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.

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.

Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties

This project uses a machine learning approach in order to predict the number of goals scored by two teams in a match and then calculates the winning team

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…

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).

The aim of this project to see to do the prediction of the weather using the different types of machine learning model.

Proyek pertama predictive analytics untuk membangun model machine learning yang dapat memprediksi harga sewa rumah dan apartement di India.

🌱 Predicting Number of Sales per Product 🌱

Using Spotipy (Spotify Api) and data science techniques, create a playlist with songs similar to user's top tracks.

🌱 Predicting Ames House Prices 🌱

Machine learning project to predict NYC property prices.

Sales Time Series Forecasting using Machine Learning Techniques (Random Forest, XGBoost, and Stacked Ensemble Regressor)

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

Prediction on energy consumptions of the city of Seattle in order to reach its goal of being a carbon neutral city in 2050.

The Revolving Credit Behavior Modeling project analyzes revolving credit to facilitate flexible access to funds within a credit limit, assisting financial institutions in setting accurate pricing strategies by addressing risk factors like inflation and interest rates.

Aprendizaje automático en la celebración de contratos gubernamentales en Colombia.

Predict the mileage per gallon (mpg) for cars