Random Forest Regression | Python

[@RuchiSehgal-l3l]

Thank you so much sir, this video came up just when I needed it! You are a godsent angel for me, thank you so much for helping students by posting such videos! Keep up the good work!

[@RestartUPSC2026]

Thank you very much! Today I learned something.

[@StatsWire]

You're welcome Khalid

[@hilolaamankulova6767]

Thank you so much, this is very useful for me.

[@StatsWire]

Thank you for your kind words.

[@RollerBladingSports]

Would be nice, if you'd left the dataframe here available for download.

[@StatsWire]

Thank you for the suggestion. I have added the dataset link for you. Please download it from here GitHub link: github.com/siddiquiamir/Data/blob/master/bike_rentals.csv

[@RollerBladingSports]

@@StatsWire Thank you for quick response!

[@StatsWire]

@@RollerBladingSports You're welcome

[@robloxkhan5761]

The error MSE is too big. You can normalize the data to train the RF to reduce error

[@StatsWire]

Yes!

[@ouissalsadouni2837]

Amazing ! thank you

[@StatsWire]

I'm glad you liked it!

[@tanushgowda7453]

Sir you removed the date and time column but on what basis can you give us a in depth reason because date and time is also a important factor

[@francescocorallo4952]

Thank you very much. I have a question. How to plot in a graph the y_test and y_pred then make a forecast for the next 30 days and visualize it?

[@StatsWire]

Hi, What kind of values do you want to plot?

[@francescocorallo4952]

@@StatsWire In my case I have to face with time series related to pumping performance and I would like to make a model able to give me a forecast prediction. Is related to predictive maintenance. Could you help me or show me where to find such sample model? I appreciate your help and your way to explain. In case could you give me your email and we may discuss there.

[@StatsWire]

@@francescocorallo4952 You can make ARIMA models.

[@francescocorallo4952]

@@StatsWire I tried with arima but the overcome was not really good with an important mismatch in the history phase so I tried using random forrest linear regression model approach using R code achieving a better history match. Could you give me the same example but using python? What is your feedback?

[@adrianduran3745]

Why does y_pred require an = after now? Is this a new update? What should I set it = to?

[@StatsWire]

We are saving the results in y_pred so we are using =

[@rg50241]

can u share the commands for getting the co efficient and intercepts

[@StatsWire]

You can try this .coef_

[@StevenSmith68828]

How do you forecast?

[@StatsWire]

You can use time series.

[@Parth-kt9xm]

Where can I get this dataset?

[@StatsWire]

You can find it here: github.com/siddiquiamir/Data

[@brahmialexandre633]

Bro, u're amazing

[@StatsWire]

Thank you so much:)

[@levonmcgregor6113]

thank you!

[@StatsWire]

You're welcome!

[@inas2323]

Thank you very much

[@StatsWire]

You're welcome!

[@jongcheulkim7284]

Thank you.

[@StatsWire]

You're welcome

[@ShankarTmsw]

why date time dropping why ??? sir

[@StatsWire]

This will not help in prediction.

[@husseinhmaid8326]

Can you share the csv Files?

[@StatsWire]

Please download it from here: github.com/siddiquiamir/Data/blob/master/bike_rentals.csv

[@sumitrodagai7706]

dataset link share me please

[@StatsWire]

Please find the dataset on my GitHub account " github.com/siddiquiamir/Data