This repository contains a collection of my data analysis projects, demonstrating my skills in data cleaning, exploration, visualization, and modeling.
The projects are organized into folders, each representing a separate analysis or case study.
AlexTheAnalyst Challenge: A data analysis challenge organised by popular data expert/tutor Alex Freibergend-to-end EDA with Excel: Exploratory Data Analysis featuring data cleaning, data wrangling and visualization done in Excel.Webscraping: Basic webscraping exercise.-
- etc...
This portfolio showcases my proficiency in the following areas:
- Data Cleaning and Preprocessing: Handling missing values, outliers, and data transformations.
- Exploratory Data Analysis (EDA): Visualizing and summarizing data to gain insights.
- Data Visualization: Creating compelling visualizations using libraries like Matplotlib, Seaborn, and Plotly.
- Statistical Analysis: Conducting statistical tests and interpreting results.
- Machine Learning: Building and evaluating predictive models using scikit-learn.
- Python Programming: Using libraries such as Pandas and NumPy for data manipulation.
- Database interaction: Utilizing SQL to query databases.
Each project folder contains the following:
- Jupyter Notebooks or Python scripts (.ipynb, .py): The code used for the analysis.
- Datasets (.csv, .xlsx, .json): The data files used in the analysis.
README.md: A detailed description of the project, including the problem statement, methodology, and results.- Visualizations (.png, .jpg): Images of key charts and graphs.
- Any supporting documents.
To explore a specific project:
- Navigate to the project's folder within this repository.
- Open the corresponding
README.mdfile for a detailed overview. - View the Jupyter Notebooks (.ipynb) files to examine the code and analysis.
Feel free to reach out to me if you have any questions or would like to discuss my work.
Thank you for your interest!