Comparative Analysis of Classification Models

This project focuses on comparing the performance of three popular machine learning models: Decision Trees, Support Vector Machines (SVM), and k-Nearest Neighbors (KNN) for the task of income prediction. The primary goal is to construct predictive models based on a dataset of individuals’ characteristics and income levels.

Key Project Steps:

Data Collection and Preprocessing:

• The dataset contains information about 32,561 individuals, with features related to income.
• Data preprocessing involves handling missing values, categorical encoding, and data standardization or normalization.
• Sklearn’s model library is utilized for pedestrian modeling.

Model Comparison and Analysis:

• The project involves training three machine learning models: Decision Trees, SVM, and KNN.
• Visual graphs and analysis are used to compare the performance of these models.
• Handling null values and encoding categorical data is a crucial aspect of data preparation.

Performance Evaluation:

• Performance evaluation is carried out using evaluation metrics such as accuracy, precision, recall, and F1-score.
• The confusion matrix is used to assess model performance.
• Sklearn’s libraries are leveraged for calculating these metrics.

Hyperparameter Tuning:

• Hyperparameter tuning is performed using sklearn’s capabilities to optimize each model’s performance.
• Results from the tuning process are reported and analyzed.

For more details and to explore the code repository, please visit GitHub Repository.