What is the predicted flower type?

Consider the perfume dataset in the file, it contains the strength of the smell of 20 perfumes measured by an odor meter throughout 25 seconds, so each second (column) is a new measurement. Write a program to group these perfumes based on the similarity in the strength of their smell. You should use k-means clustering algorithm. The final output of your program should show k lists of perfumes, and show outlier perfume’s records (if exists).

Can I ask for the links of the intext citations, since it is needed for the references of our paper

Coded as advised: import numpy as npimport pandas as pd def get_total_pop_by_income(income_group_name='Low income'):    population_df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/AnalyseProject/world_population.csv', index_col='Country Code')    meta_df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/AnalyseProject/metadata.csv', index_col='Country Code')     # Merge population and metadata dataframes    df = pd.merge(population_df, meta_df[['Income Group']], left_index=True, right_index=True)     # Filter by income group    df = df[df['Income Group'] == income_group_name]     if len(df) == 0:        raise ValueError(f"No data found for income group {income_group_name}")     # Convert year and population columns to numpy arrays     year_col = df.columns.values[range(0, len(df.columns.values), 2)]    population_col = df.columns.values[range(1, len(df.columns.values), 2)]    year_array = np.array(year_col, dtype=np.int64)…

True or False 'Dataset' work well with Pyspark.

Consider the following BST: 5 root 10 15 34 2 25 31 39 59 86 17 19 32 Using the efficient search method described in the recorded video, how many times do we compare a value in the BST with the target value 40 when we call contains(40) (or find(40))? Do not count any comparisons for null. Note: there the size of the BST is 18. A/ 18 30 20 42 72

Create two pairs of sets using both the hash table implementation and thebit array implementation. Both implementations should use the same sets.Using the Timing class, compare the major operations (union, intersection, difference, isSubset) of each implementation and report the actualdifference in times.

In a 384-well plate luciferase assay, we drugged 4 receptors (A, B, C, D) with their endogenous agonists at a range of doses. Below is a subset of the resulting data frame (please use the full data frame attached to the original email as a CSV file). Each row of this data frame corresponds to a single well of the 384 well plate. The columns are as follows: receptor: the receptor tested in the well agonist: the endogenous agonist used for a receptor agonist_nM: the dose of the endogenous compound (in nanomolar) ● RLU: raw luminescence of the well (our measure of receptor activity) To answer this 3 part question, we’ll ask you to submit a PDF file containing all of your source code, plots, and written interpretation. (Using something like a Jupyter Notebook with Python or Rmarkdown is recommended, but not required.) You should provide brief explanations of your thought process at each step. Part A: Different receptors have different basal rates of activity. To better facilitate…

I've wrote a script to find the top three features for a random forest, but it is not work.please assist to fix the below code. #Import scikit-learn dataset libraryfrom sklearn import datasets#Load datasetiris = datasets.load_iris()# Creating a DataFrame of given iris dataset.import pandas as pdimport numpy as npdata=pd.DataFrame({'sepal length':iris.data[:,0],'sepal width':iris.data[:,1],'petal length':iris.data[:,2],'petal width':iris.data[:,3],'species':iris.target})iris['target_names']print(data.head())# Import train_test_split functionfrom sklearn.model_selection import train_test_splitX=data[['sepal length', 'sepal width', 'petal length', 'petal width']] # Featuresy=data['species'] # Labels# Split dataset into training set and test setX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)#Import Random Forest Modelfrom sklearn.ensemble import RandomForestClassifier#Create a Gaussian Classifierrf=RandomForestClassifier(n_estimators=100)#Train the model using the…

How is the indexing and slicing used inside the for loop to produce the Scatterplot with three classifications shown below.