In this blog, I am going to analyze handwritten digits using Support Vector Machine (SVM). SVM is a supervised machine learning algorithm that can be used for both classification or regression challenges. However, it is mostly used in classification problems, which is called Support Vector Classifier (SVC).
I have used the scikit-learn library and matplotlib library of python to perform this project and used a scikit-learn predefined dataset load_digits for this project. I have given the link below.
The hypothesis to be tested: The Digits data set of scikit-learn library provides numerous data-sets that are useful for testing many problems…
In this post, I am going to analyze the data from the Weather data-set of Finland, a country in Northern Europe. The dataset has hourly temperature recorded for the last 10 years starting from 2006–04–01 00:00:00.000 +0200 to 2016–09–09 23:00:00.000 +0200. You can find the data-set on Kaggle. I am going to use the pandas and the matplotlib libraries of Python. I have given the link to the data set below.
The Hypothesis of the analysis is “ Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming”