Machine Learning Report

Task 1: K-Nearest Neighbors (KNN) Classification

Code Description: The first part of our machine learning journey involves the implementation of a K-Nearest Neighbors (KNN) classification model. Below, we provide a detailed report on this task.

Objective: The primary goal of this task was to build a KNN classifier capable of predicting the "class" of cars based on various attributes, including "buying," "maint," "door," "persons," "lug_boot," and "safety," using the provided "car.data" dataset.

Implementation:

1. Data Preparation: We initiated the task by importing essential libraries such as scikit-learn, Pandas, and NumPy. These libraries facilitated data manipulation, numerical operations, and machine learning implementation.

2. Data Loading: We loaded the dataset from "car.data" using Pandas, providing us with an overview of the dataset's structure and content.

3. Data Encoding: Since the dataset contains categorical data, we employed LabelEncoder from scikit-learn to convert these categorical attributes into numerical values. This encoding step is crucial for machine learning models to work effectively.

4. Data Splitting: To evaluate the model's performance, we divided the data into training and testing sets, allocating 90% of the data for training and 10% for testing. This separation is essential to ensure that the model generalizes well to unseen data.

5. Model Building: We chose the K-Nearest Neighbors (KNN) algorithm for classification. Our model used the training data to learn patterns and relationships between attributes and class labels.

6. Model Evaluation: After training, we evaluated the model's accuracy on the testing data. The accuracy metric helps us understand how well the model can predict the correct "class" of cars based on the provided attributes.

7. Predictions: We made predictions on a sample of the test data and compared these predictions to the actual "class" labels to gain insight into the model's performance.

Task 2: K-Means Clustering

Code Description: In the second part of our machine learning exploration, we delved into K-Means clustering. Below is a comprehensive report on this task.

Objective: The primary objective of this task was to apply K-Means clustering to the "digits" dataset, aiming to group similar digit images together and evaluate the quality of the clustering.

Implementation:

1. Data Preparation: We initiated the task by importing necessary libraries and loading the "digits" dataset. We scaled the dataset to ensure consistent feature scaling, a crucial step for clustering algorithms.

2. K-Means Clustering: We performed K-Means clustering on the scaled dataset using both 'k-means++' and 'random' initialization methods. This allowed us to explore different starting points for cluster centroids.

3. Quality Evaluation: We assessed the quality of the clustering using various metrics, including homogeneity, completeness, V-measure, adjusted Rand index, adjusted mutual information, and silhouette score. These metrics help measure the cohesion and separation of clusters.

4. Visualization: We employed PCA (Principal Component Analysis) to reduce the dimensionality of the dataset to two dimensions. This enabled us to visualize the clustering results in a two-dimensional space. We also plotted decision boundaries and cluster centroids to provide a clear visual representation of the clusters.

Conclusion: These two machine learning tasks provided valuable insights into classification and clustering techniques. The KNN classification model effectively predicted car classes based on attributes, achieving a commendable accuracy score. On the other hand, K-Means clustering successfully grouped similar digit images together, with various quality metrics helping assess the clustering performance. These tasks illustrate the versatility and practicality of machine learning in solving real-world problems.