Data Analytics by Python AI, to Understand Deviation of Minimum Temperature During Winter (Using Artificial Neural Network)

This paper is based on data analysis by Python programming language on Google collaborator platform, supported by the AI code, model used as neural network, to understand the deviation of minimum temperature for the period November to February in comparison with recorded normal. The source of data is online data collection platform ‘INDIA METEOROLOGICAL DEPARTMENT, PUNE’ and the online site ‘OGIMET’. The surface data for Alipore (42807) and the recorded normal temperature, as available there has been collected in csv file format, then uploading the csv file in Google collaborator platform, executed analysis by Python neural network model technique, to understand the predicted output, i.e. pattern of deviation of minimum temperature, based on analysis of big data, with minimum temperature data from 1969 to 2024. In this case the input column used is the column created with data of difference between minimum temperature and recorded normal for that month. The mod value of temperature difference for the four months, November to February filtered from this big data set has been used as filtered and scaled data, subjected to analyse to understand the future trend of this deviation. Model fit has been used with train-test split in 80%-20% ratio. The artificial neural network model actually resembles the structure of human brain, which can perform intelligent tasks similar to human brain by the process of learning by the machine. Like human brain a similar network is there with mapping between input and output, information propagated through different layers, using back propagation technique to learn the model to get best output with minimum loss. This technique resembles to the networking of human brain and neuron, where information is propagated through synaptic joints to react accordingly. In this paper prediction as well as analysis has been made with the application of artificial neural network with used model as LSTM supported by activation function and optimiser to learn the model for best fit output. The loss value of the model has also been verified to understand the success rate of the model