This project demonstrates how to perform feature engineering on various datasets using Python. Feature engineering is the process of transforming raw data into features that are suitable for machine learning models. It involves techniques such as data cleaning, imputation, encoding, scaling, normalization, feature selection, and feature extraction. It involves applying domain knowledge, statistical techniques, and creativity to extract relevant information from the data and create new variables that capture the underlying patterns or relationships.
To run this project, you need to have the following installed:
- Python 3.7 or higher
- Jupyter Notebook
- Pandas
- Numpy
- Scikit-learn
- Matplotlib
- Seaborn
You can install these packages using pip or conda.
The datasets used in this project are from the following sources:
Q: What is the purpose of feature engineering?
A: Feature engineering is a crucial step in machine learning, as it can improve the performance and interpretability of the models. By creating features that capture the underlying patterns and relationships in the data, feature engineering can help the models learn more effectively and generalize better to new data.
Q: What are some common feature engineering techniques?
A: Some common feature engineering techniques are:
- Data cleaning: removing or correcting invalid, missing, duplicate, or inconsistent data.
- Imputation: filling in missing values with reasonable estimates, such as mean, median, mode, or a constant value.
- Encoding: converting categorical variables into numerical values, such as one-hot encoding, label encoding, or ordinal encoding.
- Scaling: changing the range or distribution of numerical variables, such as standardization, normalization, min-max scaling, or log transformation.
- Normalization: making the data follow a standard distribution, such as Gaussian or uniform distribution.
- Feature selection: reducing the number of features by removing irrelevant, redundant, or noisy features.
- Feature extraction: creating new features from existing features by applying mathematical operations, such as polynomial features, interaction features, or principal component analysis.
Q: How to evaluate the quality of features?
A: There are several ways to evaluate the quality of features, such as:
- Visualizing the features using plots, such as histograms, boxplots, scatterplots, or correlation matrices.
- Calculating statistics and metrics, such as mean, standard deviation, skewness, kurtosis, variance inflation factor, or mutual information.
- Testing hypotheses and assumptions, such as normality test, independence test, or homoscedasticity test.
- Comparing the performance of different models using different sets of features.