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<div Align="center"><img alt="GIF" src="https://raw.githubusercontent.com/shsarv/Machine-Learning-Projects/main/01%20Start/resources/a.png"/></div>
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## Table of Content:-
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* [List of ML Projects](#list-of-projects--)
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* [Other Projects](#other-projects-)
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* [Bug / Feature Request](#bug-or-feature-request)
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* [License](#license)
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* [Work By](#work-by)
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<p align="center">
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<a href="https://github.com/shsarv/Machine-Learning-Projects"><strong>Explore the docs »</strong></a>
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<br/>
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<br/>
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<a href="https://github.com/shsarv/Machine-Learning-Projects">View Demo</a>
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.
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<a href="https://github.com/shsarv/Machine-Learning-Projects/issues">Report Bug</a>
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.
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<a href="https://github.com/shsarv/Machine-Learning-Projects/issues">Request Feature</a>
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</p>
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</p>
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## List of Projects :-
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<!--
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* [Classification of Arrhythmia using ECG Data](#1-Classification-of-Arrhythmia-using-ECG-Data)
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* [Diabetes Prediction](#2-Diabetes-prediction)
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* [Getting Admission in College Prediction](#3-Getting-Admission-in-College-Prediction)
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* [Heart Disease Prediction](#4-Heart-Disease-Prediction)
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* [Iris Flower Classification](#5-Iris-Flower-Classification)
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* [Loan Repayment Prediction](#6-Loan-Repayment-Prediction)
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* [Predict Employee Turnover](#7-Predict-Employee-Turnover)
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* [Wine Quality Prediction](#8-Wine-Quality-Prediction)
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* [The Battle of Neighborhoods](#9-The-Battle-of-Neighborhoods) -->
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### 1. [Classification of Arrhythmia using ECG Data](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Classification%20of%20Arrhythmia)
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| [Notebook File](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Classification%20of%20Arrhythmia/final%20with%20pca.ipynb) | [Complete Project](https://github.com/shsarv/Project-Arrhythmia)| [Dataset](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Classification%20of%20Arrhythmia/Data/arrhythmia.csv) |
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- The goal of this project is to predict if a person is suffering from cardiac arrhythmia or not and if yes, classify it into one of 12 available groups.
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- The Dataset used in this project is available at the UCI machine learning Repository. It can be found [Here](https://archive.ics.uci.edu/ml/datasets/Arrhythmia).
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- The best Model was Kernelized SVM over PCA Data.
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- **Accuracy achieved = 80.21%**
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### 2. [Diabetes-prediction](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Diabetes%20Prediction)
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| [Notebook File](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Diabetes%20Prediction/Diabetes%20Classification.ipynb) | [Complete Deployed Project](https://github.com/shsarv/Diabetes-prediction) | [Dataset](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Diabetes%20Prediction/dataset) | [Working Link](https://sarvdiabetes-predictions.herokuapp.com/) |
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- The objective of the project is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.
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- The data set that has used in this project has taken from the Kaggle. "This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases.
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- Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage".
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- The model best worked on this dataset is Random Forest Classifier.
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- **Accuracy achieved = 98.75%**
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### 3. [Getting Admission in College Prediction](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Getting%20Admission%20in%20College%20Prediction)
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| [Notebook File](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Getting%20Admission%20in%20College%20Prediction/Admission%20prediction.ipynb) | [Dataset](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Getting%20Admission%20in%20College%20Prediction/admission_predict.csv) |
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- The objective of the project is to predict the chances of getting admission to a reputed University based on parameters like GRE Score, TOEFL Score, University Rating, SOP, LOR, CGPA, and Research submission.
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- The data set that has used in this project has taken from the kaggle.
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- The model best worked on this dataset is Linear Regression Model.
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- **Accuracy achieved = 81.08%**
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### 4. [Heart Disease Prediction](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Heart%20Disease%20Prediction)
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| [Notebook File](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Heart%20Disease%20Prediction/Heart%20Disease%20Prediction.ipynb) | [Dataset](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Heart%20Disease%20Prediction/heart.csv) |
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- The objective of the project is to diagnostically predict whether or not a patient has Cardiac/Heart diabetes, based on certain diagnostic measurements included in the dataset like cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, etc.
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- The data set that has used in this project is taken from the Kaggle.
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- The model best worked on this dataset is Random Forest Classifier with n_estimators=90.
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- **Accuracy achieved = 83.82%**
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### 5. [Iris Flower Classification](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Iris%20Flower%20Classification)
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| [Notebook File1(all models)](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Iris%20Flower%20Classification/iris.ipynb) | [Notebook File2(KNN based)](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Iris%20Flower%20Classification/KNN%20on%20Iris%20Dataset/iris_Flower_Classifcation_using_KNN.ipynb)| [Notebook File3(SVM Based)](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Iris%20Flower%20Classification/SVM%20Iris.ipynb) |[Dataset](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Iris%20Flower%20Classification/iris_data.csv)|
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- The aim is to classify iris flowers among three species (setosa, versicolor, or virginica) from measurements of sepals and petals' length and width.
