All codes, both created and optimized for best results from the SuperDataScience Course
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Updated
Nov 5, 2017 - Python
All codes, both created and optimized for best results from the SuperDataScience Course
Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and s…
This is the code for "Binary Classification using Keras Sequential, Functional and Model Subclassing" By M.Junaid Fiaz
Harvard Project - Accuracy improvement by adding seasonality premium pricing
A minimal Deep learning library for the web.
Classic Machine Learning in R
Machine Learning project. Movies Ratings prediction & prediction of White Wine Quality using classification algorithms. The main aim of the project: dive into ML/AI.
Deteksi penyakit pada (daun) jagung berbasis citra dengan menggunakan metode GLRLM dan FCH.
Spark ML Dashboard built to plug-in and tweak the model params to real-time verify classification results on sample test data
Python and sklearn, KNN, logistic and linear regression, cross-validation
Predict the type of arrhythmia based on Electro-cardiogram (ECG) tool using machine learning models and algorithms.
In this project using New York dataset we will predict the fare price of next trip. The dataset can be downloaded from https://www.kaggle.com/kentonnlp/2014-new-york-city-taxi-trips The dataset contains 2 Crore records and 8 features along with GPS coordinates of pickup and dropoff
Solved tasks of "Machine Learning" course, contains implementations of main machine learning algorithms.
Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
Google colab notebook for the Kaggle Home prices submission
My implementation of homework 1 for the Introduction to Machine Learning class in NCTU (course number 1181).
in this repository, there are my kaggle project on loan application prediction in python and python code on linear regression, random forest, k-means, svm, and some easy but happy code to make python coding skill more better.
Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
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