Treffer: Applying Machine Learning Algorithms for Analyzing and predicting Agriculture (Crops) Performance with many types of fertilizer and temperature, humidity, rainfall.

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core the revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. In this paper we first introduced you to the python programming characteristics and features. Python is one of the most preferred languages for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries. This paper offers insight into the field of machine learning with python, taking a tour through important topics and libraries of python which enables the development of machine learning model a easy process. Then we will look at different types of machine learning and various algorithms of machine leaning. And at last, we will look at the one of the most used models i.e., Linear Regression. Linear Regression is a Machine Learning algorithm based on supervised learning. It performs a regression task. It is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called dependent variable. The variable you are using to predict the other variable's value is called the independent variable. Hypothesis function for linear regression: Y = mx + c and at last, in this paper, we will be going to understand one of the linear 4 regression models for an ice-cream selling company which will predict the sales done by the business on different temperatures. [ABSTRACT FROM AUTHOR]

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