2022-06-12 17:45:18 +02:00
|
|
|
|
{
|
|
|
|
|
|
"cells": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"slideshow": {
|
|
|
|
|
|
"slide_type": "slide"
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Regresja wielomianowa"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 137,
|
2022-06-12 17:45:18 +02:00
|
|
|
|
"metadata": {
|
|
|
|
|
|
"slideshow": {
|
|
|
|
|
|
"slide_type": "notes"
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"import ipywidgets as widgets\n",
|
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
|
|
|
|
|
|
"import numpy as np\n",
|
|
|
|
|
|
"import pandas\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"%matplotlib inline"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 138,
|
2022-06-12 17:45:18 +02:00
|
|
|
|
"metadata": {
|
|
|
|
|
|
"slideshow": {
|
|
|
|
|
|
"slide_type": "notes"
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Przydatne funkcje\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"def cost(theta, X, y):\n",
|
|
|
|
|
|
" \"\"\"Wersja macierzowa funkcji kosztu\"\"\"\n",
|
|
|
|
|
|
" m = len(y)\n",
|
|
|
|
|
|
" J = 1.0 / (2.0 * m) * ((X * theta - y).T * (X * theta - y))\n",
|
|
|
|
|
|
" return J.item()\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"def gradient(theta, X, y):\n",
|
|
|
|
|
|
" \"\"\"Wersja macierzowa gradientu funkcji kosztu\"\"\"\n",
|
|
|
|
|
|
" return 1.0 / len(y) * (X.T * (X * theta - y)) \n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"def gradient_descent(fJ, fdJ, theta, X, y, alpha=0.1, eps=10**-7):\n",
|
|
|
|
|
|
" \"\"\"Algorytm gradientu prostego (wersja macierzowa)\"\"\"\n",
|
|
|
|
|
|
" current_cost = fJ(theta, X, y)\n",
|
|
|
|
|
|
" logs = [[current_cost, theta]]\n",
|
|
|
|
|
|
" while True:\n",
|
|
|
|
|
|
" theta = theta - alpha * fdJ(theta, X, y)\n",
|
|
|
|
|
|
" current_cost, prev_cost = fJ(theta, X, y), current_cost\n",
|
|
|
|
|
|
" if abs(prev_cost - current_cost) > 10**15:\n",
|
|
|
|
|
|
" print('Algorithm does not converge!')\n",
|
|
|
|
|
|
" break\n",
|
|
|
|
|
|
" if abs(prev_cost - current_cost) <= eps:\n",
|
|
|
|
|
|
" break\n",
|
|
|
|
|
|
" logs.append([current_cost, theta]) \n",
|
|
|
|
|
|
" return theta, logs\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"def plot_data(X, y, xlabel, ylabel):\n",
|
|
|
|
|
|
" \"\"\"Wykres danych (wersja macierzowa)\"\"\"\n",
|
|
|
|
|
|
" fig = plt.figure(figsize=(16*.6, 9*.6))\n",
|
|
|
|
|
|
" ax = fig.add_subplot(111)\n",
|
|
|
|
|
|
" fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
|
|
|
|
|
|
" ax.scatter([X[:, 1]], [y], c='r', s=50, label='Dane')\n",
|
|
|
|
|
|
" \n",
|
|
|
|
|
|
" ax.set_xlabel(xlabel)\n",
|
|
|
|
|
|
" ax.set_ylabel(ylabel)\n",
|
|
|
|
|
|
" ax.margins(.05, .05)\n",
|
|
|
|
|
|
" plt.ylim(y.min() - 1, y.max() + 1)\n",
|
|
|
|
|
|
" plt.xlim(np.min(X[:, 1]) - 1, np.max(X[:, 1]) + 1)\n",
|
|
|
|
|
|
" return fig\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"def plot_fun(fig, fun, X):\n",
|
|
|
|
|
|
" \"\"\"Wykres funkcji `fun`\"\"\"\n",
|
|
|
|
|
|
" ax = fig.axes[0]\n",
|
|
|
|
|
|
" x0 = np.min(X[:, 1]) - 1.0\n",
|
|
|
|
|
|
" x1 = np.max(X[:, 1]) + 1.0\n",
|
|
|
|
|
|
" Arg = np.arange(x0, x1, 0.1)\n",
|
|
|
|
|
|
" Val = fun(Arg)\n",
|
|
|
|
|
|
" return ax.plot(Arg, Val, linewidth='2')"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 139,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
2022-06-12 17:45:18 +02:00
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"def MSE(Y_true, Y_pred):\n",
|
|
|
|
|
|
" return np.square(np.subtract(Y_true,Y_pred)).mean()"
|
2022-06-12 17:45:18 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 140,
