import pandas as pd

import numpy as np

learning_rate = 0.01

fit_intercept = True

weights = 0

def fit(X, y):

global weights

if fit_intercept:

X = np.concatenate((np.ones((X.shape[0], 1)), X), axis=1)

weights = np.zeros(X.shape[1])

for i in range(1000):

# gradient descent

current_prediction = np.dot(X, weights)

gradient = np.dot(X.T, (current_prediction - y)) / y.size

weights -= learning_rate * gradient

def predict_prob(X):

global weights

if fit_intercept:

X = np.concatenate((np.ones((X.shape[0], 1)), X), axis=1)

return np.dot(X, weights)

###

from sklearn.datasets import load_boston

boston_data = load_boston()

X = boston_data.data

y = boston_data.target

X_df = pd.DataFrame(X, columns = boston_data.feature_names)

print(boston_data.DESCR)

###

import matplotlib.pyplot as plt

import seaborn as sns

sns.distplot(y)

###

from sklearn.metrics import mean_squared_error

y_pred = [0] * 506

mean_squared_error(y, y_pred)

sns.jointplot(X[:, 5], y)

def manual_model(house):

return (house[5] - 4) * 10

y_pred = [manual_model(x) for x in X]

mean_squared_error(y, y_pred)

sns.jointplot(X[:, 5], y)

###

from sklearn.linear_model import LinearRegression

model = LinearRegression()

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 42)

model.fit(X_train, y_train)

y_pred_ml = model.predict(X_test)

mean_squared_error(y_test, y_pred_ml)

###

model_2 = LinearRegression(normalize = True)

model_2.fit(X_train, y_train)

y_pred_ml2 = model.predict(X_test)

mean_squared_error(y_test, y_pred_ml2)