Publications by Tenam Lama
Project on machine learning on Attrition dataset 2
1 Introduction Exploratory Data Analysis and Feature Engineering In the realm of People Analytics, the significance of a data set created by IBM for attrition modeling cannot be overstated. This data set presents a valuable resource for addressing critical questions related to employee turnover and engagement. With 1470 rows and 35 columns, it offe...
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Project on machine learning on Attrition dataset
1 Introduction Exploratory Data Analysis and Feature Engineering In the realm of People Analytics, the significance of a data set created by IBM for attrition modeling cannot be overstated. This data set presents a valuable resource for addressing critical questions related to employee turnover and engagement. With 1470 rows and 35 columns, it offe...
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EDA with feature engineering
1 Introduction In the realm of People Analytics, the significance of a data set created by IBM for attrition modeling cannot be overstated. This data set presents a valuable resource for addressing critical questions related to employee turnover and engagement. With 1470 rows and 35 columns, it offers a wealth of information that encompasses variou...
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Logistic regression
1 Introduction In the realm of People Analytics, the significance of a data set created by IBM for attrition modeling cannot be overstated. This data set presents a valuable resource for addressing critical questions related to employee turnover and engagement. With 1470 rows and 35 columns, it offers a wealth of information that encompasses variou...
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Cross validation Roc
1 Introduction In the realm of predictive modeling, logistic regression stands as a foundational technique for tackling binary classification problems. It allows us to analyze the relationship between a set of predictor variables and a binary outcome, making it particularly well-suited for scenarios like employee attrition prediction. However, the ...
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Neural Network model
1 Introduction A neural network is a type of machine learning algorithm that is designed to recognize patterns in data. It’s inspired by the structure and function of the human brain, where interconnected neurons work together to process and transmit information. Neural networks consist of layers of interconnected nodes (neurons) that process inp...
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Decision Tree Model
1 Introduction Decision tree algorithms are a class of machine learning algorithms used for both classification and regression tasks. They work by recursively splitting the dataset into subsets based on the values of input features, ultimately creating a tree-like structure of decisions that leads to a predicted output. In the context of our employ...
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