Objective: To solve an Otto cycle and plot the PV diagram along with Thermal efficiency calculation using Matlab.     Drive File Link:   Main File Link : https://drive.google.com/file/d/1fymHWnRGYAKNGAWOp4f4NazFv8x7i8Sz/view?usp=sharing Function File Link : https://drive.google.com/file/d/1nkS07s6tP-PHEeybTXwPMPj9SENHAYGo/view?usp=sharing…

Aim : To Simulate the Transient Movement of a simple pendulum and create an animation video of the pendulum motion using Matlab. Google Drive Link for Code and Video: https://drive.google.com/drive/folders/1KZ5BCt0fv_hbbTscJHoTJDSyBrN2Pb4l?usp=sharing % Main Code close all clear all clc %Inputs b = 0.05; l=1; m=1;…

Objective: To Write a code in MATLAB to optimise the stalagmite function and find the Global Maxima of the function.   Concept: Genetic Algorithm is a serach based optimization technique based on the principles of Genetics and Natural Selection. Its is frequently used to find optimal or near optimal solutions to difficult…

Aim: To write a program to Parse the NASA Thermodynamic data file and calucalate the Thermodynamic properties of various gas elements using Matlab. Objective: 1.Write a Function that extracts the 14 co-efficient and calculate the enthalpy, entropy and specific heats for all species. 2.Calculate molecular weight of each…

Market Analysis using Data Science Models:

Building a Predicitve Model on Real time Manufacturing Sector Data Sheet:

Applying Different types of Gradient Descent to Improve Model Performance:  

Applying Logistic Regression with Output having 3 Classes and 2 Input Features:  

Neural Network with 3 Inputs, 4 Hidden Layers and Sigmoid Activation Function:

Applying Linear Regression and Check for Bias/ Variance:

Applying Gradient Boosting:

Applying Artificial Neural Network (ANN):

Activation Functions Formulae:

Image Classification using Convolution Neural Network:

Applying Vanishing Gradient in RNN:

Apllying NLP Alogrithms to Filter Languages:

Credit Card Fraud Detection using Machine Learning:

Logistic Regression:

Artificial Neural Network:

Random Variables: A random variable is a variable whose possible values are numerial outcomes of a rondom experiment. There are to types of random variables.   1.Discreate Random Variables is one whihc take on only a countable number of distinct values such as 0,1,2,3,... discrete random variables are usually counts.…

Skewness: Skewness refers to a distribution or asymmetry that deviates from the symmetrical bell curve, or normal distribution, in a set of data. If the curve iis shifted to the left or to the right, it is aid to be skewed. Skewness can be quantified as a representation of the extent to which a given distribution varies…

Gradient Descent: Gradient Descent is an algorithm that solves optimization problems using first-order iterations. Since it is designed to find the local minimum of a differential function, gradient descent is widely used in machine learning models to find the best parameters that minimize the model's cost function

Supervised Machine Learning Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. The labelled data means some input data is already tagged with the correct output. In supervised learning, the training…

1. Regression Regression algorithms are used if there is a relationship between the input variable and the output variable. It is used for the prediction of continuous variables, such as Weather forecasting, Market Trends, etc. Below are some popular Regression algorithms which come under supervised learning: Linear Regression…

Neural Networks:   A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

K-Means Clustering Algorithm K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science. In this topic, we will learn what is K-means clustering algorithm, how the algorithm works, along with the Python implementation of k-means clustering.…

Project 1

Project 1: