refonte_paribas_ass1

This project demonstrates a complete data science workflow using the France FCHI CAC 40 dataset. The workflow includes data cleaning, exploratory data analysis (EDA), model building, and visualization.

Project Structure

project-name/
├── LICENSE
├── README.md
├── requirements.txt
├── data/
│   ├── processed/
│   │   ├── CAC 40 Historical Data.csv
|   |   └── CAC 40 History Data.xlsx
│   └── raw/
│       ├── CAC 40 Historical Data - Raw.csv
|       └── CAC 40 Historical Data.xslx
├── notebooks/
│   ├── 01_Data_Cleaning.ipynb
│   ├── 02_EDA.ipynb
│   ├── 03_Model_Building.ipynb
│   ├── 04_Visualisations.ipynb
│   ├── CAC 40 Historical Data.csv
│   └── CAC 40 Historical Data.xslx
├── results/
│   ├── FCHI_CAC_40_All_Plots.png
│   ├── FCHI_CAC_40_General_Plot.png
|   └── FCHI_CAC_40_Seaborn_Pairplot.png
└── scripts/
    ├── 01_Data_Cleaning_with_functions.py
    ├── 01_Data_Cleaning.py
    ├── 02_EDA_functions.py
    ├── 02_EDA.py
    ├── 03_Model_Building_functions.py
    ├── 03_Model_Building.py
    ├── 04_Visualisations_functions.py
    └── 04_Visualisations.py

Setup Instructions

Create Virtual Environment

1. Create Virtual Environment:

   python -m venv .venv

2. Activate Virtual Environment:

   • Windows:

     .venv\Scripts\activate

   • macOS and Linux:

     source .venv/bin/activate

3. Install Dependencies:

   pip install -r requirements.txt

Running Scripts and Notebooks

1. Navigate to Project Directory:

   cd path/to/project-name/

2. Run Scripts:

   • Data Cleaning:

     python scripts/01_data_cleaning.py

   • EDA:

     python scripts/02_EDA.py

   • Model Building:

     python scripts/03_model_building.py

   • Visualization:

     python scripts/04_visualisation.py

3. Run Jupyter Notebooks:

   jupyter notebook

   Open the desired notebook (e.g., 01_Data_Cleaning.ipynb, 02_EDA.ipynb, etc.).

Execution Order

Run the notebooks in the following order:

1. 01_Data_Cleaning.ipynb
2. 02_EDA.ipynb
3. 03_Model_Building.ipynb
4. 04_Visualisations.ipynb

Requirements

• Python 3.6+
• pandas
• seaborn
• matplotlib
• scikit-learn