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Investor-Consumer Behavior Forecasting with Classification Models

Overview

This repository details the development of classification models to forecast investor-consumer behavior. The project involved organizing the development process, data cleaning, data preparation, and exploratory data analysis (EDA). Several classification models were trained, including Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine (SVM). The analysis demonstrated that the Decision Tree model outperformed the others, achieving an impressive F1 score of 60%.

Project Highlights

  • Model Development: Organized the development of classification models to forecast investor-consumer behavior.

  • Data Processing: Directed data cleaning, data preparation, and conducted exploratory data analysis (EDA).

  • Classification Models: Successfully trained multiple classification models, including:

    • Logistic Regression
    • K-Nearest Neighbors (KNN)
    • Naive Bayes
    • Decision Tree
    • Random Forest
    • Support Vector Machine (SVM)

  • Model Performance: Highlighted that the Decision Tree model exhibited superior performance with an impressive F1 score of 60%.