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Paramita
Приєднався 27 кві 2020
Data science project
Відео
List in Python
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List in Python List operations Download NOTEs: github.com/paramitadas1/Python_Notes/blob/main/List.docx #list
Basic Data Type & Variable in Python- For beginners
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Basic Data Type & Variable in Python- For beginners. Integer Float String NOTES: github.com/paramitadas1/Python_Notes/blob/main/Pyhton_Introduction_NOTES.docx
Install Python, Anaconda, Jupyter Notebook, Setup Jupyter Notebook for beginners.
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Install Python, Anaconda, Jupyter Notebook, Setup Jupyter Notebook for beginners. Download Notes: github.com/paramitadas1/Python_Notes
ARIMA in python. Best way to Identify p d q. Time Serie Forecasting. With Example. Free Notes.
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ARIMA in python. Best way to Identify p d q. All different ways to identify pdq Time Serie Forecasting. With Example. Free Notes on ARIMA. Practice dataset. github link for Notes: github.com/paramitadas1/ARIMA_dataset github link for practice data. link for Stationarity: ua-cam.com/video/R69TZFNEao4/v-deo.html
What is stationarity ? How to make a series stationary? Stationarity in python-codes with example
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What is stationarity ? How to make a series stationary? Stationarity in python-codes #stationarity #pythonCode #HowToMakeStationary # pyhtonCodes #Examples
Holt winters Model, Easiest Times series Model. Additive multiplicative trend and seasonality
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Holt winters Model, Easiest Times series model. Build your 1st time series model. Theory Additive multiplicative trend or seasonality. Part2 theory Python codes. #HoltWintersTimeSeriesModel #EasiestTimesSeriesModel #TimeSeriesPythonCode #HoltWinter Github link for Jupyter Notebook: github.com/paramitadas1/Holt-winter
Holt winters Model, Easiest Times series Model. Additive multiplicative trend and seasonality.
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Holt winters Model, Easiest Times series model. Build your 1st time series model. Theory Additive multiplicative trend or seasonality. Part1 theory. #HoltWinters #HoltWintersTimeSeriesForecasting #EasiestTimesSeriesModel
Handling missing values in Python Explained with example Fillna dropna sklearn KNN Model Imputation
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Handling missing values in Python Explained with example. Replace Missing Data in python. Fillna , dropna , sklearn impute , KNN , Model Imputation Missing Value Analysis. Visualization of Missing values. Missing value EDA. missingno:: ua-cam.com/video/czkHO4_Zkjw/v-deo.html Github link for Working missing data file: github.com/paramitadas1/Handling-missing-values-in-Python-Explained-with-examp...
Missing Value Analysis. Visualization of Missing values. Missing value EDA. missingno
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Missing Value Analysis. Visualization of Missing values. Missing value EDA. missingno Part 2 : : Predict Chritiano Ronaldo will score a Goal or not using Data science. Logistic regression Part 2 of 5:Missing Value Analysis. Visualization of Missing values . Download csv file from: github.com/paramitadas1/Predict-goal-or-miss Chritiano-Ronaldo Part1 : ua-cam.com/video/gkuwq5zitWY/v-deo.html Play...
How to start Data science project from Scratch. Exploratory data analysis. Predict Goal
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How to do Data science project from Scratch? Predict Chritiano Ronaldo will score a Goal or not using Data science Exploratory data analysis. Numeric and Categoric feature. Part 1 of 5. Download csv file from: github.com/paramitadas1/Predict-goal-or-miss Chritiano-Ronaldo #ExploratoryDataAnalysis
Subset ,filter, Select multiple rows and columns from a pandas DataFrame using iloc , loc , ix ....
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Select multiple rows and columns from a pandas DataFrame. Best way to select rows and columns , Best practices for row and column selection using the loc, iloc, and ix methods... Python Pandas subsetting for real bussines tasks . #SubsetInPython #filter #SelectMultipleRowsColumns #iloc #loc #ix
Entity ruler SPACY. Real life Example. Match a regex Pattern as a Custom Named Entity .
