Feature Engineering

This repository contains examples and techniques for feature engineering, focusing on improving dataset quality and enhancing model performance. It covers critical aspects such as Exploratory Data Analysis (EDA) and Interquartile Range (IQR) analysis for outlier detection and handling.

Features

This repository includes:

• Exploratory Data Analysis (EDA):
  • Understanding data distribution.
  • Summary statistics and visualizations.
  • Insights into data trends and anomalies.
• Outlier Detection using IQR:
  • Identification of outliers based on the interquartile range.
  • Strategies for outlier handling (e.g., capping, removal).
• Feature Engineering Techniques:
  • Handling missing values.
  • Data normalization and scaling.
  • Feature transformation and encoding.

Prerequisites

Ensure you have the following installed:

• Python 3.8+
• Required libraries:
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn

Install dependencies using:

pip install numpy pandas matplotlib seaborn  

How to Use

1. Clone the repository:

   git clone https://github.com/ashithapallath/Feature-Engineering.git  
   cd Feature-Engineering  

2. Explore the Jupyter Notebooks (*.ipynb):

   • Notebooks include step-by-step explanations and implementations.

3. Run the notebooks using:

   jupyter notebook  

4. Follow the instructions in each notebook to reproduce the analyses and techniques.

Techniques Overview

Exploratory Data Analysis (EDA)

• Summarizing data using:
  • Descriptive statistics (mean, median, standard deviation, etc.).
  • Data visualizations (histograms, box plots, scatter plots).
• Identifying patterns, trends, and anomalies in the data.

IQR-Based Outlier Detection

• Calculation of the interquartile range:

  Q1 = data['column'].quantile(0.25)  
  Q3 = data['column'].quantile(0.75)  
  IQR = Q3 - Q1  
  lower_bound = Q1 - 1.5 * IQR  
  upper_bound = Q3 + 1.5 * IQR  
  outliers = data[(data['column'] < lower_bound) | (data['column'] > upper_bound)]  

• Options for handling outliers:
  • Removing rows with outliers.
  • Capping values at lower and upper bounds.

---

Contribution

Contributions are welcome!

1. Fork the repository.
2. Create a branch for your feature or fix.
3. Submit a pull request with a description of your changes.

License

This project is licensed under the MIT License.

Acknowledgments

Special thanks to the open-source community for providing the tools and libraries that made this repository possible.