Analyzing Potential Leads Data to Improve Marketing Strategies
Keywords:
Data science, Machine learning, Supervised learning, leads score analysis, prediction, Logistic regressionAbstract
To develop a lead scoring model using multiple machine learning models and create a user-friendly web application, the first step is to clearly define the problem and collect relevant data from various sources such as CRM, website, social media, email campaigns, etc. After cleaning and pre-processing the data, identify the essential features that can affect the lead score and split the data into training and testing sets. Next, select a number of machine learning models to train, evaluating their performance based on accuracy, precision, recall, and F1 score. Examples include decision trees, logistic regression, gradient boosting, random forests, and neural networks. After determining the best-performing model, optimize its hyperparameters to further improve its performance. Deploy the model as a web application utilizing a web framework like Flask or Django and test it for accuracy. Finally, continuously monitor the model's performance, gather feedback from users, and enhance it by adding new features, fine-tuning hyperparameters, and optimizing the user interface.
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