ds-practicals 0.1.1

A collection of practical examples for data mining, visualization, and machine learning

Pandaas

A comprehensive collection of practical examples for data mining, visualization, and machine learning. This package contains 13 practical examples covering various topics in data science and machine learning.

Installation

pip install pandaas

Features

• 13 Practical Examples: Covering data analysis, visualization, and machine learning
• Easy Access: Each practical has a print_code() function to view the complete code
• Educational: Perfect for learning data science concepts

Usage

from pandaas import pract1, pract2, pract7

# Run a practical
pract1.print_code()  # Prints the entire code

# Or import and use directly
import pandaas.pract1 as p1
p1.print_code()

Available Practicals

Machine Learning (ML)

• pract1: Principal Component Analysis (PCA)
• pract2: Linear Regression Models Comparison
• pract3: Support Vector Machine (SVM) for Digit Classification
• pract4: K-Means Clustering
• pract5: Random Forest Classifier
• pract6: Q-Learning Reinforcement Learning

Data Mining and Visualization (DMV)

• pract7: Data Loading and Analysis
• pract8: Weather Data API and Visualization
• pract9: Data Cleaning and Preprocessing
• pract10: Data Filtering and Aggregation
• pract11: Air Quality Data Visualization
• pract12: Retail Sales Analysis by Region
• pract13: Time Series Analysis and Forecasting

Requirements

• Python >= 3.7
• pandas >= 1.3.0
• numpy >= 1.21.0
• matplotlib >= 3.4.0
• seaborn >= 0.11.0
• scikit-learn >= 1.0.0
• statsmodels >= 0.13.0
• requests >= 2.26.0

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.