Welcome to my repository where I document my weekly explorations in data analysis and visualization. Every week, I pick a different dataset, dive deep into data cleaning, transformation, and visual exploration, and share my work as a Google Colab notebook.
This project is my personal journey to:
- Strengthen my data handling skills
- Explore various datasets across domains
- Apply different visualization techniques
- Build a consistent habit of weekly data exploration
Each week’s work is stored in a separate folder:
📁 Week-1_Titanic Dataset/
├── Data Handling and Visualisation on the Titanic Dataset.ipynb # Google Colab notebook
├── dataset.csv
📁 Week-2_Iris Dataset/
├── DHV on the Iris Dataset.ipynb
└── ...
Throughout this journey, I’ll be exploring:
- Data Cleaning: handling missing values, duplicates, and outliers
- Data Transformation: feature engineering, encoding, scaling
- Exploratory Data Analysis (EDA): statistical summaries & visual insights
- Visualization: histograms, scatter plots, heatmaps, interactive plots
- Tools & Libraries:
pandas,numpy,matplotlib,seaborn,plotly
- Python 3.x
- Jupyter / Google Colab
- pandas, numpy, matplotlib, seaborn, plotly
| Week | Dataset | Key Focus |
|---|---|---|
| 1 | Titanic | EDA & Basic Visualization |
| 2 | Iris Dataset | Cleaning & Feature Engineering |
| ... | ... | ... |
This is a personal learning project, but if you have interesting dataset suggestions or ideas for visualization challenges, feel free to open an issue or drop a comment.
- GitHub: [https://github.com/aditivermaa04]