Project 2 - EDA on Vehicle Insurance Customer Data  

Project 1 - English Dictionary App & Library Book Management System. The skills learnt from week 1 to week 6 to create an English Dictionary app that returns definitions of English words.

good challenge i have learn lot from this week about data base and different different types of syntax 

Project 2 - SQL Once a student is passed out from a Institute or College, he/she is known as Alumni of the Institute. Alumni’s career growth plays important role in Institute’s ranking and other networking activities. In this project, career choices of alumni of two Universities will be analyzed with respect……

Nnew Project : Womens In Stems Jobs Tableau Public Link:- https://public.tableau.com/views/Newproject1/STEMJOBsDashboard?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link

Tableau Project: -Link: - https://public.tableau.com/views/Project2_16818752302990/LiteracyrateinTamilNaidu?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link

Aim: The aim of this project is to analyze the trend of COVID-19 vaccinations across different countries and identify any factors that may be influencing the pace of vaccinations. Introduction: COVID-19 pandemic has affected millions of people around the world and the vaccination drive has been initiated globally to combat…

Project 2 - Uber Supply-Demand Gap EDA Aim The aim of this project is to collect and analyze the data in detail of the Pickup point of the Airport and City Request id in Uber Request Data.   Introduction Uber provides the 2016 data. By using this data find out the demand and supply Analysis gap of the cab. The…

QA. Do proper formatting of the data with using borders, font size and appropriate alignment of the content. QB. Make summary of Products Sales by providing Total Sum of Sales Average of Sales Standard Deviation of Sales Variance of Sales Maximum Sales Minimum Sales   QC. Analyse the sales happened by product…

Project: - As Business Analyst working IJS Bank Limited. Currently your organization is working on Loan Applicant Clients for the current quarter. The Manager of the Branch asked you to analyze Loan Applicant’s data and to Present appropriate report on the shared data. The report should be demonstrated using…

Project Summary: Predicting Heart Disease Using Clinical ParametersObjectiveThe primary goal of this project is to develop machine learning models that can predict whether a patient has heart disease based on 13 clinical parameters. The clinical parameters include age, sex, chest pain type, resting blood pressure, cholesterol…

Project Summary: Strategic Allocation of Humanitarian Aid Using Socio-Economic and Health Data1. Project Objective:The goal of this project is to assist an international humanitarian NGO in strategically allocating $120 million in funding. The allocation is to be based on a data-driven analysis that identifies countries…

Pros and Cons of SVMPros:Handling high-dimensional data: SVMs can handle high-dimensional data with a small number of samples, making them suitable for image and text classification tasks.Robust to noise and outliers: SVMs are robust to noisy data and outliers, as they focus on finding the maximum-margin hyperplane that…

After applying KNN, here are the notable differences when comparing it with other models like SVM or Decision Trees:Lazy Learning: KNN is a lazy learner and only performs computation at the time of prediction. Models like SVM or Decision Trees learn a decision boundary during training.Sensitivity to Feature Scaling: KNN…

1. What is a Neural Network?A neural network is a machine learning model inspired by the structure and function of the human brain. It is a collection of interconnected nodes or "neurons" that process and transmit information.Architecture of a Neural NetworkA neural network typically consists of three types of layers:Input…

Calculating similarity for categorical data involves different approaches compared to numerical data. Here are some common methods for measuring similarity between categorical variables:1. Hamming DistanceHamming distance measures the number of positions at which the corresponding elements are different. It is used when…

Exploring different types of machine learning algorithms that can be performed on the 1985 automobile dataset, as well as data cleaning and descriptive analytics.Data Cleaning:Before we dive into machine learning algorithms, it's essential to clean and preprocess the dataset. Here are some steps we can take:Handling missing…

Clustering:The K-Means clustering identified 3 clusters in the dataset, visualized using PCA.Clusters represent different classes of cars based on features like MSRP, horsepower, and weight.Prediction:The model performed well, with an accuracy score of around X% (depending on data), demonstrating that the features…