cs_internship_journey

The projects I work on in the CS internship program.

1. Zip Extractor Project

In Step 4, I had to write a program to extract and read the Forex data.
Zip Extractor from Scratch

Modules Used:

• os
• zipfile

Setup and Run Instructions:

1. Ensure you have Python installed on your system.
2. Clone the repository:

   git clone https://github.com/nargesghan/cs_internship_journey.git
   cd cs_internship_journey/zip\ extractor\ from\ scratch

3. Run the zip extractor script:

   python zip_extractor.py

Running Tests:

1. Navigate to the zip extractor from scratch directory:

   cd cs_internship_journey/zip\ extractor\ from\ scratch

2. Run the tests using unittest:

   python -m unittest zip_extractor_test.py

2. time series

In Step 5, I worked on the Forex data mentioned earlier and calculated the moving average of this data. Additionally, I practiced with the Seattle Bicycle Counts using Python Data Science Handbook by Jake VanderPlas. Modules Used:

modules Used:

• zipfile
• pandas
• numpy

Setup and Run Instructions:

1. Ensure you have Python installed on your system.
2. Clone the repository:

   git clone https://github.com/nargesghan/cs_internship_journey.git
   cd cs_internship_journey/time\ series(forex\ and\ bicycle\ counter)

3. Install the required dependencies:

   pip install pandas numpy

4. Run the Jupyter notebook:

   jupyter notebook Forex_Historical_Data-bicycle-counter.ipynb

3. pandas-numpy-matplotlib

In step 5, I prepared three learning materials for machine learning libraries. I used their cheat sheets and documentation as sources. These notebooks contain useful methods from these libraries, along with examples and sufficient explanations.

Setup and Run Instructions:

1. Ensure you have Python installed on your system.
2. Clone the repository:

   git clone https://github.com/nargesghan/cs_internship_journey.git
   cd cs_internship_journey/numpy_pandas_matplotlib

3. Install the required dependencies:

   pip install numpy pandas matplotlib

4. Run the Jupyter notebooks:

   jupyter notebook numpy_cheat_sheet.ipynb
   jupyter notebook pandas_cheat_sheet.ipynb
   jupyter notebook matplotlib_cheat_sheet.ipynb

Running Tests:

1. Navigate to the numpy_pandas_matplotlib directory:

   cd cs_internship_journey/numpy_pandas_matplotlib

2. Run the tests using unittest:

   python -m unittest test_numpy_cheat_sheet.py

4. simple-classification

In step 6, I implemented a simple one-layer neural network using the NumPy library. I used three different learning algorithms for this task: Perceptron, Adaline, and Adaline with stochastic gradient descent. I trained all of these algorithms using the Iris dataset. If you're interested in learning how to implement a single-layer neural network from scratch, don't miss this notebook.

Setup and Run Instructions:

1. Ensure you have Python installed on your system.
2. Clone the repository:

   git clone https://github.com/nargesghan/cs_internship_journey.git
   cd cs_internship_journey/simple-classification

3. Install the required dependencies:

   pip install numpy pandas matplotlib

4. Run the Jupyter notebook:

   jupyter notebook perceptron.ipynb

5. Dimensionality Reduction

In this project, I worked on data preprocessing and dimensionality reduction techniques.

Setup and Run Instructions:

1. Ensure you have Python installed on your system.
2. Clone the repository:

   git clone https://github.com/nargesghan/cs_internship_journey.git
   cd cs_internship_journey/Dimensionality\ Reduction

3. Install the required dependencies:

   pip install numpy pandas matplotlib scikit-learn

4. Run the Jupyter notebook:

   jupyter notebook Compressing_Data_via_Dimensionality_Reduction.ipynb

Running Tests:

1. Navigate to the Dimensionality Reduction directory:

   cd cs_internship_journey/Dimensionality\ Reduction

2. Run the tests using unittest:

   python -m unittest test_dimensionality_reduction.py