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https://github.com/elachaabane/data-preparation-myocardial-infarction-complications
https://github.com/elachaabane/data-preparation-myocardial-infarction-complications
Last synced: 3 days ago
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- Host: GitHub
- URL: https://github.com/elachaabane/data-preparation-myocardial-infarction-complications
- Owner: ElaChaabane
- Created: 2024-11-01T21:28:56.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-01T22:05:53.000Z (2 months ago)
- Last Synced: 2024-11-01T22:36:25.573Z (2 months ago)
- Language: Jupyter Notebook
- Size: 5.02 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Data-Preparation-Myocardial-infarction-complications
This project focuses on data preparation for predicting complications arising from Myocardial Infarction. The notebook includes data exploration, visualization, cleaning, and transformation processes that are essential for improving model performance.
## Project Steps
1. **Explore and Visualize the Dataset**
- Identified patterns, trends, and anomalies
- Visualized key features of the data
2. **Data Cleaning and Transformation**
- Handled missing values
- Detected and treated outliers
- Transformed categorical and numerical features
- Performed feature engineering as necessary
3. **Standard Data Preparation Tasks**
- **Data Cleaning**: Identifying and correcting mistakes or errors in the data.
- **Feature Selection**: Identifying input variables that are most relevant to the task.
- **Data Transforms**: Changing the scale or distribution of variables.
- **Feature Engineering**: Deriving new variables from available data.
- **Dimensionality Reduction**: Creating compact projections of the data.
## Dataset
The dataset is available at the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Myocardial+Infarction+Complications).
## Deliverables
- A Jupyter notebook documenting:
- Business Understanding
- Data Understanding
- Data Exploration
- Data Preparation