Pinned Repositories
angular.js
AngularJS - HTML enhanced for web apps!
AutoRCCar
OpenCV Python Neural Network Autonomous RC Car
credit-card-fraud-detection
Implemented Decision Tree, Random Forest and Deep Neural Network (5 layers) on a real anonymized dataset. Used undersampling and oversampling (SMOTE) with Deep Neural Network to balance the highly imbalanced dataset (very less fraudulent transactions as compared to non-fraudulent ones). Achieved 99.6% test accuracy using SMOTE with NN along with 0 Type-II errors (false negatives).
customer-subscription-direction
Used Logistic Regression Classifier to decide whether a customer should be directed to subscription by analyzing his app behavior. Also implemented k-fold Cross Validation to prevent overfitting.
dlib
A toolkit for making real world machine learning and data analysis applications in C++
home-security-system
Built system to recognize faces and give access to home, based on access profile (family, friend, intruder) in database. Possesses multi-level security. (PIR motion sensor inside for motion detection and webcam outside home for surveillance and face recognition) Owner/s can access system remotely and can also see live video surveillance on mobile phones/PC at any time. (Check out test_videos.txt for links to YouTube videos to see the project in action!)
loan-likelihood-prediction
Implemented following algorithms to solve the problem- Logistic Regression (with Lasso penalty), SVM (with linear and RBF kernels) and Random Forest (with 100 decision trees and Entropy & Gini impurity). Also used Grid Search to tune hyperparameters of Random Forest classifier. Achieved initial test accuracy of 56.2% with Logistic Regression and increased to 63.5% with Random Forest + Gini impurity.
minimizing-churn-rate
Applied Logistic Regression to a dataset of customers’ financial habits artificially created from real life case studies. Achieved test accuracy of 62.9%. Used Undersampling to balance the dataset, k-fold cross validation to improve accuracy and Recursive Feature Elimination to reduce chances of overfitting.
mnist-digits-classification
Built a linear neural network from scratch to classify handwritten digits (0-9) taken from MNIST datasubset in json format. Implemented Mini Batch Gradient Descent, ReLU activation function, softmax for classification and dropout to handle overfitting. Achieved test accuracy of 92.6%.
HimalayPatel's Repositories
HimalayPatel/credit-card-fraud-detection
Implemented Decision Tree, Random Forest and Deep Neural Network (5 layers) on a real anonymized dataset. Used undersampling and oversampling (SMOTE) with Deep Neural Network to balance the highly imbalanced dataset (very less fraudulent transactions as compared to non-fraudulent ones). Achieved 99.6% test accuracy using SMOTE with NN along with 0 Type-II errors (false negatives).
HimalayPatel/dlib
A toolkit for making real world machine learning and data analysis applications in C++
HimalayPatel/minimizing-churn-rate
Applied Logistic Regression to a dataset of customers’ financial habits artificially created from real life case studies. Achieved test accuracy of 62.9%. Used Undersampling to balance the dataset, k-fold cross validation to improve accuracy and Recursive Feature Elimination to reduce chances of overfitting.
HimalayPatel/angular.js
AngularJS - HTML enhanced for web apps!
HimalayPatel/AutoRCCar
OpenCV Python Neural Network Autonomous RC Car
HimalayPatel/customer-subscription-direction
Used Logistic Regression Classifier to decide whether a customer should be directed to subscription by analyzing his app behavior. Also implemented k-fold Cross Validation to prevent overfitting.
HimalayPatel/home-security-system
Built system to recognize faces and give access to home, based on access profile (family, friend, intruder) in database. Possesses multi-level security. (PIR motion sensor inside for motion detection and webcam outside home for surveillance and face recognition) Owner/s can access system remotely and can also see live video surveillance on mobile phones/PC at any time. (Check out test_videos.txt for links to YouTube videos to see the project in action!)
HimalayPatel/loan-likelihood-prediction
Implemented following algorithms to solve the problem- Logistic Regression (with Lasso penalty), SVM (with linear and RBF kernels) and Random Forest (with 100 decision trees and Entropy & Gini impurity). Also used Grid Search to tune hyperparameters of Random Forest classifier. Achieved initial test accuracy of 56.2% with Logistic Regression and increased to 63.5% with Random Forest + Gini impurity.
HimalayPatel/mnist-digits-classification
Built a linear neural network from scratch to classify handwritten digits (0-9) taken from MNIST datasubset in json format. Implemented Mini Batch Gradient Descent, ReLU activation function, softmax for classification and dropout to handle overfitting. Achieved test accuracy of 92.6%.
HimalayPatel/awesome-courses
:books: List of awesome university courses for learning Computer Science!
HimalayPatel/awesome-public-datasets
A topic-centric list of HQ open datasets. PR ☛☛☛
HimalayPatel/Babylon.js
Babylon.js: a complete JavaScript framework for building 3D games with HTML 5 and WebGL
HimalayPatel/blynk-library
Blynk library for embedded hardware. Works with Arduino, ESP8266, Raspberry Pi, Intel Edison/Galileo, LinkIt ONE, Particle Core/Photon, Energia, ARM mbed, etc.
HimalayPatel/breast-cancer-classification
Used Support Vector Machine to classify a given image from breast cancer wisconsin dataset (sklearn) into malignant or benign cancer.
HimalayPatel/ember.js
Ember.js - A JavaScript framework for creating ambitious web applications
HimalayPatel/face_detect_n_track
Fast and robust face detection and tracking
HimalayPatel/face_recognition
The world's simplest facial recognition api for Python and the command line
HimalayPatel/fashion-class-classification
Implemented Convolutional Neural Network to classify images from MNIST Fashion Class dataset into 10 different classes.
HimalayPatel/jgraphx
HimalayPatel/linux
Linux kernel source tree
HimalayPatel/meteor
Meteor, the JavaScript App Platform
HimalayPatel/milq
Useful code of Manuel Ignacio López Quintero
HimalayPatel/opencv
Open Source Computer Vision Library
HimalayPatel/rails
Ruby on Rails
HimalayPatel/react
A declarative, efficient, and flexible JavaScript library for building user interfaces.
HimalayPatel/remi
Python REMote Interface library. Platform independent. In about 100 Kbytes, perfect for your diet.
HimalayPatel/swot
Identify email addresses or domains names that belong to colleges or universities. Help automate the process of approving or rejecting academic discounts.
HimalayPatel/toy-autonomous-cars
Implemented value iteration algorithm and generated an optimal policy for cars moving in an observable, stochastic and reward-based environment. Also simulated the policy generated introducing stochasticity using numpy random and calculated the average rewards for each car.
HimalayPatel/vue
A progressive, incrementally-adoptable JavaScript framework for building UI on the web.
HimalayPatel/Vulcan
🌋 A toolkit to quickly build apps with React, GraphQL & Meteor