Gender-classification

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Gender-classification

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Gender Classification Machine Learning Model using Sk-learn in Python with 97%+ accuracy and deployment

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# 🧑‍🦰👩‍🦰Gender-classification

Data set credits: Kaggle.com

![Machine Learning](https://user-images.githubusercontent.com/75358720/149663749-1cd54223-c0cc-498b-8b7b-eaf41f3df127.png)

*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

# ⭐Visualizing physical characters & diffrences using Graphs and plots

![__results___12_1](https://user-images.githubusercontent.com/75358720/161202870-db76e2bd-c9d1-4d02-9804-f32afb3fafd7.png)

![__results___13_2](https://user-images.githubusercontent.com/75358720/161202956-f51c325e-055b-4a33-950c-0c99ffff2341.png)

![__results___14_2](https://user-images.githubusercontent.com/75358720/161202997-2beef8c9-61db-491b-b18a-9a3eb54ccf0d.png)

# ✨Accuracy of Various models we trained

**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.
![Screenshot 2022-04-01 150931](https://user-images.githubusercontent.com/75358720/161238412-64f7746a-573e-4e0e-9cce-8b45c1fd2efa.png)

# 🎯Deployment process

File index.html(interface for deployment of webapp)

![Screenshot 2022-02-20 180258](https://user-images.githubusercontent.com/75358720/154842687-644b86c0-e04b-4de8-be9a-5419ac1e42fa.jpg)

## Contribution(s)

Contributions are always welcome! You can contribute to this project in the following way:
- [ ] Deployment of model
- [ ] Accuracy improvement
- [ ] Bug fixes

<div align="center"><h2><strong>Author of this Repository 🤝</strong></h2></div>

**Aryan Raj**

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   <img src="https://img.shields.io/badge/License-MIT-yellow.svg?logo=Microsoft%20Word&style=for-the-badge" height="28"/><br>
   <br><strong>Gender-Classification</strong> is under MIT License, Please Read the <strong>LICENSE</strong>
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Gender-classification

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