This is a ML model to classify Male and Females using some physical characterstics Data. Python Libraries like Pandas,Numpy and Sklearn are used In this.
Data set credits: Kaggle.com
1. Accuracy of Decision Tree is: 96.87%
It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome. It is a graphical representation for getting all the possible solutions to a problem/decision based on given conditions.
**2. Accuracy of Random Forest is: 97.53%**👑
Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset.
3. Accuracy of Logistic Regression is: 97.27%
Logistic regression is one of the most popular Machine Learning algorithms, which comes under the Supervised Learning technique. It is used for predicting the categorical dependent variable using a given set of independent variables
4. Accuracy of KNeighbors is: 97.20%
K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categorie
K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suite category by using K- NN algorithm.
File index.html(interface for deployment of webapp)
Contributions are always welcome! You can contribute to this project in the following way:
- Deployment of model
- Accuracy improvement
- Bug fixes
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Aryan Raj
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Gender-Classification is under MIT License, Please Read the LICENSE
