User

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.preprocessing import StandardScaler, OneHotEncoder

from sklearn.compose import ColumnTransformer

from sklearn.mixture import GaussianMixture

from sklearn.cluster import DBSCAN

# Load the dataset

file$\_$path $=$ r$"$C:$\backslash$Users$\backslash$ankit$\backslash$Downloads$\backslash$Ankit$\_$proj$\backslash$Ankit$\_$proj$\backslash$Clustering$\backslash$Air$\_$Traffic$\_$Passenger$\_$Statistics.csv$"$

data $=$ pd$.$read$\_$csv$($file$\_$path$)$

# Define numerical and categorical features

numerical$\_$features $=[\text{'}$Passenger Count', 'Adjusted Passenger Count', 'Year'$]$

categorical$\_$features $=[\text{'}$Published Airline', 'GEO Region'$]$

# Perform one$-$hot encoding for categorical columns

data$\_$encoded $=$ pd$.$get$\_$dummies$($data$,$ columns$=$categorical$\_$features$)$

# Check if there are any missing values after encoding

missing$\_$values $=$ data$\_$encoded.isnull$().$sum$()$

print$("$Missing values after encoding:

$",$ missing$\_$values$)$

# Define preprocessing steps for numerical and categorical features

preprocessor $=$ ColumnTransformer$($

transformers$=[$

$(\text{'}$num$\text{'},$ StandardScaler$(),$ numerical$\_$features$),$

$(\text{'}$cat$\text{'},$ OneHotEncoder$(),$ categorical$\_$features$)$

$],$

remainder$=$'passthrough'

$)$

# Preprocess the data

try:

data$\_$preprocessed $=$ preprocessor.fit$\_$transform$($data$)$

except ValueError as e:

print$("$Error during preprocessing:", e$)$

print$("$Please check if all columns in the dataset are numeric or convertible to numeric."$)$

# If preprocessing is successful, proceed with clustering

if 'data$\_$preprocessed' in locals$()$:

# Perform GMM clustering

gmm $=$ GaussianMixture$($n$\_$components$=4)$

gmm$\_$clusters $=$ gmm$.$fit$\_$predict$($data$\_$preprocessed$)$

# Perform DBSCAN clustering

dbscan $=$ DBSCAN$($eps$=0\mathrm{.}5,$ min$\_$samples$=5)$

dbscan$\_$clusters $=$ dbscan.fit$\_$predict$($data$\_$preprocessed$)$

# Plot the clusters

plt$.$figure$($figsize$=(12,6))$

# GMM clusters

plt$.$subplot$(1,2,1)$

plt$.$scatter$($data$\_$preprocessed$[$:$,0],$ data$\_$preprocessed$[$:$,1],$ c$=$gmm$\_$clusters, cmap$=$'viridis', alpha$=0\mathrm{.}5)$

plt$.$title$(\text{'}$GMM Clustering'$)$

# DBSCAN clusters

plt$.$subplot$(1,2,2)$

plt$.$scatter$($data$\_$preprocessed$[$:$,0],$ data$\_$preprocessed$[$:$,1],$ c$=$dbscan$\_$clusters, cmap$=$'viridis', alpha$=0\mathrm{.}5)$

plt$.$title$(\text{'}$DBSCAN Clustering'$)$

plt$.$tight$\_$layout$()$

plt$.$show$()$

Error during preprocessing: For a sparse output, all columns should be a numeric or convertible to a numeric.

Please check if all columns in the dataset are numeric or convertible to numeric.

my dataset columns value are index int

$6$

$4$

Activity Period int

$6$

$4$

Operating Airline object

Operating Airline IATA Code object

Published Airline object

Published Airline IATA Code object

GEO Summary object

GEO Region object

Activity Type Code object

Price Category Code object

Terminal object

Boarding Area object

Passenger Count int

$6$

$4$

Adjusted Activity Type Code object

Adjusted Passenger Count int

$6$

$4$

Year int

$6$

$4$

Month object

dtype: object

resolve the error, and provide error free code with the output.Kindly refrain my usng chatgpt as it also dontknwo the answer, nly it can be done manualy with the knowledge