• Chandni Tiwari
• Dr Kalpana rai

In this dissertation we can focuses on decision tree techniques such as CART and Random Forest for predicting customer churn through which we can build the classification models and also compare the performance of these models with logistic regression model. This comparison is depend on the data available and the efficiency of the algorithm which we have used. To minimize the cost and increase the effectiveness churn prediction should be more accurate so that any company should know the customers who are going to switch. Predictions can be made by calling the predict() function and specifying the index of the time or times to be predicted. Let’s start off with something simple. We will fit an ARIMA model to the entire Shampoo Sales dataset and review the residual errors.

financial time series, ARIMA, machine learning, ANN, BP Neural Network ARIMA, Motivation to use arima model, ARIMA-BP on machine learning through such ARIMA-with Logistic Regression model

"ARIMA(AUTOREGRESSIVE INTEGRATED MOVING AVERAGE) FOR CHURN ANALYSIS OF COVID19 CASES IN COUNTRY)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.h401-h404, May-2022, Available :http://www.jetir.org/papers/JETIR2205851.pdf

"ARIMA(AUTOREGRESSIVE INTEGRATED MOVING AVERAGE) FOR CHURN ANALYSIS OF COVID19 CASES IN COUNTRY)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. pph401-h404, May-2022, Available at : http://www.jetir.org/papers/JETIR2205851.pdf