Weather changes in last few years have been increasing enormously and it is expected to increase more in the future. Therefore, it is a wearisome process to investigate larger forms of climate, temperature changes in the data and perform predictive analysis of the same using traditional methods. This paper aims to project temperature and seasonality changes using predictive analysis with the help of various machine learning techniques such as Time series forecasting using ARIMA & SARIMAX, Linear Regression and Auto-Correlation technique. The system proposed serves as a tool which takes in the climatic changes from huge amount of data as input and predicts the future temperature with max, min and average temperature in an efficient manner. We will be predicting the temperature change from 1992-2024 with the detailed forecast and changes from 2020- 2024 and predicting the accuracy in the changes. Predictive analytic model internment relationships among various features in a data set to assess risk with a particular set of conditions to assign a weight or score. These patterns of weight/score found in historical data can be used for predicting the future climatic and temperature changes.

Linear Regression, ARIMA, SARIMAX,Auto- correlation.