K-Nearest Neighbors (KNN) is a simple, instance-based learning algorithm used for classification and regression tasks in machine learning. It's a non-parametric method because it doesn't make any assumptions about the underlying data distribution.
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Training Phase: In the training phase, the algorithm simply stores all the available data points and their corresponding class labels (in the case of classification) or target values (in the case of regression).
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Prediction Phase: When given a new, unlabeled data point, KNN calculates the distances between that point and all the points in the training dataset. The most common distance metric used is Euclidean distance, but other metrics like Manhattan distance or Minkowski distance can also be used.
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Finding Neighbors: After calculating distances, KNN identifies the K nearest neighbors to the new data point based on the chosen distance metric. "K" is a hyperparameter that you must specify before running the algorithm.
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Classification or Regression: For classification tasks, KNN assigns the class label that is most frequent among the K nearest neighbors. In the case of regression tasks, KNN predicts the average of the target values of the K nearest neighbors.
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Decision Rule: There are different decision rules that can be used for classification tasks, such as majority voting (simplest one), weighted voting (weights are assigned based on the distance to the query point), or distance-based ranking (assigning scores based on distance and ranking by score).
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Choice of K: The value of K determines how many neighbors are considered in the prediction. A small K might lead to overfitting, while a large K might lead to underfitting.
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Distance Metric: The choice of distance metric can significantly impact the performance of KNN. It's important to choose a metric that is appropriate for the given dataset and problem.
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Normalization: Since KNN relies on distance calculations, it's often beneficial to normalize the features to ensure that each feature contributes equally to the distance computation.
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Computational Complexity: KNN has a high computational cost during prediction, as it needs to compute distances to all training samples. This can be a drawback for large datasets.
KNN is a simple yet effective algorithm that can be a good choice for small to medium-sized datasets with low to moderate dimensionality. It's easy to implement and understand, making it a popular choice for beginners and as a baseline model for comparison with more complex algorithms.
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Iris Flower Classification:
- Dataset: Iris dataset (easily accessible from scikit-learn or UCI Machine Learning Repository)
- Task: Implement KNN for classifying iris flowers into different species based on features like sepal length, sepal width, petal length, and petal width.
- Skills: Data preprocessing, KNN implementation, model evaluation.
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Handwritten Digit Recognition:
- Dataset: MNIST dataset or USPS dataset
- Task: Use KNN to recognize handwritten digits (0-9) from images.
- Skills: Image processing, feature extraction, KNN implementation, performance evaluation.
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Breast Cancer Detection:
- Dataset: Breast Cancer Wisconsin (Diagnostic) dataset (UCI ML Repository)
- Task: Develop a KNN model to predict whether a tumor is benign or malignant based on features extracted from breast cancer images.
- Skills: Data preprocessing, model training, evaluation, dealing with imbalanced datasets.
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Credit Card Fraud Detection:
- Dataset: Credit card fraud detection datasets (e.g., Kaggle datasets)
- Task: Use KNN to identify fraudulent transactions based on transactional features.
- Skills: Data preprocessing, anomaly detection, dealing with imbalanced datasets, model evaluation.
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Movie Recommendation System:
- Dataset: MovieLens dataset (e.g., MovieLens 100K or MovieLens 1M)
- Task: Implement a basic recommendation system using KNN to recommend movies to users based on their past ratings and preferences.
- Skills: Collaborative filtering, user-item similarity, recommendation algorithms, KNN implementation.
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Predicting Housing Prices:
- Dataset: Housing price datasets (e.g., Boston Housing dataset)
- Task: Use KNN regression to predict housing prices based on features like location, number of rooms, crime rate, etc.
- Skills: Data preprocessing, regression modeling, feature engineering, model evaluation.
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Image Segmentation:
- Dataset: Medical imaging datasets (e.g., MRI brain images)
- Task: Implement KNN for image segmentation to identify different regions or structures within medical images.
- Skills: Image processing, segmentation techniques, KNN implementation, evaluation.
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Human Activity Recognition:
- Dataset: Human Activity Recognition Using Smartphones dataset (UCI ML Repository)
- Task: Use KNN to classify different activities (e.g., walking, running, sitting) based on smartphone sensor data.
- Skills: Time-series data processing, feature engineering, classification modeling, model evaluation.
These projects cover a wide range of applications and difficulty levels, allowing you to gradually progress from basic implementations to more advanced and complex projects involving real-world datasets and challenges.