Learning to create Machine Learning Algorithms
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Updated
Jun 15, 2021 - Python
Learning to create Machine Learning Algorithms
A Machine Learning Project implemented from scratch which involves web scraping, data engineering, exploratory data analysis and machine learning to predict housing prices in New York Tri-State Area.
I have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and…
The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.
Determining the housing prices of California properties for new sellers and also for buyers to estimate the profitability of the deal.
Machine Learning project for Kaggle competition
Proyek pertama predictive analytics untuk membangun model machine learning yang dapat memprediksi harga sewa rumah dan apartement di India.
Predict the mileage per gallon (mpg) for cars
It is an e-commerce web portal for farmers and customers. Farmers can list there crops with quantity and base price. Customers can bid on a crop with there prices. Farmer can sell there crop to best bid. Framer can also predict the production of the crop of a particular season, year, weather, and area.
The aim of this project to see to do the prediction of the weather using the different types of machine learning model.
Predicts the red and white wine qualities, given their physicochemical attributes
Data science practice project from "Hands-On Machine Learning with SciKit-Learn and TensorFlow"
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
Bike Sharing Demand Prediction By Supervised Machine Learning Algorithms Implementation On Seoul Bike Sharing Dataset
Regression and Classification task with sklearn.
Goal is to predict the concrete compressive strength using collected data
This is the proof of concept, how a relatively unsophisticated statistical model trained on the large MPDS dataset predicts physical properties from the only crystalline structure (POSCAR or CIF).
This repository contains the code and resources for a Car Price Prediction project.
Kaggle Competition - Analysis and prediction of PUBG players' finishing placement based on their final stats
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