Feature Engineering Project

This repository contains Jupyter notebooks demonstrating various feature engineering and exploratory data analysis (EDA) techniques commonly used in machine learning projects.

Overview

Feature engineering is a crucial step in the machine learning pipeline that involves transforming raw data into features that better represent the underlying problem to predictive models, resulting in improved model accuracy on unseen data.

Getting Started

Prerequisites

1. Python 3.7 or higher
2. pip (Python package installer)
3. Jupyter Notebook or JupyterLab

Installation

1. Clone the repository

git clone https://github.com/Abhinavexists/Feature_Engineering.git

2. Navigate to the project directory

cd Feature_Engineering

3. Create a virtual environment (recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/Mac
python3 -m venv venv
source venv/bin/activate

4. Install required packages

pip install -r requirements.txt

Project Structure

Feature_Engineering/
├── Exploratory_data_analysis/
│   ├── Data_Cleaning.ipynb
│   ├── Flight_price.ipynb
│   ├── flight_price.xlsx
│   ├── googleplaystore.csv
│   ├── googleplaystorecleaned.csv
│   ├── Red_Wine.ipynb
│   └── winequality-red.csv
├── Handle_missing_values.ipynb
├── Handling_and_Outliers.ipynb
├── Handling_Imbalanced.ipynb
├── Label_Encoding.ipynb
├── LICENSE
├── OHE_Encoding.ipynb
├── Ordinal_Encoding.ipynb
├── README.md
├── SMOTE.ipynb
└── Target_guided_Ordinal.ipynb

Techniques Covered

1. Data Cleaning and EDA

• Data cleaning and preprocessing techniques
• Exploratory data analysis on various datasets:
  • Flight price prediction
  • Google Play Store apps
  • Red wine quality analysis

2. Feature Engineering Techniques

• Handling Missing Values
  • Various imputation methods
  • Deletion strategies
• Handling Outliers
  • Detection and treatment methods
  • IQR method implementation
• Handling Imbalanced Datasets
  • SMOTE (Synthetic Minority Over-sampling Technique)
  • Upsampling and downsampling methods

3. Encoding Techniques

• Label Encoding
• One-Hot Encoding (OHE)
• Ordinal Encoding
• Target-guided Ordinal Encoding

Example Use Cases

1. Flight Price Prediction

The repository includes a comprehensive example of feature engineering for flight price prediction, including:

• Date and time feature extraction
• Duration calculation
• Handling categorical variables
• Route analysis
• Stop information processing

Features processed include:

• Airline
• Source/Destination cities
• Departure/Arrival times
• Flight duration
• Number of stops
• Additional flight information

2. Red Wine Quality Analysis

This dataset focuses on Portuguese "Vinho Verde" red wine variants, analyzing physicochemical properties to predict wine quality. The analysis includes:

Dataset Features:

• Fixed acidity
• Volatile acidity
• Citric acid
• Residual sugar
• Chlorides
• Free sulfur dioxide
• Total sulfur dioxide
• Density
• pH
• Sulphates
• Alcohol
• Quality (score between 0 and 10)

Analysis Techniques:

• Correlation analysis between wine properties
• Distribution analysis of wine qualities
• Feature relationships visualization
• Outlier detection
• Quality prediction modeling

Code Examples

Feature Extraction from Datetime

# Split date into components
df[['Date','Month','Year']] = df['Date_of_Journey'].str.split('/',expand=True)
df[['Date', 'Month', 'Year']] = df[['Date', 'Month', 'Year']].astype(int)

Duration Processing

# Convert duration to minutes
df['Duration_Time'] = (df['Duration'].str.extract('(?:(\d+)h)?\s*(?:(\d+)m)?')
                      .fillna(0)
                      .astype(int)
                      .apply(lambda x:x[0]*60 + x[1], axis=1))

Categorical Encoding

from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder()
transformed = encoder.fit_transform(df[['Airline','Source','Destination']]).toarray()

Contributing

Feel free to contribute to this repository by:

1. Forking the repository
2. Creating a new branch for your feature
3. Submitting a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Abhinav Kumar Singh

• GitHub: @Abhinavexists