project1 Submitted 1.English Dictionary App(I made this by using menu driven method) 2.Library Management Application(i made this by using CRUD method )

2nd project is submitted

Project 1 Submitted

project -2 submitted.   answer no.4 in jupyter notebook answer no. 5 and 13 in excel sheet answer no.1,2,3,6 to 16 except 13 in sql file  

project 1 submitted.

project 2 Submitted.

Project 1 completed Below is link...I also attached the file. https://public.tableau.com/views/project1_16706926573300/Dashboard1?:language=en-US&:display_count=n&:origin=viz_share_link

Project -2 LITERACY RATE IN TAMILNADU submitted.

Project 1  Submitted

Project 2 Submitted. Link:https://docs.google.com/spreadsheets/d/1uERr1KpsBKhKjKe2mDA77ghU08gYi_1h/edit?usp=share_link&ouid=102074733978836310337&rtpof=true&sd=true   A lot of problems created while executing  any formula.... sometimes it take  hours to execute one Formula...2-3 times I canceled…

Project 1 Submitted.

Project 2 submitted. Submitted my  powerbi workbook and Pdf of ppt.  

Project -1 Submitted

Project -2 (Submitted)

Answer no.1 The formula for population variance is: σ² = Σ(x-μ)²/N where σ² is the population variance, Σ(x-μ)² is the sum of squared deviations of each data point from the population mean μ, and N is the total number of data points in the population. On the other hand,…

Answer no.1 1.  mean µ= ∑(x1) * p(x)             = 0*0.35+1*0.25+ 2*0.15 + 3*0.15 + 4*0.10             = 0 + 0.25 + 0.3 +0.45 +0.4             = 1.4 2. Second business movement Variance σ2…

Answer no.2  The main differences between L1 and L2 gradient descent methods in regularization can be summarized as follows: Regularization term: L1 regularization (Lasso): The regularization term is proportional to the sum of the absolute values of the weights. L2 regularization (Ridge): The regularization term is…

Answer no.1 Pros of SVM: Effective in high-dimensional spaces. Robust to overfitting. Versatile kernels. Effective in handling nonlinear data. Memory efficient. Cons of SVM: Computationally intensive. Sensitivity to parameter tuning. Lack of probabilistic output. Difficulty in handling large datasets. Black box nature.…

Answer no. 1 subitted in .ipynb form.   Answer no.2 The k-nearest neighbors (KNN) algorithm is a non-parametric classification and regression method that operates on the principle of finding the k nearest data points in the training set to a given test point and using the majority vote (for classification) or averaging…

Answer no.1 When dealing with categorical data in clustering algorithms like K-means, you need to use a similarity or distance measure that is appropriate for categorical variables. Two common distance measures for categorical data are the Hamming distance and the Jaccard similarity coefficient. Hamming Distance: The Hamming…

Answers  are submited in ms word pdf.

Project 1 - Data cleaning and EDA on Automobile 1985 dataset Submitted.

Project -2 Submitted. I am using car price assignment dataset in this challenge.