Browsing by Author "ANJAN RHUDRA PAUL"

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    JOB SELECTION USING MACHINE LEARNING ALGORITHMS
    (2022-08-30) ANJAN RHUDRA PAUL
    Suitable job selection is a great challenging task, especially for freshers. As every year millions of students complete their graduation, they come with many options for choosing their jobs. This job selection procedure depends on various factors. In this research, we try to build a model to predict whether a job is suitable for a candidate or not according to their skills, experiences, and their desires for the job. First of all, we collect data from 120 individual people who are currently appointed in a job in various fields. Then we train our model with Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, K-Nearest-Neighbor, Random Forest Tree, decision tree, and multilayer perceptron neural network algorithm to predict whether they are satisfied with their current job or not. Which acquire an 80% accuracy rate for Logistic Regression, Gaussian Naive Bayes gives a 79% accuracy, K-Nearest-Neighbor gives of 87.5% accuracy, Support Vector Machine gives 79% accuracy, Random Forest Tree gives 92% accuracy, Multilayer Perceptron Algorithm gives 91.6% accuracy, Decision 83% accuracy. We also find the other performance matrices and compare them with other algorithms to evaluate the performance of best model.