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# Machine Learning

## K-Means Clustering

1 K-Means Clustering Introduction to K-means Clustering¶K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable …

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## Hierarchical Clustering

2 – Hierarchical Clustering Hierarchical Clustering Analysis¶Clustering is the most common form of unsupervised learning, a type of machine learning algorithm used to draw inferences from unlabeled data. Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is …

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## Support Vector Classification

Support Vector Classification (SVC) is a machine learning algorithm used for binary and multi-class classification tasks. In this example, I’ll provide a step-by-step guide for implementing Support Vector Classification in Python using Scikit-Learn: Step 1: Import Libraries import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from …

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## K Nearest Neighbor Classification – KNN

K-Nearest Neighbors (KNN) is a supervised machine learning algorithm used for classification and regression tasks. In this example, I’ll provide a step-by-step guide for implementing KNN classification in Python using Scikit-Learn: Step 1: Import Libraries import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.neighbors import …

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## Naive Bayes Classification

Naive Bayes is a simple yet effective supervised machine learning algorithm commonly used for classification tasks. In this example, I’ll provide a step-by-step guide for implementing Naive Bayes classification in Python using Scikit-Learn. There are different variants of Naive Bayes, such as Gaussian Naive Bayes for continuous data and Multinomial …

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## Random Forest Classification

Random Forest Classification is an ensemble learning technique that combines multiple decision trees to improve classification accuracy and reduce overfitting. In Python, you can implement Random Forest Classification using Scikit-Learn. Here’s a step-by-step guide: Step 1: Import Libraries import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split …

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## Decision Tree Classification

Decision Tree Classification is a machine learning algorithm used for classifying data into multiple classes. In this example, I’ll provide a step-by-step guide for implementing Decision Tree Classification in Python using Scikit-Learn: Step 1: Import Libraries import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.tree …

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## Logistic Regression

Logistic Regression is a widely used machine learning algorithm for binary classification tasks, where the goal is to predict one of two possible classes (e.g., yes/no, spam/not spam). In Python, you can implement Logistic Regression using Scikit-Learn. Here’s a step-by-step guide: Step 1: Import Libraries import numpy as np import …

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## Random Forest Regression

Random Forest Regression is an ensemble learning technique used for predicting continuous numeric values. It combines multiple decision trees to reduce overfitting and increase prediction accuracy. In Python, you can implement Random Forest Regression using Scikit-Learn. Here’s a step-by-step guide: Step 1: Import Libraries import numpy as np import matplotlib.pyplot …

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