Maurice  Larson

Maurice Larson

1597658580

Why Do I Get Different Results Each Time in Machine Learning?

Perhaps your results differ from a tutorial and you want to understand why.

Perhaps your model is making different predictions each time it is trained, even when it is trained on the same data set each time.

This is to be expected and might even be a feature of the algorithm, not a bug.

In this tutorial, you will discover why you can expect different results when using machine learning algorithms.

After completing this tutorial, you will know:

  • Machine learning algorithms will train different models if the training dataset is changed.
  • Stochastic machine learning algorithms use randomness during learning, ensuring a different model is trained each run.
  • Differences in the development environment, such as software versions and CPU type, can cause rounding error differences in predictions and model evaluations.

#machine-learning

What is GEEK

Buddha Community

Why Do I Get Different Results Each Time in Machine Learning?
Shubham Ankit

Shubham Ankit

1657081614

How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)

How to Automate Excel with Python

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation

What is OPENPYXL

Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

Sheet: A sheet is a single page composed of cells for organizing data.

Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

Row: A row is a horizontal line represented by a number (1,2, etc.).

Column: A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

pip install openpyxl

CREATE A NEW WORKBOOK

We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.

from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws = wb.active
#creating new worksheets by using the create_sheet method

ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position

#Renaming the sheet
ws.title = "Example"

#save the workbook
wb.save(filename = "example.xlsx")

READING DATA FROM WORKBOOK

We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.

#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")

 

GETTING SHEETS FROM THE LOADED WORKBOOK

 

#getting sheet names
wb.sheetnames
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']

#getting a particular sheet
sheet1 = wb["sheet2"]

#getting sheet title
sheet1.title
result = 'sheet2'

#Getting the active sheet
sheetactive = wb.active
result = 'sheet1'

 

ACCESSING CELLS AND CELL VALUES

 

#get a cell from the sheet
sheet1["A1"] <
  Cell 'Sheet1'.A1 >

  #get the cell value
ws["A1"].value 'Segment'

#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)
d.value
10

 

ITERATING THROUGH ROWS AND COLUMNS

 

#looping through each row and column
for x in range(1, 5):
  for y in range(1, 5):
  print(x, y, ws.cell(row = x, column = y)
    .value)

#getting the highest row number
ws.max_row
701

#getting the highest column number
ws.max_column
19

There are two functions for iterating through rows and columns.

Iter_rows() => returns the rows
Iter_cols() => returns the columns {
  min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.

Example:

#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
  for cell in row:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C3 >

  #iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
  for cell in col:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.C3 >

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.


Example:

for row in ws.values:
  for value in row:
  print(value)

 

WRITING DATA TO AN EXCEL FILE

Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.

 

CREATING AND SAVING A NEW WORKBOOK

 

#creates a new workbook
wb = openpyxl.Workbook()

#saving the workbook
wb.save("new.xlsx")

 

ADDING AND REMOVING SHEETS

 

#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")

#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")

#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']

#deleting a sheet
del wb['sheet 0']

#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']

 

ADDING CELL VALUES

 

#checking the sheet value
ws['B2'].value
null

#adding value to cell
ws['B2'] = 367

#checking value
ws['B2'].value
367

 

ADDING FORMULAS

 

We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.
 

For example:

import openpyxl
from openpyxl
import Workbook

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']

ws['A9'] = '=SUM(A2:A8)'

wb.save("new2.xlsx")

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.

image

 

MERGE/UNMERGE CELLS

Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

For example:
Merge cells

#merge cells B2 to C9
ws.merge_cells('B2:C9')
ws['B2'] = "Merged cells"

Adding the above code to the previous example will merge cells as below.

image

UNMERGE CELLS

 

#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')

The above code will unmerge cells from B2 to C9.

INSERTING AN IMAGE

To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.

Example:

import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3

ws.add_image(img, 'A3')

wb.save("new2.xlsx")

Result:

image

CREATING CHARTS

Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:

Example

import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series

wb = openpyxl.load_workbook("example.xlsx")
ws = wb.active

values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
chart.add_data(values)
ws.add_chart(chart, "E3")
wb.save("MyChart.xlsx")

Result
image


How to Automate Excel with Python with Video Tutorial

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling

📄 Resources 📄
OpenPyXL Docs: https://openpyxl.readthedocs.io/en/stable/ 
Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial 
Subscribe: https://www.youtube.com/c/TechWithTim/featured 

#python 

Nora Joy

1607006620

Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers

sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Connor Mills

Connor Mills

1659063683

HTML, CSS & JavaScript Project: Build Cocktail App

In this tutorial, we will learn how to create a cocktail app with HTML, CSS and Javascript.

Create a cocktail app where the user can search a cocktail of choice and the app displays the ingredients and instructions to make the cocktail. We use 'The Cocktail DB' API to fetch information required for our app.

Project Folder Structure:

Before we start coding let us take look at the project folder structure. We create a project folder called – ‘Cocktail App’. Inside this folder, we have three files. These files are index.html, style.css and script.js.

HTML:

We start with the HTML code. First, copy the code below and paste it into your HTML document.

