This repository is a comprehensive portfolio showcasing my practical projects built using Python. It focuses on solving real-world business problems through data manipulation, statistical analysis,extracting insights and Buliding Visualizations.
My work here demonstrates a commitment to robust, scalable, and reproducible data Analysis practices, underscoring my journey and growth in turning complex data into clear, actionable insights.
These projects collectively validate proficiency in the following tools and technical skills crucial for a modern data analyst:
| Library/Skill | Icon | Description |
|---|---|---|
| Data Manipulation | 🐼 | Pandas for efficient data cleaning, transformation, structuring, and complex feature engineering. |
| Numerical Computing | 🔢 | NumPy for high-performance array operations and mathematical functions integral to ML. |
| Statistical Modeling | 🧠 | Scikit-learn for implementing and evaluating various machine learning algorithms (Classification, Regression, Clustering). |
| Data Visualization | 📈 | Matplotlib and Seaborn for high-quality, customized statistical graphics and exploratory data analysis (EDA). |
I follow a disciplined, iterative, and accurate approach for every data-driven project to ensure reliable outcomes:
- Business Understanding 🎯: Clearly defining the problem, objective, and success metrics in collaboration with hypothetical or real stakeholders.
- Data Acquisition & Cleaning 🧹: Sourcing data (APIs, databases, files), handling outliers and missing values, and performing necessary feature preprocessing.
- Exploratory Data Analysis (EDA) 🔍: Using statistical plots to analyze data distributions, identify correlations, and extract preliminary insights.
- Feature Engineering & Selection 🧪: Creating informative new features and applying techniques like correlation matrices for optimal feature selection.
- Evaluation & Interpretation ✅: Delivering the most powerful insights through python visualizations .
To explore and run the analysis or scripts in this repository, you'll need Python 3.8+ installed. Follow these straightforward steps to quickly set up the required environment:
-
Get the Code ⬇️ Start by cloning this repository to your local machine using Git:
git clone [Your Repository URL]
-
Choose a Project 📂 Navigate into the folder of the specific project you want to explore .
-
Install Required Libraries 📦 Install all the necessary Python libraries (like Pandas, Scikit-learn, etc.). using
pip:pip install -r requirements.txt
-
Run the Analysis
▶️ Once the libraries are installed, you can open and run the primary file:- For Notebooks: Open the
.ipynbfile in JupyterLab or VS Code to step through the analysis .
- For Notebooks: Open the
I am actively seeking opportunities to apply my programming, data analysis, and machine learning skills in a professional setting.
- Portfolio: [https://portfolio-dina.lovable.app/]
- Email: [dinaelharedy00@gmail.com]
Thank you for your review. I welcome any questions or constructive feedback on the projects presented here!