Machine Learning - End to End Data Science Projects

This repository contains files for Udacity's Machine Learning Nanodegree Project: Boston House Price Prediction

Keras 101: A simple Neural Network for House Pricing regression

Machine learning (linear regression & kernel-ridge regression) examples on the Boston housing dataset

Implement a perceptron from scratch

All the essential resources and template code needed to understand major data science and machine learning libraries like Numpy, Pandas, Matplotlib and Scikit Learn with few small projects to demonstrate their practical application.

Predicting Boston House Prices

Implementation of 11 variants of Gradient Descent algorithm from scratch, applied to the Boston Housing Dataset.

Boston Housing Prediction - 2nd project for Udacity's Machine Learning Nanodegree

Linear Regression , Cross Validation, k-mean clustering , Watershed , Gradients and Edge Detection , threshold , Correlation , Neural Network, Conventional Neural Network , Pneumonia Classification, Social Distancing, Rainfall Prediction, Boston Housing Price Prediction.

An Implementation of the Gradient Descent Algorithm on the 🏡Boston Housing DataSet🏡.

Gradient Descent for N features using two datasets: Boston House data, Power Plant Data

This project is about predicting house price of Boston city using supervised machine learning algorithms. In this we used three models Multiple Linear Regression, Decision Tree and Random Forest and finally choose the best one. Furthermore, we briefly introduced Regression, the data set, analyzed and visualized the dataset.

Dataset Boston Housing Price prediction

In this repository, a regression analysis is conducted using different machine learning and deep learning models. The study is led in order to choose the most suitable model by looking at different characteristics (models tuning, features scaling, etc).

These are all the assignments from Udacity Nanodegree Machine Learning course

Machine Learning Nano-degree Project : To assist a real estate agent and his/her client with finding the best selling price for their home