/-Implement-Machine-Learning-Models-using-Python

This study applies Decision Tree (98.54% accuracy) and K-Means clustering to financial data analysis, demonstrating their effectiveness for fraud detection and predictive modeling (Wirawan, 2023).

Primary LanguageJupyter Notebook

Implement Machine Learning Models using Python

Overview

This project demonstrates the implementation of various machine learning models on the bill_authentication.csv dataset. The tasks include data loading and cleaning, Decision Tree classification, K-Means clustering, and evaluation of classification and linear regression algorithms. The goal is to analyze financial data for predictive insights.

Tasks

Task 1: Data Loading & Cleaning

  • Objective: Load and preprocess the dataset to ensure it is ready for analysis.
  • Steps:
    1. Import necessary libraries (pandas).
    2. Load the dataset using pd.read_csv.
    3. Check for missing values using data.isnull().sum().
    4. Display basic statistical details with data.describe().
  • Outcome: The dataset is clean with no missing values, and preliminary insights are gathered.

Task 2: Decision Tree Classification

  • Objective: Implement a Decision Tree classifier to categorize the data.
  • Steps:
    1. Split the data into features (X) and target (y).
    2. Split the data into training and testing sets using train_test_split.
    3. Train the Decision Tree model (DecisionTreeClassifier).
    4. Evaluate the model using classification_report and accuracy_score.
  • Outcome: The model achieved an accuracy of 98.54%, demonstrating high performance.

Task 3: K-Means Clustering

  • Objective: Apply K-Means clustering to identify patterns in the data.
  • Steps:
    1. Determine the optimal number of clusters using the Elbow Method.
    2. Fit the K-Means model with the chosen number of clusters (n_clusters=3).
    3. Assign cluster labels to the dataset.
  • Outcome: The Elbow Method suggested 3 clusters, and the data was successfully segmented.

Task 4: Evaluate a Classification Algorithm

  • Objective: Assess the performance of the Decision Tree model using metrics.
  • Steps:
    1. Generate a confusion matrix.
    2. Calculate precision, recall, and F1-score.
  • Outcome: High precision (1.0), recall (0.967), and F1-score (0.983) indicate robust performance.

Task 5: Evaluate a Linear Regression Algorithm

  • Objective: Implement and evaluate a Linear Regression model.
  • Steps:
    1. Create a dummy target variable for regression.
    2. Split the data into training and testing sets.
    3. Train the Linear Regression model (LinearRegression).
    4. Evaluate using Mean Squared Error (MSE) and R-squared score.
  • Outcome: The model achieved an MSE of 0.189 and an R-squared score of 0.878, indicating a good fit.

Results

  • Decision Tree Classification: Accuracy of 98.54%.
  • K-Means Clustering: Optimal clusters identified (3).
  • Linear Regression: MSE of 0.189 and R-squared of 0.878.

Conclusion

This project highlights the effectiveness of machine learning models in analyzing financial data. The Decision Tree classifier performed exceptionally well, while K-Means clustering revealed meaningful patterns. The Linear Regression model also demonstrated strong predictive capabilities. These findings underscore the potential of machine learning in financial predictive analytics.

Appendix

  • Dataset: [bill_authentication.csv]