import warnings; warnings.filterwarnings("ignore")
# Let us load libraries for data and math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Data setup
# We will use the popular California housing dataset from sklearn
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing(as_frame=True)
df = housing.frame
print("Shape:", df.shape)
df.head()

# Let us see what features we have
print(df.columns.tolist())
# Look at some statistics for each column
df.describe()

# Visualize the target: housing value distribution
plt.figure(figsize=(7,4))
sns.histplot(df['MedHouseVal'], bins=30, kde=True)
plt.xlabel('Median House Value (x $100,000)')
plt.title('Distribution of California House Prices')
plt.show()

# Check for missing data
missing = df.isnull().sum()
print(missing[missing > 0])

# Calculate correlation with house value
corr = df.corr()
print(corr['MedHouseVal'].sort_values(ascending=False))

# Scatter plot: Income vs. House Value
plt.figure(figsize=(7,4))
sns.scatterplot(x='MedInc', y='MedHouseVal', data=df, alpha=0.5)
plt.xlabel('Median Income')
plt.ylabel('Median House Value')
plt.title('Income vs Price')
plt.show()

# Split data into features (X) and target (y)
y = df['MedHouseVal']
X = df.drop('MedHouseVal', axis=1)
# For simplicity, let us use all features for now

# Split into train and test sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print("Training rows:", X_train.shape[0], "| Test rows:", X_test.shape[0])

# Build and train a linear regression model
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
print('Model trained.')

# Predict test values and evaluate the model
from sklearn.metrics import mean_squared_error, r2_score
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Test MSE:", round(mse, 3))
print("Test R^2:", round(r2, 3))

# Plot predicted vs actual house prices
plt.figure(figsize=(6,6))
plt.scatter(y_test, y_pred, alpha=0.4)
plt.xlabel('Actual Price')
plt.ylabel('Predicted Price')
plt.title('Predicted vs. Actual House Prices')
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')
plt.show()

# Show feature importances (model coefficients)
coef = pd.Series(model.coef_, index=X.columns)
coef = coef.sort_values()
plt.figure(figsize=(8, 5))
coef.plot(kind='barh')
plt.title('Feature Impact on House Value')
plt.xlabel('Coefficient weight')
plt.ylabel('Feature')
plt.show()

# Try a Random Forest regressor
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)
y_rf_pred = rf.predict(X_test)
mse_rf = mean_squared_error(y_test, y_rf_pred)
r2_rf = r2_score(y_test, y_rf_pred)
print("Random Forest Test MSE:", round(mse_rf,3))
print("Random Forest R^2:", round(r2_rf,3))

# Let us make a prediction for a new house
user_median_income = input("Enter median income for the area (in tens of thousands): ")
user_age = input("Enter median house age: ")
user_rooms = input("Enter average rooms: ")
user_bedrooms = input("Enter average bedrooms: ")
user_population = input("Enter population: ")
user_occup = input("Enter average occupation: ")
user_lat = input("Enter latitude: ")
user_long = input("Enter longitude: ")
new_house = np.array([[float(user_median_income), float(user_age), float(user_rooms), float(user_bedrooms), float(user_population), float(user_occup), float(user_lat), float(user_long)]])
predicted_val = rf.predict(new_house)[0]
print("Predicted median house value: $", round(predicted_val * 100000, 2))