Serious Gaining knowledge of on time Number | Dimitry Larko | Kaggle Years

Kaggle Days China edition was held on October 19-20 at Damei Center, Beijing.

More than 400 data scientists and enthusiasts gathered to learn, make friends, and compete in a full-day offline competition.

Kaggle Days is produced by LogicAI and Kaggle.

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LogicAI is a boutique Data Science consultancy company owned by Kaggle fans and Grandmasters. As a global company, they do custom end-to-end AI and Data Science development projects as well as trainings for C-level management and tech teams.
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The right time Assortment Forecasting with the use of Facebook Predictor and Python in 20 to 30 minutes

Time Series Forecasting with Facebook Prophet and Python in 20 Minutes

Trying to forecast the next best stock?

Want to predict the weather?

Maybe you’re just trying to get a better sales forecast for your small business!

Time series forecasting can help!

In this video you’ll learn how to QUICKLY use time series forecasting to produce a forecast. In just a couple of minutes you’ll be able to preprocess your dataset using Pandas and forecast over a number of time periods using Facebook Prophet.
In this video you’ll learn how to:
1. Preparing Data for Time Series FC
2. Training Prophet Time Series Models
3. Making forecast predictions

GET THE CODE!
https://github.com/nicknochnack/TimeSeriesForecastingProphet

Links Mentioned:
Facebook Prophet: https://facebook.github.io/prophet/docs/quick_start.html

If you have any questions, please drop a comment below!

Oh, and don’t forget to connect with me!
LinkedIn: https://www.linkedin.com/in/nicholasrenotte
Facebook: https://www.facebook.com/nickrenotte/
GitHub: https://github.com/nicknochnack

Happy coding!
Nick

P.s. Let me know how you go and drop a comment if you need a hand!

SLIDES: https://docs.google.com/presentation/d/1DbBAKCcbYOjdxUtUD6aLpB_rbg3ikHzdPSnUztw2GqA/edit#slide=id.g9f34cc2927_0_0
Video Rating: / 5

ARIMA Pattern In Python| Time Number Conjecturing #6|

ARIMA(Auto Regression Integrated Moving Average) Model Implementation in Python. Following things are covered in the video:
1) Reading Time Series Data in Python using Pandas library
2) Checking for stationarity of time series model
3) Auto Arima Function to select order of Auto Regression Model
4) Predicting Future temperature values using given dataset
5) Statsmodels library is used for modelling

My medium article on the same(Contains code and dataset): https://medium.com/@nachihebbar/temperature-forecasting-with-arima-model-in-python-427b2d3bcb53?sk=ff83ea42d7ca5eee874fc4f4be219604

Recommended Books to get better at Time Series Analysis and Python:

1)Practical Time Series Analysis: https://amzn.to/31lsLhq
2)Time Series with Python: https://amzn.to/2Ez073m
3)Hands-On Time Series Analysis with R: https://amzn.to/3aUxuKq

My 2nd Youtube Channel: https://www.youtube.com/channel/UCJBz6f1QtbNrDYwR-AUcSjA
You can connect with me on linkedin: https://www.linkedin.com/in/nachiketa-hebbar-86186515b/
Video Rating: / 5

The right time Number Conjecturing by using Machine Learning

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TIMESTAMPS

0:00 Introduction
1:51 Defining Problem
2:50 Understanding the Data
3:18 Analyzing Data (Trend, Seasonality)
4:40 Traditional Timeseries Forecasting (ARIMA, Prophet)
6:01 Univariate & Multivariate Time series
8:15 Time series with Machine Learning
9:02 Types of Time series models
11:05 Machine Learning Vs. Traditional Time Series

REFERENCES
[1] Math behind Facebook prophet: https://medium.com/future-vision/the-math-of-prophet-46864fa9c55a
[2] Traditional time series analysis step by step: https://www.kaggle.com/freespirit08/time-series-for-beginners-with-arima
[2] Dealing with time series data: https://online.stat.psu.edu/stat510/lesson/1
[3] Catboost is slick : https://catboost.ai/docs/concepts/tutorials.html

Multiple The right time Group fashion making use of Apache Trigger and Facebook Visionary

#datascience #machinelearning #timeseries

This video is part of Time Series playlist here – https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK

One major challenge with time series in real world is dealing with multiple time series, Be it retailers who have millions of product and every product having different sales cycle or manufacturing industry dealing with hundreds of machinery. In such cases we need systems and solution that can help distribute time series model building across distributed nodes to enable high parallelism. In this video we will see how we can use facebook prophet to model and Apache Spark to distribute across multiple nodes