Data set credits: Kaggle.com

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