🧰 ML Collections

A curated collection of ready-to-use ML/Data Science code snippets and utilities

Stop rewriting the same exploratory data analysis code — just copy, paste, and explore! 🚀

Features • Installation • Usage • Scripts • Kaggle Resources • Contributing

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📖 About

ML Collections is a practical repository containing reusable Python scripts and utilities for machine learning and data science workflows. Whether you're working on Kaggle competitions or exploring datasets, this collection provides battle-tested code snippets that save time and reduce repetitive coding.

💡 Why This Repository?

• ⚡ Quick Start: Copy-paste ready code for common ML tasks
• 🎯 Practical: Real-world utilities used in actual Kaggle competitions
• 📊 Exploratory: Tools focused on dataset exploration and visualization
• 🔧 Modular: Each script is self-contained and easy to integrate
• 📚 Educational: Learn from curated Kaggle resources and techniques

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✨ Features

• Data Exploration Tools: Quickly analyze datasets with unique value counting and visualizations
• Distribution Analysis: Compare train/test distributions with KDE and box plots
• Dataset Utilities: Print directory trees and explore Kaggle dataset structures
• Kaggle Resources: Curated collection of valuable discussions and notebooks
• TensorFlow Setup: Pre-configured environment setup scripts

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🚀 Installation

Prerequisites

• Python 3.7 or higher
• pip package manager

Required Libraries

Install the necessary dependencies:

pip install pandas numpy matplotlib seaborn scikit-learn

For TensorFlow-based scripts:

pip install tensorflow

Clone the Repository

git clone https://github.com/Sanjidh090/ml-collections.git
cd ml-collections

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📝 Usage

Quick Start: Dataset Exploration

The simplest way to explore a dataset:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')

# Load your dataset
df = pd.read_csv('path/to/your/dataset.csv')

# Analyze unique values
for column in df.columns:
    print(f"="*100)
    print(f" Unique values in {column}:")
    print(df[column].value_counts())

print(f"=="*50)
print(f" Unique Value Report:")
print(df.nunique())

# Visualize distributions
df.hist(figsize=(16, 16), bins=30)
plt.tight_layout()
plt.show()

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📦 Scripts

1. unique.py - Unique Value Analysis

Purpose: Quickly analyze unique values and distributions in your dataset.

Features:

• Count unique values per column
• Display value frequency distributions
• Generate histogram visualizations

Usage:

# Analyze any CSV file
df = pd.read_csv('/path/to/dataset.csv')
# Run the unique value analysis (see script for details)

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2. plot_train_test_csv.py - Train/Test Distribution Comparison

Purpose: Compare feature distributions between training and test sets to detect data drift.

Features:

• Automatic label encoding for categorical variables
• KDE (Kernel Density Estimation) plots
• Box plots for outlier detection
• Train vs Test comparison for all features

Key Functions:

• convert_all_to_numeric(train, test): Converts all columns to numeric format
• dist_plots(train, test, num_features): Generates distribution comparison plots

Usage:

train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

# Convert and get numeric columns
tr_all, te_all, numeric_cols = convert_all_to_numeric(train, test)

# Plot distributions
dist_plots(tr_all, te_all, numeric_cols)

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3. intro.py - Environment Setup

Purpose: Standard setup script for TensorFlow-based Kaggle notebooks.

Features:

• TensorFlow version upgrade
• Protobuf compatibility fix
• CPU-only mode (CUDA disabled)
• Log level configuration
• Dataset file exploration

Usage:

# Run at the start of your Kaggle notebook
# Automatically lists first 5 files in /kaggle/input

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4. prompt_style_dataset.py - Directory Tree Visualization

Purpose: Print dataset directory structure in a clean ASCII tree format.

Features:

• Recursive directory traversal
• Beautiful ASCII tree formatting
• Error handling for permissions and missing paths

Usage:

from prompt_style_dataset import print_tree

# Print dataset structure
print_tree("/path/to/dataset")

Output Example:

Dataset structure for: /kaggle/working/
├── data
│   ├── train.csv
│   └── test.csv
└── models
    └── model_v1.pkl

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📚 Kaggle Resources

The kaggle/ directory contains a curated collection of valuable Kaggle discussions, notebooks, and insights:

• Pseudo-labelling Techniques (Pseudo-labelling___Chris_Deotte.md)
• Probability Calibration (Probab-Calibration-Broccoli-beef..md)
• Code Robustness Tips (Code_robust?.md)

These resources serve as a personal knowledge base for:

• 💡 Competition strategies and winning approaches
• 🔍 Advanced ML techniques
• 🛠️ Practical tips discovered across various competitions
• 📖 Concepts to revisit when they become relevant

Check out the Kaggle README for more details!

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🛠️ Additional Resources

• Markdown Cheatsheet: Quick reference for Markdown syntax

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🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-utility)
3. Commit your changes (git commit -m 'Add amazing utility')
4. Push to the branch (git push origin feature/amazing-utility)
5. Open a Pull Request

Guidelines

• Keep scripts self-contained and well-documented
• Add usage examples in comments
• Follow existing code style
• Test your code before submitting

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📄 License

This project is open source and available for educational and personal use.

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🙏 Acknowledgments

• Inspired by common Kaggle workflows and community best practices
• Built for the data science community to reduce repetitive coding
• Special thanks to Kaggle competition participants for sharing insights

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📬 Contact

Author: Sanjidh090

Repository: github.com/Sanjidh090/ml-collections

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