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>Cocktail App</title>
    <!-- Google Font -->
    <link
      href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;600&display=swap"
      rel="stylesheet"
    />
    <!-- Stylesheet -->
    <link rel="stylesheet" href="style.css" />
  </head>
  <body>
    <div class="container">
      <div class="search-container">
        <input
          type="text"
          placeholder="Type a cocktail name..."
          id="user-inp"
          value="margarita"
        />
        <button id="search-btn">Search</button>
      </div>
      <div id="result"></div>
    </div>
    <!-- Script -->
    <script src="script.js"></script>
  </body>
</html>

CSS:

Next, we style this app using CSS. For this copy, the code provided to you below and paste it into your stylesheet.

* {
  padding: 0;
  margin: 0;
  box-sizing: border-box;
  font-family: "Poppins", sans-serif;
}
body {
  height: 100vh;
  background: linear-gradient(#5372f0 50%, #000000 50%);
}
.container {
  position: absolute;
  transform: translate(-50%, -50%);
  top: 50%;
  left: 50%;
  width: 90vw;
  max-width: 37.5em;
  background-color: #ffffff;
  padding: 1.8em;
  border-radius: 0.6em;
  box-shadow: 0 1em 3em rgba(2, 9, 38, 0.25);
}
.search-container {
  display: grid;
  grid-template-columns: 9fr 3fr;
  gap: 1em;
  margin-bottom: 1.2em;
}
.search-container input {
  font-size: 1em;
  padding: 0.6em 0.3em;
  border: none;
  outline: none;
  color: #1f194c;
  border-bottom: 1.5px solid #1f194c;
}
.search-container input:focus {
  border-color: #5372f0;
}
.search-container button {
  font-size: 1em;
  border-radius: 2em;
  background-color: #5372f0;
  border: none;
  outline: none;
  color: #ffffff;
  cursor: pointer;
}
#result {
  color: #575a7b;
  line-height: 2em;
}
#result img {
  display: block;
  max-width: 12.5em;
  margin: auto;
}
#result h2 {
  font-size: 1.25em;
  margin: 0.8em 0 1.6em 0;
  text-align: center;
  text-transform: uppercase;
  font-weight: 600;
  letter-spacing: 0.05em;
  color: #1f194c;
  position: relative;
}
#result h2:before {
  content: "";
  position: absolute;
  width: 15%;
  height: 3px;
  background-color: #5372f0;
  left: 42.5%;
  bottom: -0.3em;
}
#result h3 {
  font-size: 1.1em;
  font-weight: 600;
  margin-bottom: 0.2em;
  color: #1f194c;
}
#result ul {
  margin-bottom: 1em;
  margin-left: 1.8em;
  display: grid;
  grid-template-columns: auto auto;
}
#result li {
  margin-bottom: 0.3em;
}
#result p {
  text-align: justify;
  font-weight: 400;
  font-size: 0.95em;
}
.msg {
  text-align: center;
}
@media screen and (max-width: 600px) {
  .container {
    font-size: 14px;
  }
}

Javascript:

Lastly, we implement the functionality using Javascript. Now copy the code below and paste it into your script file.

let result = document.getElementById("result");
let searchBtn = document.getElementById("search-btn");
let url = "https://thecocktaildb.com/api/json/v1/1/search.php?s=";
let getInfo = () => {
  let userInp = document.getElementById("user-inp").value;
  if (userInp.length == 0) {
    result.innerHTML = `<h3 class="msg">The input field cannot be empty</h3>`;
  } else {
    fetch(url + userInp)
      .then((response) => response.json())
      .then((data) => {
        document.getElementById("user-inp").value = "";
        console.log(data);
        console.log(data.drinks[0]);
        let myDrink = data.drinks[0];
        console.log(myDrink.strDrink);
        console.log(myDrink.strDrinkThumb);
        console.log(myDrink.strInstructions);
        let count = 1;
        let ingredients = [];
        for (let i in myDrink) {
          let ingredient = "";
          let measure = "";
          if (i.startsWith("strIngredient") && myDrink[i]) {
            ingredient = myDrink[i];
            if (myDrink[`strMeasure` + count]) {
              measure = myDrink[`strMeasure` + count];
            } else {
              measure = "";
            }
            count += 1;
            ingredients.push(`${measure} ${ingredient}`);
          }
        }
        console.log(ingredients);
        result.innerHTML = `
      <img src=${myDrink.strDrinkThumb}>
      <h2>${myDrink.strDrink}</h2>
      <h3>Ingredients:</h3>
      <ul class="ingredients"></ul>
      <h3>Instructions:</h3>
      <p>${myDrink.strInstructions}</p>
      `;
        let ingredientsCon = document.querySelector(".ingredients");
        ingredients.forEach((item) => {
          let listItem = document.createElement("li");
          listItem.innerText = item;
          ingredientsCon.appendChild(listItem);
        });
      })
      .catch(() => {
        result.innerHTML = `<h3 class="msg">Please enter a valid input</h3>`;
      });
  }
};
window.addEventListener("load", getInfo);
searchBtn.addEventListener("click", getInfo);

📁 Download Source Code:  https://www.codingartistweb.com

#html #css #javascript 

Nora Joy

1604154094

Hire Machine Learning Developers in India

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.
**
Services**
Product Engineering & Development
Re-engineering
Maintenance / Support / Sustenance
Integration / Data Management
QA & Automation
Reach us 917483546629

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.

Services

Product Engineering & Development

Re-engineering

Maintenance / Support / Sustenance

Integration / Data Management

QA & Automation

Reach us 917483546629

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers