1A.English Dictionary App #creating menu example#GUI based dictionaryfrom tkinter import *window = Tk()window.title("Dictionary App")window.geometry("600x400+50+50")f = open('words.txt','w')coll_words = {}def addNewWord(): f = open('words.txt','w') word = input('Enter a word : ') meaning = input('Enter its meaning : ')…

1.ANS # to avoid warnings from pythonimport warningswarnings.filterwarnings('ignore') import pandas as pdcustomer_detail_df=pd.read_csv(r'C:\Users\Chinna\Downloads\customer_details.csv') # ' .\ ' represent the current directorycustomer_detail_dfcustomer_detail_df.columns=["customer_id","Gender","age","driving licence present","region…

in this project i had learn how to create schema and how to import file  .csv formate from existing files and how to execute no.of conditions and create ecomerce platform.. Business model Customer to Customer (C2C) allows customers to do business with each other. This model is growing fast with e-commerce platforms…

in this project i had learned how to use MsExcel and jupitor Notebook,and mysql workbench then execute all statements     Q5.Import first 1500 rows of tables (College_A_HS, College_A_SE, College_A_SJ, College_B_HS, College_B_SE, College_B_SJ) into MS Excel. Ans:-(Same procedure for all other columns) Q13.Import the created…

Business RequirementsTitle: Women in STEM Fields.In this project i had learned and solve these problems like as below...-A pie graph of women inBiological ScientistsChemists & Materials ScientistsComputer & Mathematical OccupationsEngineers & Architects-The trend of percent women graduating in Computer science…

1. literacy rates in Tamilnadu. It is distributed according to the cities and their ranks. It contains the population, as per the gender. 2.This dashboard gives us the insight about the female population when compared to the total, and it addresses 3. the state is on literacy wise. 4.It shows us the status of each city…

project 1 using these models machine learning models Logistic Regression, K-Nearest Neighbours Classifier, Support Vector machine, Decision Tree Classifier, Random Forest Classifier on the given dataset.

 Apply Principal component analysis, K-Means Clustering & Hierarchical Clustering.

ABC NewTab and Add formating from the customize ribbens   Clean the Data with respect to date by proper date formatting and change values of 2003 to 2019, 2004 to 2020 and 2005 to 2021.    Make summary of Products Sales by providing Total Sum of Sales Average of Sales Standard Deviation of Sales Variance of Sales Maximum…

Public Sub Dataintopdf()'Create and Assian VariablesDim SaveLocation As StringDim SheetArray As StringSaveLocation = "C:\Users\Chinna\OneDrive\Desktop"SheetArray = Array("Copy Data up to 2022 cells", "Charts", "List Functions")'Select Specific Sheets From WorkBook,The Save All As PdfSheets(SheetArray).Select'ActiveSheet.Export…

1.Why there is a difference in the formula of variance for population and sample Variance : `sigma``σ^2`= `∑``(``Xi`-`μx`)``^2``/``N`   (population)` Sample : `S^2`= `∑``(``Xi`-`μx`)``^2``/``N-1`   (sample)` So When We Use N insted of N-1, We have an error…

1.Perform Gradient Descent in Python with any loss function Gradient Descent is an iterative algorithm that is used to minimize a function by finding the optimal parameters. Gradient Descent can be applied to any dimension function i.e. 1-D, 2-D, 3-D. In this article, we will be working on finding global minima for…

1.Calculate all 4 business moments using pen and paper for the below data set?   2.What is the significance of expected value when simple mean (Sum of all observations/number of observations) is already in place? It is true that simple mean is sufficient enough for having an idea about the data by computing with it's formula (Sum…

1.Pros and cons of SVM * Brief explaination on Support Vector Machine (SVM). Pros and cons. a) Support Vector Machine (SVM) The SVM algorithm was invented by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963. SVMs comes under the supervised machine learning models and it can be applied to classification (SVC —…

1.What are the pros and cons of knn Pros: K-NN is pretty intuitive and simple: K-NN algorithm is very simple to understand and equally easy to implement. To classify the new data point K-NN algorithm reads through whole dataset to find out K nearest neighbors. K-NN has no assumptions: K-NN is a non-parametric algorithm…

1.What is a Neural Network? Neural networks are a class of machine learning algorithms used to model complex patterns in datasets using multiple hidden layers and non-linear activation functions. 2.What is deep learning? Deep learning is a type of machine learning, which is a subset of artificial intelligence.deep learning is…

1.How does similarity is calculated if data is categorical in nature Brief introduction to K-means algorithm and similarity feature determination a) K-means algorithm and similarity feature determination K-means algorithm is a simple iterative machine learning algorithm used in unsupervised learning. The basic idea behind…

About the Dataset: This is a raw dataset about cars in 1985. This data set consists of three types of entities:(a) the specification of an auto in terms of various characteristics,(b) its assigned insurance risk rating,(c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree…

There are two major types of clustering in Unsupervised Machine Learning. These are: KMeans Clustering. Hierarchichal Clustering. We are going to perform both the algorithms on the dataset and find out the differences between the results. Overview: What is KMeans Clustering? Performing Kmeans Clustering. What is Hierarchical…

1. Explain the data. Check It is supervised or Unsupervised. Demonstrate? it is the supervised data because it have contain Labeled data. Supervised and unsupervised learning have one key difference. Supervised learning uses labeled datasets, whereas unsupervised learning uses unlabeled datasets. By “labeled”…

1.Display the data of white wine and red wine 2.Get the size of both white wine and red wine. 3.Get the number of null in both type of wines 4.Check for duplicates and remove them in both type of wine data 5.Get the number of uniques values in all Feature and print them 6.Print the mean density of both the sets 7.Combine…

Project 1 - COVID-19 Vaccinations Trend Analysis The Dataset is given below. Kindly use the same. Deliverables of this project: Detailed Documentations (attach a word file and also paste in the text box) Power BI Report / .PBIX file Present the project like a Case Study using Power Point The Detailed Documentation of the…

Project 2 - Supply and Demand Gap Analysis Use any Dataset of your own (download from kaggle) Supply and Demand gap analyses are widely used by companies as strategic management decision tools. This project is to identify the gap between demand and supply so that organization can take action to minimize those gaps. The…