Machine Learning lab

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import fetch_california_housing

california_housing = fetch_california_housing()
X = california_housing.data
y = california_housing.target

df = pd.DataFrame(X, columns=california_housing.feature_names)
df["Target"] = y 

print("\n📊 Statistical Summary of the Dataset:")
print(df.describe())

df.hist(bins=30, figsize=(15, 10), color='skyblue', edgecolor='black')
plt.suptitle('Histograms of Numerical Features', fontsize=16)
plt.tight_layout()
plt.show()

plt.figure(figsize=(15, 8))
sns.boxplot(data=df, orient="h", palette="Set2")
plt.title('Box Plots for Numerical Features (Detecting Outliers)', fontsize=16)
plt.tight_layout()
plt.show()

def find_outliers_iqr(data):
    outlier_dict = {}  
    for column in data.columns:
        Q1 = data[column].quantile(0.25)  
        Q3 = data[column].quantile(0.75) 
        IQR = Q3 - Q1  
        lower_bound = Q1 - 1.5 * IQR  
        upper_bound = Q3 + 1.5 * IQR  

        outliers = data[(data[column] < lower_bound) | (data[column] > upper_bound)][column]
        if not outliers.empty:
            outlier_dict[column] = outliers.values  
    return outlier_dict

outliers = find_outliers_iqr(df)