Weather-proof Mobility
In [166]: import os import pandas as pd import numpy as np import json import pprint import seaborn as sns from pandas.io.json import […]
Datathon 2020 Solutions
Weather Disruption of Public Transport Analysis Using Python
The Weather Dataset provided has been preprocessed the traffic data ha been appended after preprocessing.The aim is to find the intersection dates available from both the datasets and do a predictive analsyis after combining traffic and weather datasets.so if future weather conditions are given or predicted by time series analysis ,public trasnport disruption could be interpreted using machine learning models.
(Just a small try by an undergrad engineering student,Hope you like it 🙂 ).
Datathon2020 – NetInfo Article Recommender – Solution – nextClick
Giving a recommendation to the user to catch his eye on and meet his preferences is essential task for a recommendation system. The amount of data is increasing significantly and the idea is to get some knowledge from it. Taking advantage of user similarities or news similarities will provide useful information to predict which article will the user find interesting.
Minimizing logistical transportation expenses of Retail Supply Chain
Business Understanding As you know,lots of companies,related to logistics system,are having troubles with managing their finances,when it comes to transporting and ordering.There […]
Datathon 2020 – Article recommendation
Article recommendation Team Fire Initial Analysis: The task of recommending and predicting a next best article is modeled as a function of […]
Case NetInfo/Vesti.bg article recommendation — Team Army of Ones and Zeroes — Datathon 2020
Team Army of Ones and Zeros solving the “Netinfo/Vesti.bg” Case for Datathon 2020.
Predicting weather disruption of public transport
The purpose of the project was to build a model that tries to predict potential delays in Dubai’s bus transportation schedule, based on the weather conditions. Additional Extreme Gradient Boosting model was built, which is based on the weather conditions by 5 hours ago, which slightly improved the prediction of a few outliers, although this came at the cost of reducing the prediction accuracy for non-outliers. The overall prediction power was unfortunately unimpressive and could potentially be improved by analyzing the bus transportation data at an hourly level, by including additional data, such as global weather forecasts and traffic estimates, but also by exploring more feature engineering options, for example – seasonality, business activity, hourly segments and outlying flags.