.txt file):

    pip install -r requirements.txt

Usage

To run the Jupyter Notebooks of a project, navigate to the project directory and start Jupyter Notebook. For example:

cd python_projects/Pandas
jupyter notebook

Open the desired .ipynb file in Jupyter Notebook to explore and execute the code.

Projects

Pandas

  • boxplot.ipynb: Creating box plots.
  • cleaning_duplicate.ipynb: Cleaning duplicates.
  • combining_dataframes.ipynb: Combining dataframes.
  • dataframe.ipynb: DataFrame basics.
  • groupby.ipynb: Using groupby function.
  • index.ipynb: Indexing data.
  • melt.ipynb: Using melt function.
  • merge_dataframe.ipynb: Merging dataframes.
  • missing_values.ipynb: Handling missing values.
  • multi_index.ipynb: Working with multi-index.
  • new_column.ipynb: Adding new columns.
  • open_file.ipynb: Opening files.
  • plot_graf.ipynb: Plotting graphs.
  • reshaping_data.ipynb: Reshaping data.
  • selection_data.ipynb: Selecting data.
  • sort_data.ipynb: Sorting data.
  • stack_unstack.ipynb: Stacking and unstacking data.
  • summarying_data.ipynb: Summarizing data.

TensorFlow

  • Project1_ Celsius and Fahrenheit.ipynb: Converting Celsius to Fahrenheit.
  • Project2_ Forecasting of revenue (ice creams).ipynb: Forecasting revenues from an ice Cream store.

Contribution

Contributions are welcome! Feel free to open issues and submit pull requests. To contribute:

  1. Fork the repository
  2. Create a branch for your feature (git checkout -b feature/MyFeature)
  3. Commit your changes (git commit -m 'Add MyFeature')
  4. Push to the branch (git push origin feature/MyFeature)
  5. Open a Pull Request

License

This project is licensed under the Unlicense License - see the LICENSE file for details.