Underfitting and Overfitting
First of all both underfitting and overfitting are not good characteristics of a machine learning model. So we go for
Category: Machine Learning
K Means Clustering Algorithm Implementation with Python
Here we considered a dataset of students with name, roll number and marks. You can find the dataset below Initially
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How to Determine the Optimal Number of Clusters – K Means Clustering
This is a very important question to be answered before we move further with k means clustering algorithm. If we
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K Means Clustering Solved Numerical
K means clustering is a unsupervised learning algorithm. NO target or dependent variable is mentioned in input data. Here we
Support Vector Machine
Support vector machines was initially designed for prediction of numeric and categorical outcomes. Today SVM is mostly used as a
Logistic Regression
Logistic regression is used when we have a categorical data (‘yes’,’no’,’true’,’false’,’0′,’1′,’positive’,’negative’), as in our below mentioned example where different ages
Linear Regression
We use Linear regression in situation where we need to predict or classify. In linear regression single or multiple independent
Training and Testing Data
The preprocessed data cannot be given to algorithms directly, before that we need to decide the Independent and Dependent variables
Data Preprocessing Techniques in Machine learning
Without data machine learning cannot be imagined, so its very important to provide appropriate data to machine learning algorithms. Firstly
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Visualization Of Data using Matplotlib
How to Plot a Line ? To answer the above question we need to import matplotlib library and use plot()