KMeans Business Problem

.history/011/k_means_beginner_20221007003708.pynb

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+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+import os
+import matplotlib.pyplot as plt
+from sklearn import cluster
+from sklearn import preprocessing
+import plotly.express as px
+from sklearn.datasets import make_blobs
+plt.style.use('dark_background')

.history/011/k_means_beginner_20221007003710.pynb

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+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+import os
+import matplotlib.pyplot as plt
+from sklearn import cluster
+from sklearn import preprocessing
+import plotly.express as px
+from sklearn.datasets import make_blobs
+plt.style.use('dark_background')

.history/011/k_means_beginner_20221007003736.pynb

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+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+import os
+import matplotlib.pyplot as plt
+from sklearn import cluster
+from sklearn import preprocessing
+import plotly.express as px
+from sklearn.datasets import make_blobs
+plt.style.use('dark_background')
+

.history/011/readme_20221007002656.md

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+# Problem Statement
+
+The aim of the exercise is to implement k-NN from scratch.
+The basic implementation is done for understanding.
+
+# Objective
+
+To understand how kNN works internally.
+
+# Task
+
+- Extend the algorithm for Distance-weighted kNN classification using appropriate dataset.
+- Extend the algorithm for regression using appropriate dataset.
+- Extend the algorithm with appropriate dataset.
+- Implementing KD trees to understand information retrieval. Visit [this](https://www.analyticsvidhya.com/blog/2017/11/information-retrieval-using-kdtree/) site for dataset and references.
+
+# k-NN Algorithm
+
+K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems.
+The following two properties would define KNN
+well −
+- Lazy learning algorithm − KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification.
+- Non-parametric learning algorithm − KNN is also a non-parametric learning algorithm because it doesn’t assume anything about the underlying data.
+
+K-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
+
+1. Load the data
+2. Initialize K to your chosen number of neighbors
+3. For each example in the data
+   - Calculate the distance between the query example and the current example from the data. 
+   - Add the distance and the index of the example to an ordered collection
+4. Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by the distances
+5. Pick the first K entries from the sorted collection 6. Get the labels of the selected K entries
+6. If regression, return the mean of the K labels
+7. If classification, return the mode of the K labels
+
+Here is a template notebook to get you started:
+
+`knn_starter_exercise.ipynb`
+
+[![Open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+[![View in nbviewer](https://github.com/jupyter/design/blob/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+
+### References
+- https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_knn_algorithm_finding_nearest_neighbors.htm

.history/011/readme_20221007002718.md

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+# Problem Statement
+
+The aim of the exercise is to implement k-Means from scratch.
+The basic implementation is done for understanding.
+
+# Objective
+
+To understand how kNN works internally.
+
+# Task
+
+- Extend the algorithm for Distance-weighted kNN classification using appropriate dataset.
+- Extend the algorithm for regression using appropriate dataset.
+- Extend the algorithm with appropriate dataset.
+- Implementing KD trees to understand information retrieval. Visit [this](https://www.analyticsvidhya.com/blog/2017/11/information-retrieval-using-kdtree/) site for dataset and references.
+
+# k-NN Algorithm
+
+K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems.
+The following two properties would define KNN
+well −
+- Lazy learning algorithm − KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification.
+- Non-parametric learning algorithm − KNN is also a non-parametric learning algorithm because it doesn’t assume anything about the underlying data.
+
+K-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
+
+1. Load the data
+2. Initialize K to your chosen number of neighbors
+3. For each example in the data
+   - Calculate the distance between the query example and the current example from the data. 
+   - Add the distance and the index of the example to an ordered collection
+4. Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by the distances
+5. Pick the first K entries from the sorted collection 6. Get the labels of the selected K entries
+6. If regression, return the mean of the K labels
+7. If classification, return the mode of the K labels
+
+Here is a template notebook to get you started:
+
+`knn_starter_exercise.ipynb`
+
+[![Open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+[![View in nbviewer](https://github.com/jupyter/design/blob/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+
+### References
+- https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_knn_algorithm_finding_nearest_neighbors.htm

