EDA

📊 Python EDA Portfolio

This repository contains a set of Exploratory Data Analysis (EDA) projects built using Python.
The projects focus on real-world datasets and show practical skills in data cleaning, feature engineering, handling missing values, and basic data visualization.

The purpose of this portfolio is to demonstrate how raw data is prepared and understood before applying machine learning models.

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📁 Projects Included

✈️ Flight Price Prediction - EDA

Main Focus: Working with date, time, and string-based features.

What was done

• Extracted Day, Month, and Year from the journey date using pandas datetime functions.
• Cleaned arrival and departure time columns by removing extra date information using split and lambda.
• Combined training and test datasets to apply the same preprocessing steps to both.

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🍔 Zomato Restaurant Data - EDA

Main Focus: Cleaning text data and analyzing restaurant details.

What was done

• Solved file reading issues by using latin-1 encoding for text-heavy data.
• Checked and analyzed missing values across multiple columns such as Cuisines.
• Studied how ratings relate to cost range and types of cuisines.

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🛍️ Black Friday Sales - EDA

Main Focus: Demographic data analysis and categorical variables.

What was done

• Converted age groups into numeric values for easier analysis.
• Encoded categorical variables like City_Category using One-Hot Encoding.
• Filled missing values in product-related columns and analyzed purchase behavior.

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🛠️ Tools and Libraries

Language

• Python 3.x

Libraries Used

• Pandas
• NumPy
• Matplotlib
• Seaborn

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🧠 Key Concepts Used

• Data Cleaning: Handling missing values and fixing inconsistent data.
• Feature Engineering: Creating new useful features from existing columns.
• String Processing: Using split(), lambda functions, and basic regex.
• Categorical Encoding: One-Hot Encoding and manual mapping.
• Date and Time Handling: Extracting useful parts from date columns.
• Visualization: Simple plots to understand distributions and relationships.

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📂 Folder Structure

EDA/
├── Black Friday - EDA/
│   ├── bf_eda.ipynb
│   └── train.csv
├── Flight Prediction - EDA/
│   ├── fp_eda.ipynb
│   ├── Data_Train.xlsx
│   └── Test_set.xlsx
└── Zomato - EDA/
    ├── zomato_eda.ipynb
    └── zomato.csv

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🙏 Acknowledgment

Special thanks to Krish Naik for the excellent educational content that helped in learning these EDA and Feature Engineering topics.

• Reference Tutorial: Complete Feature Engineering- EDA- Krish Naik