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- The iris data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
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- The central goal here is to design a model that makes useful classifications for new flowers or, in other words, one which exhibits good generalization.
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- The model best worked on this dataset is the Support Vector Classifier.
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- **Accuracy achieved = 98%**
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![Contributors](https://img.shields.io/github/contributors/shsarv/Machine-Learning-Projects?color=dark-green) ![Forks](https://img.shields.io/github/forks/shsarv/Machine-Learning-Projects?style=social) ![Stargazers](https://img.shields.io/github/stars/shsarv/Machine-Learning-Projects?style=social) ![Issues](https://img.shields.io/github/issues/shsarv/Machine-Learning-Projects) ![License](https://img.shields.io/github/license/shsarv/Machine-Learning-Projects)
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### 6. [Loan Repayment Prediction](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Loan%20Repayment%20Prediction)
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------------------
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| [Notebook File](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Loan%20Repayment%20Prediction/Loan_Repayment_Prediction.ipynb)| [Dataset](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Loan%20Repayment%20Prediction/loan_data.csv)|
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**This Contain All the Machine Learning Projects that I have done while understanding Machine Learning Concepts.**
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- Predicts whether the bank should approve the loan of an applicant based on his profit using _Ensemble Learning Methods_.
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- The data set that has used in this project is taken from the Kaggle.
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- The model best worked on this dataset is Random Forest Classifier with n_estimators=600.
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- **Accuracy achieved = 84.75%**
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### 7. [Predict Employee Turnover](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Predict%20Employee%20Turnover%20with%20scikitlearn)
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| [Notebook File](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Predict%20Employee%20Turnover%20with%20scikitlearn/Learner_Notebook3.ipynb)| [Dataset](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Predict%20Employee%20Turnover%20with%20scikitlearn/employee_data.csv)|
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- It is a guided Project.
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- The objective of this project is to predict Employee Churn using Decision Tree and Random Forest Classifiers.
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- The Dataset is taken From a guided project Course available at Coursera named _Predict-Employee-Turnover-with-scikit-learn_.
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- **Accuracy achieved = 97%**
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### 8. [Wine Quality Prediction](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Wine%20Quality%20prediction)
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| [Notebook File](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Wine%20Quality%20prediction/Wine.ipynb)| [Dataset](https://github.com/shsarv/Machine-Learning-Projects/blob/main/Wine%20Quality%20prediction/winequality.csv)|
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- The Objective of the project is to predict the quality of the Wine based on different features present in the dataset.
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- The data set that has used in this project is taken from the Kaggle.
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- The model best worked on this dataset is Random Forest Classifier with n_estimators=100.
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- **Accuracy achieved = 90.31%**
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### 9. [The Battle of Neighborhoods](https://github.com/shsarv/Machine-Learning-Projects/tree/main/The%20Battle%20of%20Neighborhoods%20-Coursera%20capstone)
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| [Dataset](https://github.com/shsarv/Machine-Learning-Projects/tree/main/The%20Battle%20of%20Neighborhoods%20-Coursera%20capstone/dataset)| [Notebook File](https://github.com/shsarv/Machine-Learning-Projects/blob/main/The%20Battle%20of%20Neighborhoods%20-Coursera%20capstone/Capstone%20Project%20-%20The%20Battle%20of%20the%20Neighborhoods%20-%20London%20Neighborhood%20Clustering.ipynb)| [Complete Project](https://github.com/shsarv/Coursera_capstone)|
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- This project aims to select the safest borough in London based on the total crimes, explore the neighborhoods of that borough to find the 10 most common venues in each neighborhood and finally cluster the neighborhoods using k-mean clustering.
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- The crime statistics dataset of London found on Kaggle has crimes in each Boroughs of London from 2008 to 2016. The year 2016 being the latest we will be considering the data of that year which is actually old information as of now. The crime rates in each borough may have changed over time.
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- This is a Coursera Capstone Project for Data Science professional certication.
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- **k- mean Clustering problem**
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----------
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## Other Projects:-
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### [Deep Learning Projects](https://github.com/shsarv/Deep-Learning-Projects)
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* FIFA 2020
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* Digit Recogination
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* Loading...
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### Deployed Projects :-
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* [Restaurant-Recommendation-System](https://github.com/shsarv/Restaurant-Recommendation-System)
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* [Diabetes Prediction](https://github.com/shsarv/Diabetes-prediction)
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* [Unplug the Players](https://github.com/shsarv/UNPLUG-THE-PLAYER)
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* Loading.....
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### [EDA Projects](https://github.com/shsarv/EDA-Projects)
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- Covid19.
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- Global Terrorism.
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- Flat in cracow.
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- Trending Results.
