Predict Business performance using machine learning Random Forest algorithm

The application of machine learning algorithms in predictive analytics has become a pivotal element in contemporary business decision-making processes. This study explores the efficacy of the Random Forest algorithm a renowned ensemble learning method or predicting business performance metrics such as sales, revenue, and customer engagement. The Random Forest algorithm is esteemed for its capacity to handle large datasets with numerous input variables and its intrinsic mechanism for feature selection, thereby enhancing prediction accuracy while mitigating overfitting concerns. We collected a comprehensive dataset encompassing various business performance indicators and their potential determinants, such as market trends, customer demographics, operational metrics, and competitive landscape data. Following data preprocessing to ensure data quality and relevance, we executed feature selection techniques to isolate the most impactful predictors. We then partitioned the dataset into training and testing subsets for model development and evaluation, respectively. The Random Forest model was trained on the training set with a diverse array of hyperparameters to identify the optimal configuration. Model validation was conducted using k-fold cross-validation to ensure generalizability across various data subsets. Post-training evaluation on the testing set employed standard performance metrics Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared value to assess the model’s predictive accuracy.The application of machine learning algorithms in predictive analytics has become a pivotal element in contemporary business decision-making processes. This study explores the efficacy of the Random Forest algorithm a renowned ensemble learning method or predicting business performance metrics such as sales, revenue, and customer engagement. The Random Forest algorithm is esteemed for its capacity to handle large datasets with numerous input variables and its intrinsic mechanism for feature selection, thereby enhancing prediction accuracy while mitigating overfitting concerns. We collected a comprehensive dataset encompassing various business performance indicators and their potential determinants, such as market trends, customer demographics, operational metrics, and competitive landscape data. Following data preprocessing to ensure data quality and relevance, we executed feature selection techniques to isolate the most impactful predictors. We then partitioned the dataset into training and testing subsets for model development and evaluation, respectively. The Random Forest model was trained on the training set with a diverse array of hyperparameters to identify the optimal configuration. Model validation was conducted using k-fold cross-validation to ensure generalizability across various data subsets. Post-training evaluation on the testing set employed standard performance metrics Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared value to assess the model’s predictive accuracy.

کلیدواژه ها:

Predictive Analytics ، Random Forest Algorithm ، Business Intelligence ، Machine Learning ، Data Preprocessing