Posts

How to Download Public and Membership Youtube Video

If you want to download an entire playlist at once, just put the playlist url into yt-dlp If you haven't installed yt-dlp yet, and have no idea what the hell I'm talking about, here are instructions Steps: 1. Download https://github.com/yt-dlp/yt-dlp/releases/latest/download/yt-dlp.exe 2. Install ffmpeg (Optional, but recommended) Download https://github.com/yt-dlp/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-win64-gpl.zip 3. Extract the files and find ffmpeg.exe and ffprobe.exe inside the bin folder 4. Copy those two files to the same folder as yt-dlp.exe 5. Open windows explorer to the folder where you downloaded yt-dlp.exe 6. In the address bar of the the folder that yt-dlp.exe is in, type cmd and press enter 7. Then in the terminal window that opens, type yt-dlp "URL" replacing URL with the link u want to download. Wait for your video to finish downloading :smile: NOTE: If you want to download Youtube Membership videos that require an ...
Post a Comment
Read more

How to Set Up Your Business Email on Android with Hostinger

https://www.hostinger.in/tutorials/how-to-setup-email-on-android/
Post a Comment
Read more

How to Debug Android TV App using IP

How to Connect Android TV to Your PC Using ADB Normally, we use a USB cable to connect a PC with an Android. Since this is not feasible with Android TVs, manufacturers allow you to  set up ADB wirelessly . To do that: On your Android TV, go to  Settings > Device Preferences > About > Status  and note down the  IP address . Open the command prompt on your PC and enter the command  adb connect <the IP address> . tiny adb on desktop enter ip address You will get a prompt on your Android TV asking you to authorize a connection to the computer. Tap on  OK . To check if you have successfully established the ADB connection to your Android TV, enter the command  adb devices  and see if the device shows up under  List of devices attached . Source:  https://www.makeuseof.com/how-to-use-adb-on-android-tv/
Post a Comment
Read more

Gitlab cicd with hostinger

 ###Step1: give access of gitlab to hostinger ( one time step )  ### 1. Goto Hostinger -> advanced -> Git -> Copy SSH 2. Goto gitlab Profile -> SSH Key ( Paste Hostinger ssh key ) ### step 2: create repository in Hostinger ### 1. Goto hostinger -> advanced -> GIT -> create a new repository ( enter gitlab project repository ssh url )  2. Select branch and enter directory of hostinger  3. Click on Create button  ### for CiCd ( auto deployment) ### 1. Click on auto deployment and copy webhook url  2. Paste this URL into gitlab repository's settings ->  webhook .   ### End ### Reference - https://www.youtube.com/watch?v=bj7XvNaYA5U
Post a Comment
Read more

create a CRUD web application with Google Apps Script

  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...
Post a Comment
Read more

sales forecasting ML model in retail using the Prophet algorithm from the Facebook Prophet library

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...
Post a Comment
Read more

sales forecasting using a time series ML model called SARIMA

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...
Post a Comment
Read more