Ariba Code-A-Thon 2018

homesite kaggle

Demonstrating how to build an XGBoost model and deploy it to Algorithmia, from a Jupyter notebook

Ist Place Solution

In this project I used different regression algorithms to predict sales of stores. I used Kaggles free GPUs and Datasets in this competition. Those different algorithms include random forrest, decision tree, xgboost, K Nearest Neighbour and so on. Initially I used feature engineering to get my data into the best shape.

A Repository for handling different complex machine learning algorithms like boosting etc. This repository contains/will contain all the important algorithms implemented on real data. The helper functions defined will prevent from writing complex codes and will help us realize our goal faster.

This repository represents the End to End Machine Learning Project (Rain Fall Prediction in Australia).

The diamonds price prediction with linear regression models.

Epinions.com is a website where people can post reviews of products and services. It covers a wide variety of topics. For this case study, we downloaded a set of 600 posts about digital cameras and cars and saved as “Eopinions.csv”. The dataset has 2 columns: ‘class’ and ‘text’. We need to predict 'class' based on 'text'.

Agri AI is an advanced precision agriculture platform designed to aid farmers and agricultural enthusiasts in making more informed decisions regarding crop management, pest control, and yield prediction. At its core, the platform utilizes sophisticated machine learning algorithms, specifically the Random Forest and XGBoost models, to analyze data

Customer Conversion Prediction project is to build a machine learning model that can predict whether a client will subscribe to the insurance based on their demographic and marketing data.

Projects based on Machine Leaning

Comparative Analysis of Machine Learning Models for Predicting Smart Grid Stability

In this project i am trying to use NLP, ML concepts on Amazon reviews using various ML based model like XGBoost, Decision tree classifier and random forest

This project implements a machine learning forecast model using XGBoost to predict headcount based on historical data. The model preprocesses the data, trains on the training set, and generates predictions for the test set.

Parameters of extreme gradient boosting model are fine tuned to achieve better accuracy

Data analysis, visualization, classification