Welcome to the KriyaNiti Group.
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We are very passionate about simplifying complex task...
Here we provide basic and needy scripts...
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Here is a source code for a simple inventory management system using Google Apps Script: function checkInventory() { var sheet = SpreadsheetApp.getActiveSheet(); var data = sheet.getDataRange().getValues(); var message = "Inventory:\n"; for (var i = 1; i < data.length; i++) { var item = data[i][0]; var quantity = data[i][1]; message += item + ": " + quantity + "\n"; } Browser.msgBox(message); } function updateInventory(item, amount) { var sheet = SpreadsheetApp.getActiveSheet(); var data = sheet.getDataRange().getValues(); for (var i = 1; i < data.length; i++) { if (data[i][0] == item) { var newQuantity = parseInt(data[i][1]) + amount; sheet.getRange(i+1, 2).setValue(newQuantity); break; } } } function submitForm(e) { var i...
sales forecasting ML model in retail using the Prophet algorithm from the Facebook Prophet library: import pandas as pd from fbprophet import Prophet # Load the sales data into a pandas dataframe sales_data = pd.read_csv('sales_data.csv') # Convert the date column to a pandas datetime object sales_data['date'] = pd.to_datetime(sales_data['date']) # Rename the columns to 'ds' and 'y' for Prophet compatibility sales_data = sales_data.rename(columns={'date': 'ds', 'sales': 'y'}) # Create and train the Prophet model model = Prophet() model.fit(sales_data) # Create a future dataframe for the next 30 days future = model.make_future_dataframe(periods=30) # Make sales predictions for the next 30 days sales_predictions = model.predict(future) # Print the sales predictions for the next 30 days print(sales_predictions.tail(30)) In this code, we first load the sales data into a pandas dataframe and convert the date column to a...
“Parsing filters unsupported” error during extraction of RAR in Ubuntu install unrar: sudo apt-get install unrar then to extract, go to the path of the .rar file and run commond : unrar x [filename.rar]
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