🦟 Dengue Data Cleaning & Validation

Data Preprocessing Pipeline using Python & Pandas

A professional data cleaning project focused on preparing a dengue outbreak dataset for reliable analysis and machine learning.

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📖 Overview

This project demonstrates a complete data cleaning and validation workflow for a dengue outbreak dataset.

The objective is to improve data quality, eliminate inconsistencies, and prepare the dataset for exploratory analysis, visualization, and predictive machine learning models.

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✨ Features

Feature	Status
🔍 Missing Value Analysis	✅
🧹 Duplicate Detection	✅
📝 Standardize Categorical Data	✅
🔢 Data Type Conversion	✅
📊 Outlier Detection (IQR)	✅
✔ Data Validation	✅
📄 Data Audit Report	✅

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🏗 Data Cleaning Workflow

Raw Dataset
      │
      ▼
Missing Value Analysis
      │
      ▼
Duplicate Detection
      │
      ▼
Data Type Conversion
      │
      ▼
Categorical Standardization
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      ▼
Outlier Detection
      │
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Data Validation
      │
      ▼
Clean Dataset

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🛠 Technologies Used

Technology	Purpose
🐍 Python	Data Processing
🐼 Pandas	Data Cleaning
🔢 NumPy	Numerical Operations
📊 Matplotlib	Visualization
📈 Seaborn	Exploratory Analysis

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📂 Project Structure

dengue-data-cleaning/

├── data/
│   ├── raw/
│   └── cleaned/
│
├── notebooks/
│   └── data_cleaning.ipynb
│
├── reports/
│   └── cleaning_report.pdf
│
├── screenshots/
│
├── requirements.txt
└── README.md

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🚀 Project Tasks

• Analyze missing values
• Detect duplicate records
• Standardize inconsistent labels
• Convert incorrect data types
• Identify outliers using the IQR method
• Validate cleaned data
• Generate a data quality report

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📊 Expected Outcome

✔ High-quality cleaned dataset

✔ Improved data consistency

✔ Reduced data errors

✔ Ready for machine learning

✔ Better analytical insights

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▶ Installation

Clone the repository

git clone https://github.com/YOUR_USERNAME/dengue-data-cleaning.git

Install dependencies

pip install -r requirements.txt

Run the notebook

jupyter notebook

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🎯 Skills Demonstrated

• Data Cleaning
• Data Validation
• Data Preprocessing
• Data Quality Assessment
• Exploratory Data Analysis (EDA)
• Python Programming
• Pandas
• Data Wrangling

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👨‍💻 Author

Tasnem Islam Prome

Python • Data Analytics • Machine Learning

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