Random Forest

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1 - Implementation of Machine Learning Algorithms for Customer Churn Prediction
Manal Loukili Fayçal Messaoudi Raouya El Youbi
10.61186/jist.34208.11.43.196

20.1001.1.23221437.2023.11.43.5.4

Churn prediction is one of the most critical issues in the telecommunications industry. The possibilities of predicting churn have increased considerably due to the remarkable progress made in the field of machine learning and artificial intelligence. In this context, w More

Churn prediction is one of the most critical issues in the telecommunications industry. The possibilities of predicting churn have increased considerably due to the remarkable progress made in the field of machine learning and artificial intelligence. In this context, we propose the following process which consists of six stages. The first phase consists of data pre-processing, followed by feature analysis. In the third phase, the selection of features. Then the data was divided into two parts: the training set and the test set. In the prediction process, the most popular predictive models were adopted, namely random forest, k-nearest neighbor, and support vector machine. In addition, we used cross-validation on the training set for hyperparameter tuning and to avoid model overfitting. Then, the results obtained on the test set were evaluated using the confusion matrix and the AUC curve. Finally, we found that the models used gave high accuracy values (over 79%). The highest AUC score, 84%, is achieved by the SVM and bagging classifiers as an ensemble method which surpasses them. Manuscript profile
Open Access Article
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2 - An Analysis of Covid-19 Pandemic Outbreak on Economy using Neural Network and Random Forest
Md. Nahid Hasan Tanvir Ahmed Md. Ashik Md. Jahid Hasan Tahaziba Azmin Jia Uddin
10.52547/jist.34246.11.42.163

20.1001.1.23221437.2023.11.42.7.4

The pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Consequently, in this paper, w More

The pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Consequently, in this paper, we use machine learning classifiers and regressors to construct an early warning model to tackle economic recession due to the cause of covid-19 pandemic outbreak. A publicly available database created by the National Bureau of Economic Research (NBER) is used to validate the model, which contains information about national revenue, employment rate, and workers' earnings of the USA over 239 days (1 January 2020 to 12 May 2020). Different techniques such as missing value imputation, k-fold cross validation have been used to pre-process the dataset. Machine learning classifiers- Multi-layer Perceptron- Neural Network (MLP-NN) and Random Forest (RF) have been used to predict recession. Additionally, machine learning regressors-Long Short-Term Memory (LSTM) and Random Forest (RF) have been used to detect how much recession a country is facing as a result of positive test cases of covid-19 pandemic. Experimental results demonstrate that the MLP-NN and RF classifiers have exhibited average 88.33% and 85% of recession (where 95%, 81%, 89% and 85%, 81%, 89% for revenue, employment rate and workers earnings, respectively) and average 90.67% and 93.67% of prediction accuracy for LSTM and RF regressors (where 92%, 90%, 90%, and 95%, 93%, 93% respectively). Manuscript profile

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List of Articles Random Forest