iris数据集的基本数据分析方法，包括KNN,LG,NB,SVM算法。

Data Science and Analytics Internship at The Sparks Foundation This repository contains all the tasks for the Data Science and Analytics Intern at The Sparks Foundation. TASK-1 Improve our LinkedIn profile. TASK-2 To Explore Supervised Machine Learning In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables. TASK-3 To Explore Unsupervised Machine Learning From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually. TASK-4 To Explore Decision Tree Algorithm For the given ‘Iris’ dataset, create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly. TASK-5 To explore Business Analytics Perform ‘Exploratory Data Analysis’ on the provided dataset ‘SampleSuperstore’. You are the business owner of the retail firm and want to see how your company is performing. You are interested in finding out the weak areas where you can work to make more profit. What all business problems you can derive by looking into the data? You can choose any of the tool of your choice (Python/R/Tableau/PowerBI/Excel)

Exploratory Analysis of dataset Iris with Python, proposed by Data Science Academy.

Data Science in Python - Classification of iris plant through k-Nearest Neighbors analysis. Data set is provided by UCI Machine Learning Repository.

A hands-on project to learn machine learning fundamentals by classifying iris flowers. This project demonstrates data exploration, visualization, model training, and evaluation using Python's popular data science libraries.

This project provides a step-by-step walkthrough of how to load, inspect, and visualize the classic Iris dataset using Python libraries like Pandas, Seaborn, and Matplotlib — ideal for beginners in data science.

Task2 for the Data Science & Business Analytics Internship at THE SPARKS FOUNDATION. Task 1: Predict the optimum number of clusters and represent it visually by using given 'Iris' dataset. Language Used: Python ,IDE Used: Jupyter Notebook

My Projects under The Spark Foundation as a Data Science and Business Analytics Intern Task 1 :- In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables. In this section we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. Task 2 :- Prediction using UnSupervised ML From the given 'Iris' dataset, predict the optimum number of cluster's and represent it visually. Our main objective is to classify the flowers into their respective species - Iris setosa, Iris virginica and Iris versicolor by using various possible plots.