This project provides a complete workflow for cleaning, imputing, evaluating, sorting, and visualizing datasets. It is designed to help data analysts explore and prepare data for deeper insights and modeling.
- ✅ Imputation – Handling missing values using statistical and model-based methods.
- 📊 Evaluation – Comparing imputation results and measuring their effectiveness.
- 🧽 Cleaning – Removing duplicates, fixing inconsistencies, and filtering out noisy data.
- 🔍 Sorting & Filtering – Organizing data for clearer analysis.
- 📈 Visualization – Generating informative charts using Matplotlib/Seaborn to identify trends.
- 🧑💻 Streamlit UI (optional) – Interactive interface to explore data visually.
if an error occur for the bokeh library:
pip install --force-reinstall --no-deps bokeh==2.4.3
do not forget:
pip install -r requirements.txt
- python
- streamlit
- pandas
- numpy
- sklearn
- matplotlib
- bokeh
- altair
- plotly
- io
This project is ideal for practicing:
Real-world data preprocessing
Missing value treatment
Evaluation of imputation strategies
Clear and clean visualization for storytelling