Portofolio about Machine Learning and Deep Learning Project
- first excercise machine learning
- using dataset about telco churn dataset
- training modelling using Logistic Regression, Random Forest, and Gradient Boosting
- Random Forest make better result for training model
- modelling using K-prototypes
- using 3 categories features and 2 numerical features
- sentiment analysis using tweepy and python readlines
- with search topic about "uu ite" and "kritik"
- the result about topic is dominate by negative sentiment
- Face recognition using Haarcascade
- face recognition training using 4 difference face
- Weather prediction using ANN modelling using pytorch
- had 2 hidden layer each layer with 5 neuron and 3 neuron
- after 1000 epoch of training model the result get :
- accuracy : 83%, loss : 0.404
- had a good recall for no raining prediction (96%) but worst for raining prediction (40%)
- MNIST prediction using Logistic Regression,ANN and CNN modelling using pytorch
- after 15 epoch of training model the result get :
- CNN had better result between each other (acc:88%,loss:0.40)
- ANN get lower result from logistic regression, maybe due to had unpotimized ANN model (1 hidden layer with 50 neuron)