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.

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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.

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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.

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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.

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Appendix

• Dataset: [bill_authentication.csv]