Python programming in Boston and Iris data set

1. Use the Boston data set available in sklearn package. Perform exploratory data analysis and construct a linear regression to predict the house price. You can use the following Python code to load the dataset.

import pandas as pd # conventional alias

from [login to view URL] import load_boston

dataset = load_boston()

df = [login to view URL]([login to view URL], columns=[login to view URL]

[login to view URL] to the following link of Iris data set.

[login to view URL] where you can download the data set. You goal is to set-up and train a classifier. You are required to show the steps in preparing the data set, perform exploratory data analysis, identify the classifier suitable for this task. Read about confusion matrix and present the confusion matrix for your trained algorithm.

[login to view URL] Sigmoid function / Softmax function is used in classification?Now observe the following function in Figure Q1 and state if it can be used in place of the sigmoid function for binary classification.

Please see the figure in the uploaded doc

Aptitudini: Python, Machine Learning (ML), Arhitectură software, Statistică, Exploatarea Datelor

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Hey, I also worked on those two datasets when I was a beginner. I can do it. We can discuss the details over chat, feel free to message me and ask any question. Let me talk about my experience now. I have been work Mai multe

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Hi! I am an expert in Computer Vision and Machine learning python stack, sickit-learn, numpy,pandas,matplotlib, OpenCv and tensorflow. I did my bachelors with majors in Computer Vision and Deep Learning. I am dedicated Mai multe

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I have experience in implementing ML and DL models using tenserflow, keras and scikit-learn. I have completed many projects. Some of these are as: 1. Human Activity Recognition (HAR) :- I have tried three models Suppo Mai multe

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I am a Python Data Analytics Professional and trainer , I've gone through the Boston data a couple of time. You can proceed with further details in the chat box

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