|
2022-06-12 17:45:18 +02:00
|
|
|
|
"metadata": {
|
|
|
|
|
|
"slideshow": {
|
|
|
|
|
|
"slide_type": "fragment"
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Funkcja regresji wielomianowej\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"def h_poly(Theta, x):\n",
|
|
|
|
|
|
" \"\"\"Funkcja wielomianowa\"\"\"\n",
|
|
|
|
|
|
" return sum(theta * np.power(x, i) for i, theta in enumerate(Theta.tolist()))\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"def get_poly_data(data, deg):\n",
|
|
|
|
|
|
" m, n_plus_1 = data.shape\n",
|
|
|
|
|
|
" n = n_plus_1 - 1\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" X1 = data[:, 0:n]\n",
|
|
|
|
|
|
" X1 /= np.amax(X1, axis=0)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" Xs = [np.ones((m, 1)), X1]\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" for i in range(2, deg+1):\n",
|
|
|
|
|
|
" Xn = np.power(X1, i)\n",
|
|
|
|
|
|
" Xn /= np.amax(Xn, axis=0)\n",
|
|
|
|
|
|
" Xs.append(Xn)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" X = np.matrix(np.concatenate(Xs, axis=1)).reshape(m, deg * n + 1)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" y = np.matrix(data[:, -1]).reshape(m, 1)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" return X, y\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"\n",
|
2022-06-15 20:06:32 +02:00
|
|
|
|
"def polynomial_regression(X, y, n):\n",
|
2022-06-12 17:45:18 +02:00
|
|
|
|
" \"\"\"Funkcja regresji wielomianowej\"\"\"\n",
|
|
|
|
|
|
" theta_start = np.matrix([0] * (n+1)).reshape(n+1, 1)\n",
|
|
|
|
|
|
" theta, logs = gradient_descent(cost, gradient, theta_start, X, y)\n",
|
|
|
|
|
|
" return lambda x: h_poly(theta, x)"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 141,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"def predict_values(model, data, n):\n",
|
|
|
|
|
|
" x, y = get_poly_data(np.array(data), n)\n",
|
|
|
|
|
|
" preprocessed_x = []\n",
|
|
|
|
|
|
" for i in x:\n",
|
|
|
|
|
|
" preprocessed_x.append(i.item(1))\n",
|
|
|
|
|
|
" return y, model(preprocessed_x), MSE(y, model(preprocessed_x))\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"def plot_and_mse(data, data_test, n):\n",
|
|
|
|
|
|
" x, y = get_poly_data(np.array(data), n)\n",
|
|
|
|
|
|
" model = polynomial_regression(x, y, n)\n",
|
|
|
|
|
|
" \n",
|
|
|
|
|
|
" fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
|
|
|
|
|
|
" plot_fun(fig, polynomial_regression(x, y, n), x)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" y_true, Y_pred, mse = predict_values(model, data_test, n)\n",
|
|
|
|
|
|
" print(f'Wielomian {n} stopnia, MSE = {mse}')"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 152,
|
2022-06-12 17:45:18 +02:00
|
|
|
|
"metadata": {
|
|
|
|
|
|
"slideshow": {
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"slide_type": "notes"
|
2022-06-12 17:45:18 +02:00
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/html": [
|
|
|
|
|
|
"<div>\n",
|
|
|
|
|
|
"<style scoped>\n",
|
|
|
|
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
|
|
|
|
" vertical-align: middle;\n",
|
|
|
|
|
|
" }\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" .dataframe tbody tr th {\n",
|
|
|
|
|
|
" vertical-align: top;\n",
|
|
|
|
|
|
" }\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" .dataframe thead th {\n",
|
|
|
|
|
|
" text-align: right;\n",
|
|
|
|
|
|
" }\n",
|
|
|
|
|
|
"</style>\n",
|
|
|
|
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
|
|
|
|
" <thead>\n",
|
|
|
|
|
|
" <tr style=\"text-align: right;\">\n",
|
|
|
|
|
|
" <th></th>\n",
|
|
|
|
|
|
" <th>sqrMetres</th>\n",
|
|
|
|
|
|
" <th>price</th>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </thead>\n",
|
|
|
|
|
|
" <tbody>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>49</th>\n",
|
|
|
|
|
|
" <td>37</td>\n",
|
|
|
|
|
|
" <td>338000.00</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>1171</th>\n",
|
|
|
|
|
|
" <td>90</td>\n",
|
|
|
|
|
|
" <td>855000.00</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>368</th>\n",
|
|
|
|
|
|
" <td>16</td>\n",
|
|
|
|
|
|
" <td>399000.00</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>1206</th>\n",
|
|
|
|
|
|
" <td>58</td>\n",
|
|
|
|
|
|
" <td>359602.00</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>1500</th>\n",
|
|
|
|
|
|
" <td>20</td>\n",
|
|
|
|
|
|
" <td>424977.14</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>...</th>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>50</th>\n",
|
|
|
|
|
|
" <td>78</td>\n",
|
|
|
|
|
|
" <td>420000.00</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>396</th>\n",
|
|
|
|
|
|
" <td>52</td>\n",
|
|
|
|
|
|
" <td>275000.00</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>1367</th>\n",
|
|
|
|
|
|
" <td>55</td>\n",
|
|
|
|
|
|
" <td>192750.00</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>771</th>\n",
|
|
|
|
|
|
" <td>62</td>\n",
|
|
|
|
|
|
" <td>558745.00</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>337</th>\n",
|
|
|
|
|
|
" <td>55</td>\n",
|
|
|
|
|
|
" <td>246330.00</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
|
|
|
|
|
"<p>1674 rows × 2 columns</p>\n",
|
|
|
|
|
|
"</div>"
|
|
|
|
|
|
],
|
2022-06-12 17:45:18 +02:00
|
|
|
|
"text/plain": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" sqrMetres price\n",
|
|
|
|
|
|
"49 37 338000.00\n",
|
|
|
|
|
|
"1171 90 855000.00\n",
|
|
|
|
|
|
"368 16 399000.00\n",
|
|
|
|
|
|
"1206 58 359602.00\n",
|
|
|
|
|
|
"1500 20 424977.14\n",
|
|
|
|
|
|
"... ... ...\n",
|
|
|
|
|
|
"50 78 420000.00\n",
|
|
|
|
|
|
"396 52 275000.00\n",
|
|
|
|
|
|
"1367 55 192750.00\n",
|
|
|
|
|
|
"771 62 558745.00\n",
|
|
|
|
|
|
"337 55 246330.00\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"\n",
|
|
|
|
|
|
"[1674 rows x 2 columns]"
|
2022-06-12 17:45:18 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 152,
|
2022-06-12 17:45:18 +02:00
|
|
|
|
"metadata": {},
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Wczytanie danych (mieszkania) przy pomocy biblioteki pandas\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"alldata = pandas.read_csv('data_flats.tsv', header=0, sep='\\t',\n",
|
|
|
|
|
|
" usecols=['price', 'rooms', 'sqrMetres'])\n",
|
|
|
|
|
|
"alldata = alldata[['sqrMetres', 'price']]\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"alldata = alldata.sample(frac=1)\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"alldata"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 153,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# alldata = np.matrix(alldata[['sqrMetres', 'price']])\n",
|
|
|
|
|
|
"data_train = alldata[0:1600]\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"data_test = alldata[1600:]\n",
|
|
|
|
|
|
"data_train = np.matrix(data_train).astype(float)\n",
|
|
|
|
|
|
"data_test = np.matrix(data_test).astype(float)"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 144,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"Wielomian 1 stopnia, MSE = 41910519165.43458\n",
|
|
|
|
|
|
"Wielomian 2 stopnia, MSE = 60658890503.01548\n",
|
|
|
|
|
|
