Please follow the rubric. Its done by jupyter notebook. I need a datasets too.Please cover all points. 1) This project is individual 2) The project will be presented in front of the class on...

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Please follow the rubric. Its done by jupyter notebook. I need a datasets too.Please cover all points.

1) This project is individual


2) The project will be presented in front of the class on __________________


3) The presentation can be in Jupyter notebook format. Your Jupyter notebook should be commented enough to understand the steps you have taken and descriptive enough to understand your results


4) You should aim to keep your presentations within
7 to 10
minutes to allow everyone time to present. Although your Jupyter notebook will include all the details, your presentation should focus on highlights to keep your presentation within the time limit


5) The presentation should include:


a. Description of the problem or question you are studying or trying to solve.


b. Short description of the dataset you have selected.


c. State the methods you are using for dimensional reduction and clustering


d. Describe your development, testing and evaluation of dimensional reduction and clustering



i. You will want to see whether dimensional reduction provided any benefit to your analysis



ii. You will also want to compare different and evaluate the performance of different clustering methods


e. Summarize Results and Provide Conclusions


f. If there were any problems that you came across, please describe those as well as well as how you tried to resolve them. If something did not work as you would have liked, please describe that as well.


g. Further study ideas or plans, if any

Answered Same DayMay 13, 2021

Answer To: Please follow the rubric. Its done by jupyter notebook. I need a datasets too.Please cover all...

Ishvina answered on May 14 2021
170 Votes
DIMENSIONALITY REDUCTION AND CLUSTERING PROJECT
DIMENSIONAL REDUCTION AND CLUSTERING PROJECT
SUBMITTED BY:
PROBLEM STATEMENT
To Create
the model that can classify the different species of the Iris flower.
PROBLEM SOLVING APPROACH
Load/Import the dataset.
Build the model
Train the model
Make predictions.
Here we are using the famous Iris Dataset through which we classify the different species of the iris flower . The steps we follow are first importing or loading our dataset using sckit learn or reading the csv file from the url stated
2
THE IRIS DATASET
The Iris Dataset was introduced by the
British statistician and biologist Ronald     
    Fisher.
The data set consists of:
 150 samples , 50 samples of each specie.
 3 labels: species of Iris : Iris setosa , Iris         
    virginica and Iris versicolor
 4 features: Sepal length in cm
                Sepal width in cm
                Petal length in cm
                Petal Width in cm
 
This is the Iris dataset which was introduced by a biologist Ronald Fisher , The dataset has 150 samples - 50 samples belonging to each species . We have three species namely Iris setosa – marked as 0 in our target array , Iris Versicolor marked as 1 and iris verginica marked as 2. A single sample of Iris flower has four features namely Sepal length , sepal width , petal length and petal width.
3
DIMENSIONALITY REDUCTION - PCA
It is the...
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