📊 Data Analysis Projects

A curated collection of data analysis and machine learning projects implemented in Python, designed for learning, experimentation, and showcasing data-driven insights.

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📋 Table of Contents

1. Overview
2. Projects Included
3. Tech Stack & Libraries
4. Setup & Installation
5. Project Usage
6. Code Structure
7. Visualization & Reporting
8. Enhancement Ideas
9. Contributing
10. License

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💡 Overview

This repository houses a suite of Python-based data analysis and machine learning projects, using real or synthetic datasets. Each project focuses on a complete pipeline: data ingestion, cleaning, analysis, modeling, and visualization—ideal as portfolio pieces or learning templates.

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📁 Projects Included

• Exploratory Data Analysis (EDA)
  A step-by-step analysis on structured datasets, showcasing cleaning, summary statistics, and visual exploration.

• Machine Learning Models
  Classification, regression, and clustering examples using Scikit-Learn, with hyperparameter tuning and evaluation.

• Time Series Forecasting
  ARIMA or Prophet models for trend and seasonality analysis—complete with forecasting pipelines.

• NLP Text Analysis
  Sentiment analysis, topic modeling, and text preprocessing workflows.

(You can update project names and descriptions based on what's in your repo.)

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🧰 Tech Stack & Libraries

• Python 3.8+
  • pandas, NumPy for data manipulation
  • Matplotlib, Seaborn, Plotly for visualizations
  • scikit-learn for classic ML pipelines
  • statsmodels, Prophet for time series
  • nltk, spaCy for text analysis

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⚙️ Setup & Installation

git clone https://github.com/MisaghMomeniB/Data-Analysis-Projects.git
cd Data-Analysis-Projects
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

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🚀 Project Usage

Navigate into a project folder and run its main notebook or script:

cd project_name
jupyter notebook analysis.ipynb

Or for Python scripts:

python run_analysis.py --input data.csv --output results/

Customize parameters like dataset paths, model hyperparameters, or output destinations per project.

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📂 Code Structure

Data-Analysis-Projects/
├── project_1_Eda/
│   ├── data/
│   ├── notebooks/
│   └── requirements.txt
├── project_2_ml_classification/
│   ├── data/
│   ├── src/
│   │   ├── data_prep.py
│   │   ├── model.py
│   │   └── evaluate.py
├── project_3_time_series/
│   └── notebooks/
└── README.md

Each project typically includes:

• Raw and processed data/ folders
• Notebooks (.ipynb) or scripts (.py) for sequential steps: loading → cleaning → visualization → modeling → reporting
• requirements.txt or shared dependencies in root

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📊 Visualization & Reporting

• Statistical summaries (histograms, boxplots, correlation matrices)
• ML model diagnostics (ROC curves, confusion matrices)
• Forecast plots with trend and seasonality
• Interactive charts (optional Plotly or Bokeh)

Results are saved in reports/ or via notebook outputs for sharing or portfolio display.

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💡 Enhancement Ideas

• 🔄 Add automated pipeline runner for batch execution
• 📦 Package reusable modules (data preprocessing, model utilities)
• 🧠 Integrate hyperparameter tuning with GridSearchCV or Optuna
• 🔍 Add interactive dashboards using Streamlit or Dash
• 📝 Include model explainability, like SHAP value visualizations

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🤝 Contributing

Improvements and additional projects welcome!

1. Fork the repository
2. Add a new folder project_X_descriptive_name/
3. Add clean code, notebook, and a requirements.txt
4. Submit a Pull Request with an overview of your project

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📄 License

This repository is licensed under the MIT License—see LICENSE for details.