"Wielomian 3 stopnia, MSE = 63228721451.021095\n"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFvCAYAAADkPtfiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAA/wElEQVR4nO3deXSW9Z3//9fnzkoWdhBIALVEkKVgYKy2aBWqldQ1sUO/7el26Nhva/ud1pk5XX490575TqvTOafTmVHHcZge63ynlg43KK1Yq4C1WDcSUMJmEK3cCfuaBBKyfH5/fO4shntN7vu+7uX5OCcn4bquXPcnVwJ58VneH2OtFQAAALzj87oBAAAAuY5ABgAA4DECGQAAgMcIZAAAAB4jkAEAAHiMQAYAAOCxjAxkxpifGWOOGmMaY7z+z40xu40xu4wxv0h2+wAAAOJhMrEOmTHmekltkh631s6Pcm2VpF9JWmatPWWMmWytPZqKdgIAAMQiI3vIrLUvSjo5+Jgx5gPGmN8aY+qNMX8wxswJnvoLSQ9Za08FP5cwBgAA0kpGBrIwHpX0dWvtYkl/Lenh4PErJF1hjHnJGPOKMeYWz1oIAAAQQr7XDUgEY0yZpA9L+h9jTN/houD7fElVkm6QVCnpRWPMAmvt6RQ3EwAAIKSsCGRyPX2nrbWLQpwLSHrVWtsl6R1jzFtyAe31FLYPAAAgrKwYsrTWnpULW5+UJOMsDJ5+Uq53TMaYiXJDmAc8aCYAAEBIGRnIjDFPSHpZ0mxjTMAYs0rSZyStMsa8IWmXpDuClz8r6YQxZrekLZL+xlp7wot2AwAAhJKRZS8AAACySUb2kAEAAGQTAhkAAIDHMm6V5cSJE+2ll17qdTMAeC0QkI4cCX9+yhSpoiJ17QGQ8+rr649baycN53MzLpBdeuml2rZtm9fNAOC11aulb3xDam+/+FxpqfT3fy+tWpXyZgHIXcaYPw33cxmyBJCZVq6UfGH+CfP53HkAyBAEMgCZqbxc2rjRvS8tdcdKSweOl5V52z4AiEPGDVkCQL+lS6WWFmnNGmn/fmnWLNczRhgDkGEIZAAyW1kZc8UAZDyGLAEAADxGIAMAAPAYgQwAAMBjBDIAAACPEcgAAAA8RiADAADwGIEMAADAYwQyAAAAjxHIAAAAPEYgAwAA8BiBDAAAwGMEMgAAAI8RyAAAADxGIAMAAPBY0gKZMWa6MWaLMWa3MWaXMeYvQ1xzgzHmjDFmR/Dtb5PVHgAAgHSVn8R7d0v6K2ttgzGmXFK9MeY5a+3uIdf9wVp7axLbAQAAkNaS1kNmrT1krW0IftwqaY+kimS9HgAAQKZKyRwyY8ylkq6S9GqI09caY94wxjxjjJmXivYAAACkk2QOWUqSjDFlkvySvmGtPTvkdIOkmdbaNmNMjaQnJVWFuMc9ku6RpBkzZiS3wQAAACmW1B4yY0yBXBj7b2vtuqHnrbVnrbVtwY83SiowxkwMcd2j1tol1tolkyZNSmaTAQAAUi6ZqyyNpP+UtMda+5Mw10wJXidjzNXB9pxIVpsAAADSUTKHLD8i6bOSdhpjdgSPfVfSDEmy1j4i6W5JXzHGdEs6L+lT1lqbxDYBAACknaQFMmvtVkkmyjUPSnowWW0AAADIBFTqBwAA8BiBDAAAwGMEMgAAAI8RyAAAADxGIAMAAPAYgQwAAMBjBDIAAACPEcgAAAA8RiADAADwGIEMAADAYwQyAAAAjxHIAAAAPEYgAwAA8BiBDAAAwGMEMgAAAI8RyAAAADxGIAMAAPAYgQwAAMBjBDIAAACPEcgAAAA8RiADAADwGIEMAADAYwQyAAAAjxHIAAAAPEYgAwAA8BiBDAAAwGMEMgAAAI8RyAAAADxGIAMAAPAYgQwAAMBjBDIAAACPEcgAAAA8RiADAADwGIEMAADAYwQyAAAAjxHIAAAAPEYgAwAA8BiBDAAAwGMEMgAAAI8RyAAAADxGIAMAAPAYgQwAAMBjBDIAAACPEcgAAAA8RiADAADwGIEMAADAYwQyAAAAjxHIAAAAPEYgAwAA8BiBDAAAwGMEMgAAAI8lLZAZY6YbY7YYY3YbY3YZY/4yxDXGGPMvxpj9xpg3jTHVyWoPAABAuspP4r27Jf2VtbbBGFMuqd4Y85y1dvega1ZIqgq+fUjSvwXfAwAA5Iyk9ZBZaw9ZaxuCH7dK2iOpYshld0h63DqvSBprjJmarDYBAACko5TMITPGXCrpKkmvDjlVIengoD8HdHFoAwAAyGpJD2TGmDJJfknfsNaeHeY97jHGbDPGbDt27FhiGwgAAOCxpAYyY0yBXBj7b2vtuhCXNEuaPujPlcFj72OtfdRau8Rau2TSpEnJaSwAAIBHkrnK0kj6T0l7rLU/CXPZBkmfC662vEbSGWvtoWS1CQAAIB0lc5XlRyR9VtJOY8yO4LHvSpohSdbaRyRtlFQjab+kc5K+mMT2AAAApKWkBTJr7VZJJso1VtK9yWoDAABAJqBSPwAAgMcIZAAAAB4jkAEAAHiMQAYAAOAxAhkAAIDHCGQAAAAeI5ABAAB4jEAGAADgsWRW6gcA5KrWVmnNGqmpSaqqklaulMrLvW4VkLYIZACAxNq6VaqpkXp7pfZ2qbRUuu8+aeNGaelSr1sHpCWGLAEAidPa6sJYa6sLY5J733e8rc3b9gFpikAGAEicNWtcz1govb3uPICLEMgAAInT1DTQMzZUe7u0f39q2wNkCAIZACBxqqrcnLFQSkulWbNS2x4gQxDIAACJs3Kl5Avzq8Xnc+cBXIRABgBInPJyt5qyvHygp6y0dOB4WZm37QPSFGUvAACJtXSp1NLiJvDv3++GKVeuJIwBERDIAACJV1YmrVrldSuAjMGQJQAAgMcIZAAAAB4jkAEAAHiMQAYAAOAxAhkAAIDHWGUJAKnU2urKQTQ1uar2K1e6Gl0AchqBDABSZetWqabGbbLd3u4Kpt53nyuYunSp160D4CGGLAEgFVpbXRhrbR3YfLu9feB4W5u37QPgKQIZAKTCmjWuZyyU3l53HkDOIpABQCo0NQ30jA3V3u62GAKQswhkAJAKVVUDm20PVVrq9nsEkLMIZACQCitXSr4w/+T6fO48gJxFIAOAVCgvd6spy8sHespKSweOl5V52z4AnqLsBQCkytKlUkuLm8C/f78bply5kjAGgEAGAClVViatWuV1KwCkGYYsAQAAPEYgAwAA8BiBDAAAwGMEMgAAAI8xqR8Asl1rq1vZ2dTkCtSuXOnKbQBIGwQyAMhmW7e6zct7e90WTaWl0n33udpnS5d63ToAQQxZAkC2am11Yay1dWAfzfb2geNtbd62D0A/AhkAZKs1a1zPWCi9ve48gLRAIAOAbNXUNNAzNlR7u9stAEBaIJABQLaqqhrYN3Oo0lK3dROAtEAgA4BstXKl5Avzz7zP584DSAsEMgDIVuXlbjVleflAT1lp6cBxNjUH0gZlLwAgmy1dKrW0uAn8+/e7YcqVKwljQJohkAFAtisrk1at8roVACJgyBIAAMBjBDIAAACPEcgAAAA8RiADAADwWNICmTHmZ8aYo8aYxjDnbzDGnDHG7Ai+/W2y2gIAAJDOkrnK8jFJD0p6PMI1f7DW3prENgAAAKS9pPWQWWtflHQyWfcHAADIFl7PIbvWGPOGMeYZY8y8cBcZY+4xxmwzxmw7duxYKtsHAACQdF4GsgZJM621CyX9q6Qnw11orX3UWrvEWrtk0qRJqWofAABASngWyKy1Z621bcGPN0oqMMZM9Ko9AAAAXvEskBljphhjTPDjq4NtOeFVewAAALyStFWWxpgnJN0gaaIxJiDp+5IKJMla+4ikuyV9xRjTLem8pE9Za22y2gMAAJCukhbIrLX/K8r5B+XKYgAAAOQ0r1dZAgAA5DwCGQAAgMcIZAAAAB4jkAEAAHiMQAYAAOCxqIHMGPN1Y8y4VDQGAAAgF8VS9uISSa8bYxok/UzSs9QLAzBira3SmjVSU5NUVSWtXCmVl3vdKgDwhIklWwUr6t8s6YuSlkj6laT/tNa+ndzmXWzJkiV227ZtqX5
