Machine Learning
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May 23, 2026
Naive_Bayes_1_email_spam_filter Naive Bayes For Email Classification¶ In [8]: import pandas as pd from sklearn.naive_bayes import MultinomialNB from sklearn.model_selection import train_test_split...
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May 23, 2026
LDA_2_wine LDA : Reducing Dimension¶ Application of LDA in Modelling¶ In [ ]: import numpy as np from sklearn.datasets import...
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May 23, 2026
Linear Discriminant Analysis (LDA) is a supervised machine learning technique that projects high-dimensional data into a lower-dimensional space. It maximizes...
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May 23, 2026
Customer Segmentation Using PCA and K-Means PCA_1_Application_Kmeans-credit-card-customer-segmentation Clustering Credit Card Users¶ In this notebook, our main task is to cluster...
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May 23, 2026
How PCA Speeds Up Model Training Time Reduces data dimensionality: PCA eliminates redundant, highly correlated features from your dataset. Decreases...
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May 23, 2026
PCA Application Notebook Summary This notebook demonstrates how Principal Component Analysis (PCA) converts high-dimensional datasets into lower-dimensional visualizations while preserving...
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May 23, 2026
This notebook loads the breast cancer dataset and inspects its shape, duplicates, missing values, and class balance. It separates features...
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May 22, 2026
tut_PCA_1_implementation_using_numpy Example1) PCA 2D to 1D¶ In [1]: import numpy as np import matplotlib.pyplot as plt In [ ]: Create...
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May 22, 2026
We can use K-means clustering for labeling, but it is typically used for pseudo-labeling or semi-supervised learning rather than direct...
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May 22, 2026
This articles describes the Within Cluster Sum Of Squares, a metric that is used to determine the spread of the...
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