.history/011/readme_20221007002722.md

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+# Problem Statement
+
+The aim of the exercise is to implement k-Means Clustering from scratch.
+The basic implementation is done for understanding.
+
+# Objective
+
+To understand how kNN works internally.
+
+# Task
+
+- Extend the algorithm for Distance-weighted kNN classification using appropriate dataset.
+- Extend the algorithm for regression using appropriate dataset.
+- Extend the algorithm with appropriate dataset.
+- Implementing KD trees to understand information retrieval. Visit [this](https://www.analyticsvidhya.com/blog/2017/11/information-retrieval-using-kdtree/) site for dataset and references.
+
+# k-NN Algorithm
+
+K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems.
+The following two properties would define KNN
+well −
+- Lazy learning algorithm − KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification.
+- Non-parametric learning algorithm − KNN is also a non-parametric learning algorithm because it doesn’t assume anything about the underlying data.
+
+K-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
+
+1. Load the data
+2. Initialize K to your chosen number of neighbors
+3. For each example in the data
+   - Calculate the distance between the query example and the current example from the data. 
+   - Add the distance and the index of the example to an ordered collection
+4. Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by the distances
+5. Pick the first K entries from the sorted collection 6. Get the labels of the selected K entries
+6. If regression, return the mean of the K labels
+7. If classification, return the mode of the K labels
+
+Here is a template notebook to get you started:
+
+`knn_starter_exercise.ipynb`
+
+[![Open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+[![View in nbviewer](https://github.com/jupyter/design/blob/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+
+### References
+- https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_knn_algorithm_finding_nearest_neighbors.htm

.history/011/readme_20221007002725.md

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+# Problem Statement
+
+The aim of the exercise is to implement K-Means Clustering from scratch.
+The basic implementation is done for understanding.
+
+# Objective
+
+To understand how kNN works internally.
+
+# Task
+
+- Extend the algorithm for Distance-weighted kNN classification using appropriate dataset.
+- Extend the algorithm for regression using appropriate dataset.
+- Extend the algorithm with appropriate dataset.
+- Implementing KD trees to understand information retrieval. Visit [this](https://www.analyticsvidhya.com/blog/2017/11/information-retrieval-using-kdtree/) site for dataset and references.
+
+# k-NN Algorithm
+
+K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems.
+The following two properties would define KNN
+well −
+- Lazy learning algorithm − KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification.
+- Non-parametric learning algorithm − KNN is also a non-parametric learning algorithm because it doesn’t assume anything about the underlying data.
+
+K-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
+
+1. Load the data
+2. Initialize K to your chosen number of neighbors
+3. For each example in the data
+   - Calculate the distance between the query example and the current example from the data. 
+   - Add the distance and the index of the example to an ordered collection
+4. Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by the distances
+5. Pick the first K entries from the sorted collection 6. Get the labels of the selected K entries
+6. If regression, return the mean of the K labels
+7. If classification, return the mode of the K labels
+
+Here is a template notebook to get you started:
+
+`knn_starter_exercise.ipynb`
+
+[![Open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+[![View in nbviewer](https://github.com/jupyter/design/blob/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+
+### References
+- https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_knn_algorithm_finding_nearest_neighbors.htm

.history/011/readme_20221007002732.md

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+# Problem Statement
+
+The aim of the exercise is to implement K-Means Clustering from scratch.
+The basic implementation is done for understanding.
+
+# Objective
+
+To understand how k works internally.
+
+# Task
+
+- Extend the algorithm for Distance-weighted kNN classification using appropriate dataset.
+- Extend the algorithm for regression using appropriate dataset.
+- Extend the algorithm with appropriate dataset.
+- Implementing KD trees to understand information retrieval. Visit [this](https://www.analyticsvidhya.com/blog/2017/11/information-retrieval-using-kdtree/) site for dataset and references.
+
+# k-NN Algorithm
+
+K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems.
+The following two properties would define KNN
+well −
+- Lazy learning algorithm − KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification.
+- Non-parametric learning algorithm − KNN is also a non-parametric learning algorithm because it doesn’t assume anything about the underlying data.
+
+K-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
+
+1. Load the data
+2. Initialize K to your chosen number of neighbors
+3. For each example in the data
+   - Calculate the distance between the query example and the current example from the data. 
+   - Add the distance and the index of the example to an ordered collection
+4. Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by the distances
+5. Pick the first K entries from the sorted collection 6. Get the labels of the selected K entries
+6. If regression, return the mean of the K labels
+7. If classification, return the mode of the K labels
+
+Here is a template notebook to get you started:
+
+`knn_starter_exercise.ipynb`
+
+[![Open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+[![View in nbviewer](https://github.com/jupyter/design/blob/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+
+### References
+- https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_knn_algorithm_finding_nearest_neighbors.htm