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
2022-06-12 17:45:18 +02:00
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"image/png": "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
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
|
|
|
|
|
]
|
2022-06-12 17:45:18 +02:00
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"image/png": "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
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
2022-06-12 17:45:18 +02:00
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"for n in range(1, 4):\n",
|
|
|
|
|
|
" plot_and_mse(data_train, data_test, n) "
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 145,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Ilość nauki do oceny"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 154,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/html": [
|
|
|
|
|
|
"<div>\n",
|
|
|
|
|
|
"<style scoped>\n",
|
|
|
|
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
|
|
|
|
" vertical-align: middle;\n",
|
|
|
|
|
|
" }\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" .dataframe tbody tr th {\n",
|
|
|
|
|
|
" vertical-align: top;\n",
|
|
|
|
|
|
" }\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" .dataframe thead th {\n",
|
|
|
|
|
|
" text-align: right;\n",
|
|
|
|
|
|
" }\n",
|
|
|
|
|
|
"</style>\n",
|
|
|
|
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
|
|
|
|
" <thead>\n",
|
|
|
|
|
|
" <tr style=\"text-align: right;\">\n",
|
|
|
|
|
|
" <th></th>\n",
|
|
|
|
|
|
" <th>number_courses</th>\n",
|
|
|
|
|
|
" <th>time_study</th>\n",
|
|
|
|
|
|
" <th>Marks</th>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </thead>\n",
|
|
|
|
|
|
" <tbody>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>0</th>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4.508</td>\n",
|
|
|
|
|
|
" <td>19.202</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>1</th>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>0.096</td>\n",
|
|
|
|
|
|
" <td>7.734</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>2</th>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>3.133</td>\n",
|
|
|
|
|
|
" <td>13.811</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>3</th>\n",
|
|
|
|
|
|
" <td>6</td>\n",
|
|
|
|
|
|
" <td>7.909</td>\n",
|
|
|
|
|
|
" <td>53.018</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>4</th>\n",
|
|
|
|
|
|
" <td>8</td>\n",
|
|
|
|
|
|
" <td>7.811</td>\n",
|
|
|
|
|
|
" <td>55.299</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>...</th>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>95</th>\n",
|
|
|
|
|
|
" <td>6</td>\n",
|
|
|
|
|
|
" <td>3.561</td>\n",
|
|
|
|
|
|
" <td>19.128</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>96</th>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>0.301</td>\n",
|
|
|
|
|
|
" <td>5.609</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>97</th>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>7.163</td>\n",
|
|
|
|
|
|
" <td>41.444</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>98</th>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>0.309</td>\n",
|
|
|
|
|
|
" <td>12.027</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>99</th>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>6.335</td>\n",
|
|
|
|
|
|
" <td>32.357</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
|
|
|
|
|
