Handwritten Digit Recognition using neural nets and ML classifiers
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Updated
Jan 24, 2019 - Python
Handwritten Digit Recognition using neural nets and ML classifiers
Predict which customers should a call-center call for greater assertiveness in a sale
Refining Fake News Classification with Sentiment Analysis. Final project for EMIS 8331: Advanced Data Mining at SMU (Spring 2017).
Python script that defines a Flask web application with two endpoints for predicting two machine learning models.
The code of the random forest project I shared on my Medium account
Diabetes Prediction using Machine Learning Algorithms
Using lexical features of a URL to classify it as malicious or not using Random Forest
fraud_check_prj_10
Disease Prediction Model using SVM, GaussianNB and Random Forest Classifiers.
BitVision is a Python-based computer vision app that allows users to record actions (3D poses) on video using Mediapipe and map them to keyboard inputs.
CSE601 Course Projects - Fall 2017
Minimal implementation of Random Forest classifier using decision stumps and bootstrap sampling without sklearn.
Fake News Kaggle competition
ML model building using Mlflow workflow for en-to-end development.
Machine Learning Prediction Software Based on Classification and Regression Based on Processor [CPU] Specifications
A web app using streamlit to predict Penguin Species
Determined bank customer’s subscription credibility by training ML model, achieving an accuracy of 88%. Visualized model predictions against past data to facilitate data-driven decision-making.
This is a computer vision project which is integrated with Machine Learning model which is to classify Rock, paper and scissors. The input data is given form our hand and it is collected and run from separate files in this repository.
Persian hand writing digit detection using tensorflow
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