Download any dataset and do the following: a. Count number of categorical and numeric features b. Remove one correlated attribute (if any) c. Display five-number summary of each attribute and show it visually

Download any dataset and do the following:

a. Count number of categorical and numeric features

b. Remove one correlated attribute (if any)

c. Display five-number summary of each attribute and show it visually


CODE





 import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt


# Load the Iris dataset into a pandas DataFrame

iris_df = pd.read_csv('iris.data', header=None,

                      names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class'])


# Count the number of categorical and numeric features

categorical_features = iris_df.select_dtypes(include=['object']).columns

numeric_features = iris_df.select_dtypes(include=['float64']).columns


print(f"Number of categorical features: {len(categorical_features)}")

print(f"Number of numeric features: {len(numeric_features)}")


# Calculate the correlation matrix

correlation_matrix = iris_df[numeric_features].corr()


# Find the most highly correlated attribute

most_correlated_attribute = correlation_matrix.abs().sum().idxmax()


# Remove the most highly correlated attribute

iris_df = iris_df.drop(columns=most_correlated_attribute)


# Display the five-number summary of each attribute and show it visually

plt.figure(figsize=(10, 6))

sns.boxplot(data=iris_df, orient='h')

plt.title("Five-Number Summary of Iris Dataset")

plt.xlabel("Value")

plt.show()


# Display the summary statistics of each attribute

summary_statistics = iris_df.describe()

print(summary_statistics)


Comments

Post a Comment

Popular posts from this blog

Using Titanic dataset, do the following: a. Find total number of passengers with age less than 30 b. Find total fare paid by passengers of first class c. Compare number of survivors of each passenger class

  Using Titanic dataset, do the following: a. Find total number of passengers with age less than 30 b. Find total fare paid by passengers of first class c. Compare number of survivors of each passenger class CODE import seaborn as sns # Load the Titanic dataset from seaborn titanic = sns.load_dataset('titanic') # Find the total number of passengers with age less than 30 passengers_age_less_than_30 = titanic[titanic['age'] < 30] total_passengers_age_less_than_30 = len(passengers_age_less_than_30) print(f"Total number of passengers with age less than 30: {total_passengers_age_less_than_30}") # Find the total fare paid by passengers of first class total_fare_first_class = titanic[titanic['class'] == 'First']['fare'].sum() print(f"Total fare paid by passengers of first class: {total_fare_first_class}") # Compare the number of survivors of each passenger class survivors_by_class = titanic.groupby('class')['survived...
Read more

Differentiate between Unimodal, Bimodal and Multimodal with graphic representation.

Image
Differentiate between Unimodal, Bimodal and Multimodal with graphic representation.  Unimodal, bimodal, and multimodal are terms used to describe the shape and characteristics of a distribution. They indicate the number of peaks or modes present in the data. Here's a differentiation between unimodal, bimodal, and multimodal distributions along with graphic representations: Unimodal Distribution: A unimodal distribution has a single peak or mode, indicating that there is one dominant value or range of values in the dataset. It is the most common type of distribution. Graphic Representation of Unimodal Distribution: In a histogram or density plot, a unimodal distribution appears as a single prominent peak. Example of Unimodal Distribution: In a dataset representing the heights of individuals, a unimodal distribution would occur if most individuals have a similar height, resulting in a single peak at the most common height. Bimodal Distribution: A bimodal distribution has two distinct...
Read more

Import iris data using sklearn library . Compute mean, mode, median, standard deviation, confidence interval and standard error for each feature ii. Compute correlation coefficients between each pair of features and plot heatmap iii. Find covariance between length of sepal and petal iv. Build contingency table for class feature

Import iris data using sklearn library or (Download IRIS data from: (https://archive.ics.uci.edu/ml/datasets/iris or import it from sklearn.datasets) i. Compute mean, mode, median, standard deviation, confidence interval and standard error for each feature ii. Compute correlation coefficients between each pair of features and plot heatmap iii. Find covariance between length of sepal and petal iv. Build contingency table for class feature CODE  import numpy as np import pandas as pd from sklearn.datasets import load_iris from scipy import stats import seaborn as sns import matplotlib.pyplot as plt # Load the Iris dataset iris = load_iris() data = iris.data feature_names = iris.feature_names target = iris.target target_names = iris.target_names # Convert data to a pandas DataFrame for easier analysis df = pd.DataFrame(data, columns=feature_names) df['class'] = target_names[target] # Compute mean, mode, median, standard deviation, confidence interval, and standard error for each f...
Read more