"<p>100 rows × 3 columns</p>\n",
|
|
|
|
|
|
"</div>"
|
|
|
|
|
|
],
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
" number_courses time_study Marks\n",
|
|
|
|
|
|
"0 3 4.508 19.202\n",
|
|
|
|
|
|
"1 4 0.096 7.734\n",
|
|
|
|
|
|
"2 4 3.133 13.811\n",
|
|
|
|
|
|
"3 6 7.909 53.018\n",
|
|
|
|
|
|
"4 8 7.811 55.299\n",
|
|
|
|
|
|
".. ... ... ...\n",
|
|
|
|
|
|
"95 6 3.561 19.128\n",
|
|
|
|
|
|
"96 3 0.301 5.609\n",
|
|
|
|
|
|
"97 4 7.163 41.444\n",
|
|
|
|
|
|
"98 7 0.309 12.027\n",
|
|
|
|
|
|
"99 3 6.335 32.357\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"[100 rows x 3 columns]"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 154,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"data_marks_all = pandas.read_csv('Student_Marks.csv')\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"data_marks_all"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 155,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"outputs": [],
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"source": [
|
|
|
|
|
|
"data_marks_all = data_marks_all[['time_study', 'Marks']]\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"# data_marks_all = data_marks_all.sample(frac=1)\n",
|
|
|
|
|
|
"data_marks_train = data_marks_all[0:70]\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"data_marks_test = data_marks_all[70:]\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"data_marks_train = np.matrix(data_marks_train).astype(float)\n",
|
|
|
|
|
|
"data_marks_test = np.matrix(data_marks_test).astype(float)"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 156,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Wielomian 1 stopnia, MSE = 383.05506630630464\n",
|
|
|
|
|
|
"Wielomian 2 stopnia, MSE = 394.48126522686164\n",
|
|
|
|
|
|
"Wielomian 3 stopnia, MSE = 392.8631214454169\n"
|
|
|
|
|
|
]
|
2022-06-18 11:52:01 +02:00
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"image/png": "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
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"image/png": "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
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/plain": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"image/png": "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
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"for n in range(1, 4):\n",
|
|
|
|
|
|
" plot_and_mse(data_marks, data_marks_test, n) "
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 149,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/html": [
|
|
|
|
|
|
"<div>\n",
|
|
|
|
|
|
"<style scoped>\n",
|
|
|
|
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
|
|
|
|
" vertical-align: middle;\n",
|
|
|
|
|
|
" }\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" .dataframe tbody tr th {\n",
|
|
|
|
|
|
" vertical-align: top;\n",
|
|
|
|
|
|
" }\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" .dataframe thead th {\n",
|
|
|
|
|
|
" text-align: right;\n",
|
|
|
|
|
|
" }\n",
|
|
|
|
|
|
"</style>\n",
|
|
|
|
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
|
|
|
|
" <thead>\n",
|
|
|
|
|
|
" <tr style=\"text-align: right;\">\n",
|
|
|
|
|
|
" <th></th>\n",
|
|
|
|
|
|
" <th>age</th>\n",
|
|
|
|
|
|
" <th>sex</th>\n",
|
|
|
|
|
|
" <th>bmi</th>\n",
|
|
|
|
|
|
" <th>children</th>\n",
|
|
|
|
|
|
" <th>smoker</th>\n",
|
|
|
|
|
|
" <th>region</th>\n",
|
|
|
|
|
|
" <th>charges</th>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </thead>\n",
|
|
|
|
|
|
" <tbody>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>238</th>\n",
|
|
|
|
|
|
" <td>19</td>\n",
|
|
|
|
|
|
" <td>male</td>\n",
|
|
