refactored-guacamole

📊 Data Analysis and Visualization

Data Science Project README.md Template

# 📊 Data Science Project Name

A brief description of your data science project.

![Python](https://img.shields.io/badge/Python-3.9+-blue.svg)
![License](https://img.shields.io/badge/License-MIT-green.svg)
![Status](https://img.shields.io/badge/Status-Active-success.svg)

## 📋 Table of Contents

- [About](#about)
- [Dataset](#dataset)
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)

## 🎯 About

This project analyzes [describe your data] to [describe your goal].

### Objectives
- Explore and visualize data patterns
- Build predictive models
- Generate actionable insights

## 📁 Dataset

| Feature | Description |
|---------|-------------|
| `feature_1` | Description of feature 1 |
| `feature_2` | Description of feature 2 |
| `target` | What we're predicting |

**Source:** [Dataset Link](https://example.com)

**Size:** 10,000 rows × 15 columns

## 🛠️ Installation

```bash
# Clone repository
git clone https://github.com/username/project-name.git
cd project-name

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

📦 Requirements

pandas==2.0.0
numpy==1.24.0
matplotlib==3.7.0
seaborn==0.12.0
scikit-learn==1.2.0
jupyter==1.0.0

🚀 Usage

# Quick start
from src.model import train_model
from src.data import load_data

# Load data
data = load_data('data/raw/dataset.csv')

# Train model
model = train_model(data)

# Make predictions
predictions = model.predict(new_data)

Run Jupyter Notebook

jupyter notebook notebooks/analysis.ipynb

📂 Project Structure

project-name/
│
├── data/
│   ├── raw/              # Original data
│   └── processed/        # Cleaned data
│
├── notebooks/
│   ├── 01_eda.ipynb      # Exploratory analysis
│   ├── 02_modeling.ipynb # Model building
│   └── 03_evaluation.ipynb
│
├── src/
│   ├── __init__.py
│   ├── data.py           # Data loading functions
│   ├── features.py       # Feature engineering
│   ├── model.py          # Model training
│   └── visualize.py      # Plotting functions
│
├── models/               # Saved models
├── reports/              # Generated reports
│   └── figures/          # Saved plots
│
├── requirements.txt
├── README.md
└── LICENSE

📊 Results

Model Performance

Model	Accuracy	Precision	Recall	F1-Score
Logistic Regression	0.85	0.83	0.87	0.85
Random Forest	0.92	0.91	0.93	0.92
XGBoost	0.94	0.93	0.95	0.94

Key Visualizations

Key Findings

1. Finding 1: Description of insight
2. Finding 2: Description of insight
3. Finding 3: Description of insight

🔧 Technologies Used

• Python - Programming language
• Pandas - Data manipulation
• NumPy - Numerical computing
• Matplotlib/Seaborn - Visualization
• Scikit-learn - Machine learning
• Jupyter - Interactive notebooks

🤝 Contributing

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