Unsupervised Learning - Kmeans Week 11 Challenge

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Objective: Brief introduction to K-means algorithm and similarity feature determination. Implementation of K-means algorithm in python for automobile dataset. a) K-means algorithm and similarity feature determination K-means algorithm is a simple iterative machine learning algorithm used in unsupervised learning

The prime objective of this article is to implement a CNN to perform image classification on the famous fashion MNIST dataset. Deep learning’s CNN’s have proved to be the state-of-the-art technique for image recognition tasks. Keras is a deep learning library in Python which provides an interface for creating an artificial neural network. It is an open-sourced program. It is built on top of Tensorflow.

Methodology In Uber, the Request Data set the first column is Request id in this column the request-id is present the placed by the customer. In the second column, the pickup point is present in this data only two Airport and City pickup point is present. The next column is Driver Id in this column so many drivers are present each driver assigns a separate driver ID. Another column is Status in this Status column three statuses are present completed, cancelled and No car available. The last two columns are the Request timestamp and Drop Timestamp in this column the request time and drop time are present. The first step is cleaning the data. In data cleaning I cleaned the data with the transform data option present in Power BI because the dataset was not cleaned and included some missing values, so with the help of the duplicates function in Power BI, I cleaned the data and replace the null values with 0 and then started working on it. After Data cleaning performs the Data interpretation step. In Data interpretation found some important information. The data set was almost clean and completed. Replace the Null values with 0 There is a total of 6746 rows and 4 columns are present.

dashboard empowers mission driven organizations to harness the power of data visualization for social change. Women are tracked away from science and mathematics throughout their education, limiting their training and options to go into these fields as adults. The data set contains the data of womengraduated by years, employed persons, Stem Jobs by Education add Salary comparison. This dashboard addresses some major gaps between Men and Women in STEM (Science, Technology, Engineering & Mathematics) fields.

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Q4 import mysql.connector testdb=mysql.connector.connect( database="Alumni", host="localhost", user="root", port="3305", password="login@123" ) cursor=testdb.cursor() cursor.execute("select * from College_B_SJ limit 1000;") for x in cursor: print(x)

Excel sheet link : https://docs.google.com/spreadsheets/d/1xAgZemB6xSyKxcjDz4HTg-T1wiQM81fz/edit?usp=share_link&ouid=102019669998141935744&rtpof=true&sd=true

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Aim- To prepare complete covid-19 analysis via visualisation Introduction-covid-19 is a virus to which it affects directly our respiratory system day by day its inceasing by spreading through ... Problem statement- the covid is spreading throgh one by one it attacks directly respirotory system which leads…

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