|
|
|
|
" <td>29.070</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>yes</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>northwest</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>17352.68030</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>809</th>\n",
|
|
|
|
|
|
" <td>25</td>\n",
|
|
|
|
|
|
" <td>male</td>\n",
|
|
|
|
|
|
" <td>25.840</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>no</td>\n",
|
|
|
|
|
|
" <td>northeast</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>3309.79260</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>1053</th>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>male</td>\n",
|
|
|
|
|
|
" <td>29.800</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>yes</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>southwest</td>\n",
|
|
|
|
|
|
" <td>25309.48900</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>1177</th>\n",
|
|
|
|
|
|
" <td>40</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>female</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>27.400</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>no</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>southwest</td>\n",
|
|
|
|
|
|
" <td>6496.88600</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>964</th>\n",
|
|
|
|
|
|
" <td>52</td>\n",
|
|
|
|
|
|
" <td>male</td>\n",
|
|
|
|
|
|
" <td>36.765</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>no</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>northwest</td>\n",
|
|
|
|
|
|
" <td>26467.09737</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>...</th>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>374</th>\n",
|
|
|
|
|
|
" <td>20</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>male</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>33.330</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>no</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>southeast</td>\n",
|
|
|
|
|
|
" <td>1391.52870</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>950</th>\n",
|
|
|
|
|
|
" <td>57</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>male</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>18.335</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>no</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>northeast</td>\n",
|
|
|
|
|
|
" <td>11534.87265</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>954</th>\n",
|
|
|
|
|
|
" <td>34</td>\n",
|
|
|
|
|
|
" <td>male</td>\n",
|
|
|
|
|
|
" <td>27.835</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>yes</td>\n",
|
|
|
|
|
|
" <td>northwest</td>\n",
|
|
|
|
|
|
" <td>20009.63365</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>521</th>\n",
|
|
|
|
|
|
" <td>32</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>female</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>44.220</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>no</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>southeast</td>\n",
|
|
|
|
|
|
" <td>3994.17780</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <th>963</th>\n",
|
|
|
|
|
|
" <td>46</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>male</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>24.795</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>no</td>\n",
|
|
|
|
|
|
" <td>northeast</td>\n",
|
|
|
|
|
|
" <td>9500.57305</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
|
|
|
|
|
"<p>1338 rows × 7 columns</p>\n",
|
|
|
|
|
|
"</div>"
|
|
|
|
|
|
],
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
" age sex bmi children smoker region charges\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"238 19 male 29.070 0 yes northwest 17352.68030\n",
