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Gender Prediction Using Machine Learning Models

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gender prediction using machine learning

In the era of personalization and targeted user experiences, gender prediction from names can play a significant role in various applications. This project focuses on building a machine-learning model to predict gender based on names using multiple classification algorithms.

Project Objectives

The goal was to build a robust machine learning pipeline that can accurately predict gender based on text input (names). The project aimed to explore different models and evaluate their performance.

Data Loading and Exploration

The dataset was loaded using pandas and inspected for its structure. The dataset contained names along with their corresponding gender labels.

Key steps in the exploration included:

  • Understanding the distribution of gender labels
  • Identifying missing or duplicate values, if any
  • Analyzing the length and frequency of names for potential feature extraction

Data Preprocessing

Preprocessing steps were critical to prepare the dataset for machine learning models:

  • Text Vectorization: The project used TF-IDF (Term Frequency-Inverse Document Frequency) to convert text features into numerical format suitable for machine learning.
  • Encoding: The target labels were encoded using LabelEncoder.
  • Data Splitting: The dataset was split into training and testing sets for robust evaluation.

Model Training and Evaluation

The project experimented with various models, including:

  • Logistic Regression: Known for its interpretability and effectiveness on linear datasets.
  • Naive Bayes: A probabilistic classifier well-suited for text classification tasks.
  • XGBoost: A powerful gradient boosting algorithm for higher accuracy and performance.

Evaluation Metrics

The models were evaluated using:

  • Accuracy: Percentage of correct predictions
  • Precision and Recall: To measure false positives and false negatives
  • Confusion Matrix: Visualization of prediction results
  • AUC-ROC Curve: Evaluating model performance across different thresholds

Results and Key Insights

The Logistic Regression model stood out with superior accuracy and balanced precision and recall scores. The confusion matrix and AUC-ROC curve provided additional insights into the model's decision boundaries.

Visuals

Here are the key plots that capture the performance of the model:

       1. Accuracy Metrics Plot:

This plot highlights the accuracy achieved by the model across training and testing datasets.

      2. Confusion Matrix Plot:

This matrix illustrates the distribution of true and false predictions.

       3. ROC-AUC Curve:
The ROC-AUC curve showcases the model's ability to distinguish between classes at different thresholds.

Gender Prediction App

About the Author

Results-driven Data Analyst with expertise in SQL, Power BI, Tableau, and Excel. Proven track record in data extraction, cleaning, and analysis, driving data-driven decisions. Skilled in collaborating with cross-functional teams to enhance data quality and deliver actionable insights.
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