import pandas as pd
from sklearn.datasets import fetch_california_housing
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error, r2_score
import seaborn as sns
import matplotlib.pyplot as plt

data = fetch_california_housing()
X = pd.DataFrame(data.data, columns=data.feature_names)
y= data.target

X.columns

y

print(X.describe())
print(X.head())

print("Missing values:", X.isnull().sum())

# Visualize  EDA
sns.distplot(y)
plt.xlabel("Median House Value")
plt.ylabel("Density")
plt.title("Distribution of median house values")
plt.show()

# data preprocessing
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

X_train,X_test,y_train,y_test=train_test_split(X_scaled,y,test_size=0.2,random_state=42)

svr = SVR(kernel='rbf')
svr.fit(X_train, y_train)

y_pred = svr.predict(X_test)




mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("Meam Squared Error:", mse)
print("R-squared:", r2)




from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVR

# Define a parameter grid to explore
param_grid = {
    'kernel': ['linear', 'rbf'],  # Experiment with different kernels
    'C': [0.01, 0.1, 1, 10, 100],  # Regularization parameter
    'gamma': [0.001, 0.01, 0.1, 1],  # Gamma for RBF kernel (optional)
}

# Create the GridSearchCV object
grid_search = GridSearchCV(SVR(), param_grid, cv=5)  # 5-fold cross-validation

# Fit the grid search to the training data
grid_search.fit(X_train, y_train)

# Get the best model and its parameters
best_model = grid_search.best_estimator_
best_params = grid_search.best_params_

# Use the best model for prediction and evaluation
y_pred = best_model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("Mean Squared Error:", mse)
print("R-squared:", r2)
print("Best Hyperparameters:", best_params)