anuraghazra

credit_risk_analysis

Credit risk analysis: based on the number of family dependents and the duration of the month's loan, classify the credit rating or risk rating using the decision tree method (C50 with R).

customer-segmentation

Customer segmentation from shop customer data.

datatekswith_r

Preprocessing data .txt with R

ex-study-cases

This repository is a collection of case studies on predictive, computer vision and recommendation systems. This code, I collect from course Dicoding Indonesia.

indian-liver--patient

This repository is about prediction of disease Liver patient

iris-dataset

Berikut merupakan analisis klasifikasi dataset Iris menggunakan metode Decision Tree, SVM dan Logistic Regression.

marketplace-review-from-google-playstore

This repository is an analysis of the classification of sentiment reviews from users of the marketplace application, where the word weighting methods used are TFIDF and Word2Vec. Meanwhile, the classification method used is Support Vector Machine (SVM). There are two kernels used in this analysis, namely the kernel Linear and the kernel Radial Basis Function (RBF).

mysql

This repository contains my project of SQL in DQLAB Id

online-sales

by power BI

rockpaperscissors

classification of rock, papers and scissors.

salesofcandyandcereal_fordoctorvisits

Regression Analysis: To determine the effect of candy and cereal consumption on the increasing number of dental visits (with R).

simple-statistics-using-r

The following is a simple statistical analysis using a customer satisfaction level dataset. The data was obtained from the DQLab learning page assessment.

spotify-playlist

Implementation of recommendation system

tetaniarizki

transferlearning

This is sub bab of Course Convolutionan Neural Network (CNN) with Tensorflow. And in this code, I use a technique called `Transfer Learning` in which utilize an already trained network to help solve a similar problem to the one it was originally trained to solve.

twitter-sentiment-analysis-in-python

The objective of this task is to detect hate speech in tweets. Tweet contains negative/hate sentiments as well as positive sentiments. So, the task is to classify negative tweets from other tweets. Given a training sample of tweets and labels, where label '1' denotes the tweet is negative and label '0' denotes the tweet is not negative. The objective is to predict the labels on the test dataset.

virtual_internship_home_credit

MYSQL

visualization

This repository contains my project visualization in Tableau.