Metadata-Version: 2.4
Name: ds-practicals-guru
Version: 0.6.0
Summary: A collection of Data Science practical Jupyter Notebooks with detailed explanations — covering data wrangling, descriptive statistics, regression, classification, and visualization.
Author: GURU
License: MIT
Project-URL: Homepage, https://github.com/guru/ds-practicals-guru
Keywords: data-science,machine-learning,pandas,scikit-learn,visualization,practicals,jupyter,notebooks
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Education
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3
Requires-Dist: numpy>=1.21
Requires-Dist: scikit-learn>=1.0
Requires-Dist: matplotlib>=3.4
Requires-Dist: seaborn>=0.11
Requires-Dist: jupyter>=1.0
Requires-Dist: nltk>=3.7
Dynamic: license-file

# ds-practicals-guru

A collection of **Data Science practical Jupyter Notebooks** with detailed explanations — covering data wrangling, descriptive statistics, regression, classification, and visualization.

## 📓 Included Notebooks

| # | Notebook | Topic | Dataset |
|---|----------|-------|---------|
| 1 | `Practical_1_Data_Wrangling.ipynb` | Data Wrangling — loading, inspecting, missing values, type conversion | Titanic |
| 2 | `Practical_2_Data_Wrangling_II.ipynb` | Data Wrangling II — missing values, outlier capping (Winsorisation) | Student data |
| 3 | `Practical_3_Descriptive_Statistics.ipynb` | Descriptive & grouped summary statistics | Iris |
| 4 | `Practical_4_Linear_Regression.ipynb` | Linear Regression — train, predict, evaluate (MSE, R²) | Boston Housing |
| 5 | `Practical_5_Logistic_Regression.ipynb` | Logistic Regression — binary classification, confusion matrix | Social Network Ads |
| 6 | `Practical_6_Naive_Bayes.ipynb` | Gaussian Naïve Bayes — multi-class classification | Iris |
| 8 | `Practical_8_Histogram.ipynb` | Histogram visualization with KDE | Titanic |
| 9 | `Practical_9_Box_Plot.ipynb` | Box plot visualization — grouped by gender/survival | Titanic |
| 10 | `Practical_10_Iris_Visualization.ipynb` | Histograms, boxplots, feature type identification | Iris |

Each notebook includes:
- ✅ Theory & concept explanations
- ✅ Step-by-step code with markdown headers
- ✅ Inline comments explaining each line
- ✅ Summary tables with key takeaways

---

## Installation

```bash
pip install ds-practicals-guru
```

## Quick Start

```python
import ds_practicals_guru

# Get the path to installed notebooks
notebooks_path = ds_practicals_guru.get_notebooks_path()
print(f"Notebooks are at: {notebooks_path}")

# List all available notebooks
for nb in ds_practicals_guru.list_notebooks():
    print(f"  📓 {nb}")
```

Then open the notebooks directory in **Jupyter Notebook**, **JupyterLab**, or **VS Code**:

```bash
jupyter notebook $(python -c "import ds_practicals_guru; print(ds_practicals_guru.get_notebooks_path())")
```

## Dependencies

- Python ≥ 3.8
- pandas ≥ 1.3
- numpy ≥ 1.21
- scikit-learn ≥ 1.0
- matplotlib ≥ 3.4
- seaborn ≥ 0.11
- jupyter ≥ 1.0

## License

MIT
