Gender Classification is a project designed to predict the gender of individuals based on available data, with three gender categories: Male (M), Female (F), and Unknown. The project aims to facilitate customer classification by predicting the gender of individuals with an "Unknown" label and assigning them a gender label (either M or F) using machine learning techniques.
- Predicts the gender of individuals with an "Unknown" label.
- Utilizes Decision Tree algorithm for gender classification.
- Achieves an accuracy of 96% in gender prediction.
- scikit-learn (for machine learning)
- pandas (for data manipulation)
This project aims to improve customer classification processes by accurately predicting the gender of individuals with missing or unknown gender labels. By automating gender classification, businesses can enhance their marketing strategies, personalize customer experiences, and improve efficiency.
- Handling missing or unknown gender values in the dataset.
- Ensuring the accuracy and reliability of gender predictions.
- Optimizing the Decision Tree model for high performance and accuracy.
- Developed and implemented the gender classification model using Decision-Tree algorithm.
- Conducted data preprocessing, model training, and evaluation.
- Achieved an accuracy of 96% in gender prediction.
- Explore other machine learning algorithms for gender classification.
- Enhance the model's performance by fine-tuning hyperparameters.
- Expand the project to handle additional demographic variables for customer segmentation.