EDA & Feature Engineering Projects

This repository contains three Jupyter Notebook projects that demonstrate Exploratory Data Analysis (EDA) and Feature Engineering (FE):

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📊 1. Wine Quality EDA

• Dataset: Wine Quality
• Techniques Used:
  • Univariate & Bivariate Analysis
  • Correlation Heatmap
  • Feature Importance
• Objective: Understand factors affecting wine quality.

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✈️ 2. Flight Price Prediction

• Dataset: Flight Fare Details
• Techniques Used:
  • Date-Time Feature Extraction
  • Handling Categorical Features
  • Data Cleaning & Outlier Treatment
• Objective: Predict flight prices using cleaned and engineered features.

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📱 3. Google Playstore App Analysis

• Dataset: Google Playstore Apps
• Techniques Used:
  • Handling Missing Values
  • Feature Engineering (e.g., Install Count Binning)
  • Data Visualization using Seaborn/Matplotlib
• Objective: Analyze trends and patterns in app categories, ratings, and installs.

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🛠 Tools Used

• Python
• Pandas, Numpy
• Matplotlib, Seaborn
• Jupyter Notebook

📎 License

Open-source for learning and collaboration.