The DataAnalyzer class is designed to streamline the process of loading, preprocessing, visualizing, and analyzing data, particularly in the context of classification problems. The class also includes methods for bias mitigation and implicit bias detection, making it a comprehensive tool for machine learning projects.
- Data Loading: Load CSV data into a pandas DataFrame.
- Preprocessing: Handle missing values, encode categorical variables, and prepare data for modeling.
- Data Visualization: Generate histograms to visualize the distribution of features against the target variable.
- Bias Mitigation: Resample data to address class imbalance and train a RandomForestClassifier.
- Model Evaluation: Evaluate the model's performance on resampled data using accuracy score and classification report.
- Implicit Bias Detection: Analyze and visualize potential biases in the features against the target variable.
Before using the DataAnalyzer class, ensure you have the following Python libraries installed:
- pandas
- matplotlib
- seaborn
- scikit-learn
You can install these libraries using pip:
pip install pandas matplotlib seaborn scikit-learnStart by importing the necessary libraries and initializing the DataAnalyzer class with your dataset path and target column name.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.utils import resample
from sklearn.preprocessing import LabelEncoder
from DataAnalyzer import DataAnalyzer
# Example Initialization
data_analyzer = DataAnalyzer(data_path="Employee.csv", target_column="LeaveOrNot")Use the load_data() method to load your dataset into a pandas DataFrame.
data_analyzer.load_data()This will print the first few rows of the dataset to the console.
The preprocessing() method handles missing values, encodes categorical variables, and separates features from the target variable.
data_analyzer.preprocessing()This will print the dataset's head before and after preprocessing to the console.
The visualize_data() method generates histograms to visualize the distribution of each feature against the target variable.
data_analyzer.visualize_data()This will display the histograms for each feature.
The bias_mitigation() method resamples the dataset to mitigate class imbalance and trains a RandomForestClassifier model.
data_analyzer.bias_mitigation()This will train the model and print a success message if training is successful.
Use the evaluate_resampled_model() method to evaluate the trained model on the test data.
accuracy = data_analyzer.evaluate_resampled_model()
print(f"Accuracy: {accuracy}")This will print the accuracy and classification report of the resampled model.
The check_implicit_bias() method analyzes and visualizes potential biases in the dataset.
data_analyzer.check_implicit_bias()This will display histograms and print statistical parity differences for each feature against the target variable.
Here's a complete example of how to use the DataAnalyzer class:
# Initialize the DataAnalyzer with your dataset
data_analyzer = DataAnalyzer(data_path="Employee.csv", target_column="LeaveOrNot")
# Load the dataset
data_analyzer.load_data()
# Preprocess the data
data_analyzer.preprocessing()
# Visualize the data
data_analyzer.visualize_data()
# Perform bias mitigation and train the model
data_analyzer.bias_mitigation()
# Evaluate the model
accuracy = data_analyzer.evaluate_resampled_model()
print(f"Accuracy: {accuracy}")
# Check for implicit bias
data_analyzer.check_implicit_bias()The DataAnalyzer class is a versatile tool for data analysis, preprocessing, visualization, and bias mitigation in machine learning projects. By following the steps outlined in this documentation, you can efficiently analyze your datasets and build more robust models with a focus on fairness and performance.