📊 Exploratory_Data_Analysis

A collection of detailed Exploratory Data Analysis (EDA) notebooks using Python, Pandas, Matplotlib, and Seaborn. The goal is to understand data patterns, distributions, correlations, and outliers across multiple datasets, laying the groundwork for effective machine learning and business intelligence.

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📂 Datasets Explored

Notebook	Dataset Description
EDA_1.ipynb	Red Wine Quality Dataset
EDA_2.ipynb	Student Performance Indicator
EDA_3.ipynb	Flight Prices Dataset
EDA_4.ipynb	Google Play Store Dataset
EDA_Task-1.ipynb	Miscellaneous/Additional Task

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🛠️ Key Tools & Libraries

• Python 3.10
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

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📌 Highlights

• Data Cleaning & Handling Missing Values
• Visualizing Distributions & Relationships
• Correlation Heatmaps
• Feature Engineering Insights
• Dataset-specific trends & outliers

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🚀 Getting Started

1. Clone the repo

   git clone https://github.com/Gaurabh007/Exploratory_Data_Analysis.git