ds-practicals-guru 0.6.0

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

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

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Installation

pip install ds-practicals-guru

Quick Start

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:

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