KriyaNiti HelpDesk

  To create a CRUD web application with Google Apps Script, we will use Google Sheets as our database and build a web interface using HTML and JavaScript. Here is an example code for a basic CRUD web application: Step 1: Create a new Google Sheet To begin, open a new Google Sheet and give it a name. This sheet will serve as the database for our CRUD application. In the first row, add the following headers: "ID", "Name", "Email", "Phone". Step 2: Create a new Google Apps Script Next, click on "Tools" in the menu bar and select "Script editor". This will open the Google Apps Script editor. In the editor, create a new script file and give it a name. Step 3: Create the HTML interface In the script editor, create a new HTML file by clicking on "File" -> "New" -> "Html file". Name the file "index". In this file, we will create the interface for our CRUD application. Here is an example co...

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...

This code loads the sales data from a CSV file, sets the date as the index, and aggregates the sales by day. It then splits the data into training and testing sets, fits a SARIMA model to the training data, and forecasts the sales for the next 30 days. Finally, it plots the actual and forecasted sales, along with the confidence intervals. Note that you may need to adjust the model parameters (order and seasonal_order) depending on your data.  import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.statespace.sarimax import SARIMAX # Load sales data sales_data = pd.read_csv('sales_data.csv', parse_dates=['date']) # Set date as index and aggregate sales by day sales_data.set_index('date', inplace=True) sales_data = sales_data.resample('D').sum() # Split data into training and testing sets train_data = sales_data.iloc[:-30] test_data = sales_data.iloc[-30:] # Fit SARIMA model to training data model = SARIMAX(train_data, o...

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...

  Here are some examples of machine learning models that can be used in an IT company management system and their corresponding code: Resource allocation using clustering: # Load the required libraries and data import pandas as pd from sklearn.cluster import KMeans data = pd.read_csv('resource_data.csv') # Apply KMeans clustering to identify groups of resources with similar skill sets kmeans = KMeans(n_clusters=3) kmeans.fit(data[['skill_1', 'skill_2', 'skill_3']]) # Assign each resource to a cluster cluster_labels = kmeans.predict(data[['skill_1', 'skill_2', 'skill_3']]) data['cluster'] = cluster_labels # Allocate resources based on cluster assignments project_data = pd.read_csv('project_data.csv') project_data['resource_cluster'] = kmeans.predict(project_data[['required_skill_1', 'required_skill_2', 'required_skill_3']]) resource_allocation = project_data.groupby('resource_clus...