Machine_Learning

Dimensanality reduction

Performed a dimensanality reduction using PCA and t-SNE

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data.

1. First we want Standardize the given Data.
2. Calculate the covariance matrix X of data points.
3. Calculate eigen vectors and corresponding eigen values.
4. Sort the eigen vectors according to their eigen values in decreasing order.
5. Choose first k eigen vectors and that will be the new k dimensions.
6. Transform the original n dimensional data points into k dimensions. Eigen Vectors: These vectors gives the in which direction the maximal spread occurs in the data.

To know more about Pca i written a blog in medium please visit below link

https://medium.com/analytics-vidhya/dimensionality-reduction-in-machine-learning-179ba0c83066

Linear Regression

Linear Regression

Linear regression is perhaps one of the most well known and well-understood algorithms in statistics and machine learning. Linear regression was developed in the field of statistics and is studied as a model for understanding the relationship between input and output numerical variables, but with the course of time, it has become an integral part of the modern machine learning toolbox.

Linear Regression is all about finding a line (or) plane that fits the given data as well as possible.

y=mx+b, Here m is the slope of the line and b is the y-intercept. it is the equation similar to algebra. But in statistics, the points do not lie perfectly on a line. it models around which the data lie if a strong line pattern exists.

For Complte overview of Linear Regression, i wrritten a blog in medium to read visit below link

'https://medium.com/analytics-vidhya/intuition-and-implementation-of-linear-regression-b1d2509d458e'