Data Analysis and Visualization Practice 📊

Welcome to my GitHub repository! This project contains various notebooks demonstrating my practice and exploration of data analysis, visualization, and preprocessing techniques using popular Python libraries like NumPy, Pandas, Matplotlib, Seaborn, and more.

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🚀 Key Topics Covered

1. Exploratory Data Analysis (EDA):

   • Feature_Flight_Price_Prediction_EDA.ipynb: Comprehensive EDA for flight price prediction, uncovering key insights and patterns.

2. Data Cleaning and Preprocessing:

   • Handlig_Missing_Values.ipynb: Techniques to handle missing values effectively in datasets.
   • Handling_SMOTE.ipynb: Using SMOTE (Synthetic Minority Oversampling Technique) to address class imbalance.

3. Data Imbalance Handling:

   • Handling_Imbalanced_Dataset.ipynb: Methods to balance datasets and improve model training.

4. Domain-Specific EDA:

   • Red_Wine_DtastEDA.ipynb: Analyzing the red wine dataset for insights into quality parameters.

5. Library-Specific Practice:

   • num.ipynb: Practice with NumPy for numerical computations.
   • pan.ipynb: Exploration of Pandas for data manipulation.
   • mat.ipynb: Visualization practices with Matplotlib.
   • sql.ipynb: Basic SQL operations and integration with Python.

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📂 Repository Structure

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├── Feature_Flight_Price_Prediction_EDA.ipynb  # Flight price prediction EDA
├── Handlig_Missing_Values.ipynb              # Handling missing values
├── Handling_Imbalanced_Dataset.ipynb         # Managing imbalanced datasets
├── Handling_SMOTE.ipynb                      # SMOTE implementation
├── Red_Wine_DtastEDA.ipynb                   # EDA for Red Wine dataset
├── mat.ipynb                                 # Matplotlib practice
├── num.ipynb                                 # NumPy practice
├── pan.ipynb                                 # Pandas practice
├── sql.ipynb                                 # SQL integration and queries
└── README.md                                 # This README file