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

Load a Pandas dataframe with a selected dataset. Identify and count the missing values in a dataframe. Clean the data after removing noise as follows: a. Drop duplicate rows. b. Detect the outliers and remove the rows having outliers c. Identify the most correlated positively correlated attributes and negatively correlated attributes

  Load a Pandas dataframe with a selected dataset. Identify and count the missing values in a dataframe. Clean the data after removing noise as follows: a. Drop duplicate rows. b. Detect the outliers and remove the rows having outliers c. Identify the most correlated positively correlated attributes and negatively correlated attributes CODE import pandas as pd # Load the dataset df = pd.read_csv('your_dataset.csv') # Identify and count missing values missing_values = df.isnull().sum() print("Missing values:") print(missing_values) # Drop duplicate rows df = df.drop_duplicates() # Detect outliers and remove rows with outliers def remove_outliers(df, column):     Q1 = df[column].quantile(0.25)     Q3 = df[column].quantile(0.75)     IQR = Q3 - Q1     lower_bound = Q1 - 1.5 * IQR     upper_bound = Q3 + 1.5 * IQR     return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)] columns_to_check = ['column1', 'c...
Read more

The weights of 8 boys in kilograms: 45, 39, 53, 45, 43, 48, 50, 45. Find the median

  The weights of 8 boys in kilograms: 45, 39, 53, 45, 43, 48, 50, 45. Find the median To find the median, we need to arrange the weights in ascending order. Here are the weights of the 8 boys in kilograms: 39, 43, 45, 45, 45, 48, 50, 53 To find the median, we look for the middle value. Since there are 8 weights, the median is the average of the two middle values. In this case, the two middle values are 45 and 45. Therefore, the median weight is: (45 + 45) / 2 = 90 / 2 = 45 So, the median weight of the 8 boys is 45 kilograms. OR Here's how you can find the median of the given weights using Python: CODE:- weights = [45, 39, 53, 45, 43, 48, 50, 45] sorted_weights = sorted(weights) length = len(sorted_weights) if length % 2 == 0:     median = (sorted_weights[length // 2 - 1] + sorted_weights[length // 2]) / 2 else:     median = sorted_weights[length // 2] print("The median weight is:", median) When you run this code, it will output: The median weight is: 45.0 So, th...
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