TexData Analytics

Hello guys,

It's a new year, new time and back making another series of now-points.

Welcome to our new Time Series Analysis Recipe using python. In this, we cover:

1. Outlier detection
2. Exploratory Data Analysis (EDA)
3. Univariate time series analysis.
4. Additional statistical modelling techniques.
5. Forecasting using supervised and unsupervised learning methods.
6. Forecasting using deep learning.

Time series is an indispensable tool for ensuring a time series model is well-specified, adheres to assumptions, and provides reliable forecasts. Without it, you might end up with misleading models and poor predictive performance.
https://github.com/Jeremiah-Katumo/dsa

Welcome to TexData Analytics,

We can build dynamic and interactive dashboards with the most common metrics and KPIs in a way that is easy to digest. Tap the button to send a message to get started.
Visit https://public.tableau.com/app/profile/jeremykatush for more examples.

Decomposition provides a useful abstract model for thinking about time series generally and for better understanding problems during time series analysis and forecasting. Check my GitHub for complete code https://github.com/Jeremiah-Katumo/stock-time-series-analysis.git

Shiny is a framework in R used by business analysts to create data analysis web applications. It very useful in the market as it can update any data fed into it and anyone can download data from the web applications. This are just some of the uses of shiny.
Click the link to see more https://jeremiah-kurwa.shinyapps.io/World-population-indicators/

Welcome to our Data sessions. As from 7:30 pm to 9:30 pm on every Tuesdays and Thursdays.
Today we'll take you through the fundamentals of R programming and Analysis with R. Tell a friend to tell a friend.


Zoom Link: https://us05web.zoom.us/j/84536452109?pwd=VDVxaTh4bUJUOFI4c1FMbjVHZkVKUT09

Follow me on medium to learn about data preparation for machine learning algorithms.
https://link.medium.com/DRuM5ylDErb

SVMs do not emit any natural measures of feature importance; however in R, we can quantify the importance of each feature using permutations. Predicted class probabilities are returned for the reference class of interest.
The results indicate that OverTime (Yes/No) is the most important feature in predicting attrition.
The images plots permutation scores colored by features (feature effect plots on the probability scale). The one with the highest probability (OverTime) is considered in predicting attrition rate.

We are TexData Analytics. We can solve a wide variety of your data problems. We do data cleaning, visualization, machine learning, creating interactive dashboards etcetera. For more services, please feel free to visit our website or contact us through WhatsApp.