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Entity ruler SPACY. What and How ? Real life Example End to end How to Create and implement on a Dataframe column With def and for loop with regex pattern match. regex pattern to Custom Named Entity Not just printing the output
Thanks Paramita, this is a great and helpful tutorial!!!...
you are best
What to do when our data is yearly based...is it seasonal or nonseasonal
do you have git repo?
Hi @paramita, can you upload sarima.csv?
Thanks for the well explained theory
Hi its great content and knowledge sharing ! thanks ! please keep sharing more content like this
very good understanding of your expiation
thanks dor notes and data, where si the code ?
Thank you so much mam
thank you so much Paramita. Very well-explained.
One Que, My data is not stationary but as you mentioned i went with custom for loop to identify the p,d,q values and there d was 0 with lowest RMSE, but still data is not stationary so d should be one if i take diff by 1 , am i right? why that for loop suggests 0 value for d?
seasonal_decompose(df,model='additive',freq=4).plot(); this code may not be executes you can use period keyword instead of freq in that statement ()line 46 in original github code !
Hai how to use data in multiple sku along with sales date with two years
Dam Arima you look good 😍
Nicely and Perfectly Explained. Kudos to Paramita
can u give a full summary of machine learning explaing each M.L algorithm so that we can understand everything what involves in M.L
It seems that the data set that you provide has been corrupted. It contains information of just a month.
Clearly explained mam... Thanks alot
very helpful thanku
Finally, I have found a great teacher who can explain time series concepts with ease. It would be helpful if you could create a video on deploying machine learning models.
I agree with teaching how to get this deployed.
Won't we use SARIMA ? Given we are working on sales forecasting? This type of data has seasonality
Please Ma'am start to teach . Your content is very great.
Thank you :) It helps so much
Excellent video. Well explained & detailed.
Hello, Good day. If you can be of an assistance please. I working on a project work that has to do with forecasting using ARIMA. Can you please help me?
Thank you very much paramita..this video really helped me alot . practical implementation is what i was looking for. You deserve more ..thank you once again
please give solution ---- Input In [14] model=ARIMA(df.train,order=(pdq).fit() ^ IndentationError: expected an indented block
where is the video on acf and apcf plots
Your explanations are among the best. BTW... what about the SARIMA video? :)
mam why stopped posting videos. Its good
Many thanks to you. Great videos, very helpful!
Many thanks to you. Great videos, very helpful!
Amazing Lecture Mam
Hi Paramita, Very nicely explained tutorial. The csv that is provided has data only for January of the year 2014. Where can we see the rest of the data? Regards, KM
Sheer Brilliance. Won't ever forget how your channel helped me. May Lord Ram bless you 🙏 💜
Hello Paramita, thank you for explaining it so well. Just one note - in Seasonal decompose, frequency parameter has been deprecated and should be replaced with period parameter
Nice video, well explained, congrats and keep posting!
Thank you Ma'am great tutorial
Very useful tutorial. The best I've found on the net, thank you very much!
Thanks
The data is suitable for SARIMA/Holts Winter Method but you explained with ARIMA........!
You didn’t explain why to make a series stationary… 😠
By this we are removing trend and seasonality for better forecast
you are brilliant ,please continue the rest of the parts out of 5
The iteratools method is outstanding. Thank you for sharing and congratulations for your talent.
Hii I am doing my data scientist course If you could provide more videos It will be a great help Or you can provide your notes plz
Thanks for video. I have some error : model=ARIMA(train,order=(5,0,4)).fit() ------ValueError: The computed initial AR coefficients are not stationary You should induce stationarity, choose a different model order, or you can pass your own start_params.
Hello Paramita, thanks a lot for your video. I wanted to ask you if you've read how to apply forecasting models to time series with multiple SKU (like 500 - 2000) considering the efficiency while running it, thinking of using the forecast once every week. I would really appreciate if you can indicate me a study case or real case in which I can take a look at the approach within the code. Thanks in advance!!
More than Great😍
Thank you so much Ma'am but can you also explain how to do the hourly prediction (24 hrs). I would be helpful if you explain it.