ML

#LAB 1
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import fetch_california_housing

data=fetch_california_housing(as_frame=True)
df=data.frame
print("Dataset Sample:")
print(df.head())

def plot_histograms(df):
    df.hist(figsize=(12,10), bins=30,color='skyblue', edgecolor='black')
    plt.suptitle('Histograms of Numerical Features:',fontsize=16)
    plt.tight_layout(rect=(0,0,1,0.97))
    plt.show()

def plot_boxplots(df):
    plt.figure(figsize=(14,10))
    for i,column in enumerate(df.columns,1):
        plt.subplot(3,3,i)
        sns.boxplot(y=df[column],color='skyblue')
        plt.title(f'Box Plot of(column)',fontsize=12)
        plt.tight_layout()
    plt.show()

def analyze_features(df):
    print("\nFeature Analysis:")
    for column in df.columns:
        print(f'\nFeature:(column)')
        print(f"Mean: {df[column].mean():2f},Median: {df[column].median():2f}, Std Dev: {df[column].std():2f}")
        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
        outliers=df[(df[column]<lower_bound) | (df[column]>upper_bound)]
        print(f'Number of Outliers: {len(outliers)}')

plot_histograms(df)
plot_boxplots(df)
analyze_features(df)

#LAB 2
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing

data=fetch_california_housing(as_frame=True)
df=data.frame
print("Data Sample")
print(df.head())
correlation_matrix=df.corr()
print("Correlational Matrix:")
print(correlation_matrix)

def plot_heatmap(corr_matrix):
    plt.figure(figsize=(10,8))
    sns.heatmap(corr_matrix,annot=True,fmt=".2f",cmap="cool",cbar=True,square=True,linewidths=0.5)
    plt.title("correlation matrix heatmap", fontsize=16)
    plt.show()

def plot_pairplot(df):
    sns.pairplot(df,diag_kind="kde",corner=True,plot_kws={'alpha':0.5},diag_kws={'fill':True})
    plt.suptitle("Pair Plot of Numerical Features",y=1.02,fontsize=16)
    plt.show()

plot_heatmap(correlation_matrix)
plot_pairplot(df)

#LAB 3
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

iris = load_iris()
X = iris.data
y = iris.target
target_names = iris.target_names


scaler = StandardScaler()
X_standardized = scaler.fit_transform(X)


pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_standardized)
print(X_pca)


pca_df = pd.DataFrame(data=X_pca, columns=['Principal Component 1', 'Principal Component 2'])
pca_df['Target'] = y


plt.figure(figsize=(8, 6))
colors = ['r', 'g', 'b']

for target, color, label in zip(range(len(target_names)), colors, target_names):
    plt.scatter(
        pca_df.loc[pca_df['Target'] == target, 'Principal Component 1'],
        pca_df.loc[pca_df['Target'] == target, 'Principal Component 2'],
        color=color,
        alpha=0.6,
        label=label
    )

plt.title('PCA of Iris Dataset (2 components)', fontsize=16)
plt.xlabel('Principal Component 1', fontsize=12)
plt.ylabel('Principal Component 2', fontsize=12)
plt.legend(title='Target', loc='best')
plt.grid(alpha=0.3)
plt.show()

#LAB 5
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
np.random.seed(42)
x = np.random.rand(100)
labels = np.where(x[:50]<=0.5,1,2)
x_train =x[:50].reshape(-1,1)
y_train = labels
x_test = x[:50].reshape(-1,1)
k_values=[1,2,3,4,5,20,30]
for k in k_values:
    knn =KNeighborsClassifier(n_neighbors=k)
    knn.fit(x_train,y_train)
    y_pred =knn.predict(x_test)
    print(f"\nk={k}:")
    print(f"Predicted classes :{y_pred}")