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.
| 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 |
- Python 3.10
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
- Data Cleaning & Handling Missing Values
- Visualizing Distributions & Relationships
- Correlation Heatmaps
- Feature Engineering Insights
- Dataset-specific trends & outliers
- Clone the repo
git clone https://github.com/Gaurabh007/Exploratory_Data_Analysis.git