.history/011/readme_20221007002734.md

@@ -0,0 +1,45 @@
+# Problem Statement
+
+The aim of the exercise is to implement K-Means Clustering from scratch.
+The basic implementation is done for understanding.
+
+# Objective
+
+To understand how kmeans works internally.
+
+# Task
+
+- Extend the algorithm for Distance-weighted kNN classification using appropriate dataset.
+- Extend the algorithm for regression using appropriate dataset.
+- Extend the algorithm with appropriate dataset.
+- Implementing KD trees to understand information retrieval. Visit [this](https://www.analyticsvidhya.com/blog/2017/11/information-retrieval-using-kdtree/) site for dataset and references.
+
+# k-NN Algorithm
+
+K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems.
+The following two properties would define KNN
+well −
+- Lazy learning algorithm − KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification.
+- Non-parametric learning algorithm − KNN is also a non-parametric learning algorithm because it doesn’t assume anything about the underlying data.
+
+K-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
+
+1. Load the data
+2. Initialize K to your chosen number of neighbors
+3. For each example in the data
+   - Calculate the distance between the query example and the current example from the data. 
+   - Add the distance and the index of the example to an ordered collection
+4. Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by the distances
+5. Pick the first K entries from the sorted collection 6. Get the labels of the selected K entries
+6. If regression, return the mean of the K labels
+7. If classification, return the mode of the K labels
+
+Here is a template notebook to get you started:
+
+`knn_starter_exercise.ipynb`
+
+[![Open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+[![View in nbviewer](https://github.com/jupyter/design/blob/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+
+### References
+- https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_knn_algorithm_finding_nearest_neighbors.htm

.history/011/readme_20221007002736.md

@@ -0,0 +1,45 @@
+# Problem Statement
+
+The aim of the exercise is to implement K-Means Clustering from scratch.
+The basic implementation is done for understanding.
+
+# Objective
+
+To understand how k-means works internally.
+
+# Task
+
+- Extend the algorithm for Distance-weighted kNN classification using appropriate dataset.
+- Extend the algorithm for regression using appropriate dataset.
+- Extend the algorithm with appropriate dataset.
+- Implementing KD trees to understand information retrieval. Visit [this](https://www.analyticsvidhya.com/blog/2017/11/information-retrieval-using-kdtree/) site for dataset and references.
+
+# k-NN Algorithm
+
+K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems.
+The following two properties would define KNN
+well −
+- Lazy learning algorithm − KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification.
+- Non-parametric learning algorithm − KNN is also a non-parametric learning algorithm because it doesn’t assume anything about the underlying data.
+
+K-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
+
+1. Load the data
+2. Initialize K to your chosen number of neighbors
+3. For each example in the data
+   - Calculate the distance between the query example and the current example from the data. 
+   - Add the distance and the index of the example to an ordered collection
+4. Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by the distances
+5. Pick the first K entries from the sorted collection 6. Get the labels of the selected K entries
+6. If regression, return the mean of the K labels
+7. If classification, return the mode of the K labels
+
+Here is a template notebook to get you started:
+
+`knn_starter_exercise.ipynb`
+
+[![Open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+[![View in nbviewer](https://github.com/jupyter/design/blob/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/gimseng/99-ML-Learning-Projects/blob/master/010/exercise/knn_starter_exercise.ipynb)
+
+### References
+- https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_knn_algorithm_finding_nearest_neighbors.htm