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-----------
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## Bug or Feature Request
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## List of Projects :-
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If you find a bug (Link are not working properly or file is not working), kindly open an issue [here](https://github.com/shsarv/Machine-Learning-Projects/issues/new/choose).
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If you'd like to request your work or optimize the models, feel free to do so by opening an issue [here](https://github.com/shsarv/Machine-Learning-Projects/issues/new/choose).
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| Project Name | Link |
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|---------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|
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| AI Room Booking Chabot using IBM Watson | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/AI%20Room%20Booking%20Chatbot%20%5BIBM%20WATSON%5D) |
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| Brain Tumor Detection | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/BRAIN%20TUMOR%20DETECTION%20%5BEND%202%20END%5D) |
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| Classification of Arrhythmia Using ECG Data | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Classification%20of%20Arrhythmia%20%5BECG%20DATA%5D) |
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| Colorize Black and White Image | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Colorize%20Black%20%26%20white%20images%20%5BOPEN%20CV%5D) |
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| Diabetes Detection | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Diabetes%20Prediction%20%5BEND%202%20END%5D) |
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| Distracted Driver Detection | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Distracted%20Driver%20Detection) |
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| Drowsiness Detection | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Drowsiness%20detection%20%5BOPEN%20CV%5D) |
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| Gender and Age Detection | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Gender%20and%20age%20detection%20using%20deep%20learning) |
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| Getting Admission in College Prediction | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Getting%20Admission%20in%20College%20Prediction) |
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| Heart Disease Detection | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Heart%20Disease%20Prediction%20%5BEND%202%20END%5D) |
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| Human Activity Detection | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Human%20Activity%20Detection) |
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| Human Detection and Counting Project | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Human%20Detection%20%26%20Counting%20Project%20%5BOPEN%20CV%5D) |
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| IPL Score Prediction | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/IPL%20Score%20Prediction) |
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| Iris Flower Classification | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Iris%20Flower%20Classification) |
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| Lane Line Detection | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Lane%20Line%20Detection%20%5BOPEN%20CV%5D) |
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| Loan Repayment Prediction | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Loan%20Repayment%20Prediction) |
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| Mechanisms of Action (MOA) Prediction | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Mechanisms%20Of%20Action%20(MoA)%20Prediction) |
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| Medical Chatbot | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Medical%20Chatbot%20%5BEND%202%20END%5D%20%5BNLP%5D) |
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| Predict Employee Turnover | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Predict%20Employee%20Turnover%20with%20scikitlearn) |
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| Predict Property maintainance Fine | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Predicting%20Property%20Maintenance%20Fines) |
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| Research Topic Prediction | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Research%20topic%20Prediction) |
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| Smile Selfie Capture | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Smile%20Selfie%20Capture%20Project%20%20%5BOPEN%20CV%5D) |
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| The Battle of Neighbourhood | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/The%20Battle%20of%20Neighborhoods%20-Coursera%20capstone) |
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| Time Series Regression MultiStore Sales | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/TimeSeries%20Multi%20StoreSales%20prediction) |
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| Wine Quality Prediction | [Link](https://github.com/shsarv/Machine-Learning-Projects/tree/main/Wine%20Quality%20prediction) |
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## Contributing
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Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are **greatly appreciated**.
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* If you have suggestions for adding or removing projects, feel free to [open an issue](https://github.com/shsarv/Machine-Learning-Projects/issues/new) to discuss it, or directly create a pull request after you edit the *README.md* file with necessary changes.
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* Please make sure you check your spelling and grammar.
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* Create individual PR for each suggestion.
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* Please also read through the [Code Of Conduct](https://github.com/shsarv/Machine-Learning-Projects/blob/main/CODE_OF_CONDUCT.md) before posting your first idea as well.
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### Creating A Pull Request
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1. Fork the Repo
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2. Create your Project Branch (`git checkout -b Project/AmazingProject`)
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3. Commit your Changes (`git commit -m 'Add some AmazingProject'`)
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4. Push to the Branch (`git push origin Project/AmazingProject`)
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5. Open a Pull Request
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## License
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MIT License Copyright (c) 2020 Sarvesh Kumar Sharma
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Distributed under the MIT License. See [LICENSE](https://github.com/shsarv/Machine-Learning-Projects/blob/main/LICENSE.md) for more information.
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## Work By
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<table>
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### Work By
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<table>
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<tr>
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<td align="center"><a href="https://github.com/shsarv"><img src="https://avatars2.githubusercontent.com/u/55739302?s=400&u=1e7714cb1cbe3437a527a877486c94611f0e7ab0&v=4" width="100px;" alt=""/><br /><sub><b>Sarvesh Sharma</b></sub></a><br /><a href="https://github.com/shsarv" title="github"><img src="https://img.shields.io/github/followers/shsarv?style=social"></a> <a href="https://twitter.com/sarveshroli/" title="twitter"><img src="https://img.shields.io/twitter/follow/sarveshroli?label=twitter&style=social"></a></td>
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