|
|
|
|
|
|
"809 25 male 25.840 1 no northeast 3309.79260\n",
|
|
|
|
|
|
"1053 47 male 29.800 3 yes southwest 25309.48900\n",
|
|
|
|
|
|
"1177 40 female 27.400 1 no southwest 6496.88600\n",
|
|
|
|
|
|
"964 52 male 36.765 2 no northwest 26467.09737\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"... ... ... ... ... ... ... ...\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"374 20 male 33.330 0 no southeast 1391.52870\n",
|
|
|
|
|
|
"950 57 male 18.335 0 no northeast 11534.87265\n",
|
|
|
|
|
|
"954 34 male 27.835 1 yes northwest 20009.63365\n",
|
|
|
|
|
|
"521 32 female 44.220 0 no southeast 3994.17780\n",
|
|
|
|
|
|
"963 46 male 24.795 3 no northeast 9500.57305\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"\n",
|
|
|
|
|
|
"[1338 rows x 7 columns]"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 149,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"data_ins = pandas.read_csv('insurance.csv')\n",
|
|
|
|
|
|
"data_ins = data_ins.sample(frac=1)\n",
|
|
|
|
|
|
"data_ins"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 150,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"outputs": [],
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"source": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"data_ins = data_ins[['age', 'charges']]\n",
|
|
|
|
|
|
"data_ins_train = data_ins[0:1200]\n",
|
|
|
|
|
|
"data_ins_test = data_ins[1200:]\n",
|
|
|
|
|
|
"data_ins_train = np.matrix(data_ins_train).astype(float)\n",
|
|
|
|
|
|
"data_ins_test = np.matrix(data_ins_test).astype(float)"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"execution_count": 151,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Wielomian 1 stopnia, MSE = 146688971.1828306\n",
|
|
|
|
|
|
"Wielomian 2 stopnia, MSE = 146881616.236919\n",
|
|
|
|
|
|
"Wielomian 3 stopnia, MSE = 146891792.9142127\n"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2022-06-18 11:52:01 +02:00
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"image/png": "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
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/plain": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAFkCAYAAABLi72wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABkvUlEQVR4nO3deXzU1b3/8dfJnkzCLlsAAUUQN1DqUql1qQto64KKSyvttbVVsUVtFb297e92E73XhdaltbRX6L0VFBeoQnG34g4KCiqLiLKEfZskZJuc3x9nxkzC7Jk1eT8fjzxm8p3vfL9nEqMfzzmfz8dYaxERERGR3JSX6QGIiIiISOIUzImIiIjkMAVzIiIiIjlMwZyIiIhIDlMwJyIiIpLDFMyJiIiI5LCCTA8g3Xr16mUHDx6c6WGIiKTGxo2wdWv41/v2hcrK9I1HRGK2dOnSHdbag+J9X6cL5gYPHsySJUsyPQwRkdSYMQOmTIGamgNf83jgN7+Bq69O+7BEJDpjzOeJvE/LrCIi6eD1ukDr1lvdo9ebmvtMnAh5Yf7VnpfnXheRDqXTzcyJiKTd4sUwfjw0N7sZM48HbroJFiyAsWOTe6+KCnfdtvfLy3PHy8uTez8RyTgFcyIiqeT1usAqeCYusAQ6fjxs3pz8AGvsWHfdOXNg7Vo49FA3I6dATqRDUjAnIpJKc+a4GbJQmpvd66nYw1Zerr1xIp2E9syJiKTSmjWhkxHAHV+7Nr3jEZEOR8GciEgqDRvm9qyF4vG4JVARkXZQMCcikkrKLhWRFFMwJyKSSoHs0oqKlhk6j6fluJISRKSdlAAhIpJqyi4VkRRSMCcikg7KLhWRFNEyq4iIiEgOUzAnIiIiksO0zCoiksu8XrcXb80aVwZl4kSXXCEinUZKZ+aMMd2MMXONMZ8YYz42xpxkjOlhjHneGLPG/9jdf64xxvzeGLPWGPOBMebYoOtM8p+/xhgzKej4ccaYD/3v+b0xxqTy84iIZJXFi6GyEqZMgbvuco+Vle64iHQaqV5mnQ7801o7AjgG+BiYCrxorR0GvOj/HmAcMMz/dQ3wEIAxpgfwS+AE4Hjgl4EA0H/OD4Led06KP4+ISHYI7vka6DBRU9NyvLo6s+MTkbRJWTBnjOkKnAL8BcBa22Ct3QOcD8z0nzYTuMD//HxglnXeAroZY/oBZwPPW2t3WWt3A88D5/hf62Ktfctaa4FZQdcSEenYYun5KiKdQipn5oYA24H/Mca8b4yZYYzxAH2stVX+c7YAffzPK4ENQe/f6D8W6fjGEMcPYIy5xhizxBizZPv27e38WCIiWUA9X0XEL5XBXAFwLPCQtXY0UEPLkioA/hk1m8IxBO7zsLV2jLV2zEEHHZTq24mIpJ56voqIXyqDuY3ARmvt2/7v5+KCu63+JVL8j9v8r28CBga9f4D/WKTjA0IcFxHp+NTzVUT8UhbMWWu3ABuMMcP9h84APgLmA4GM1EnAPP/z+cBV/qzWE4G9/uXYRcBZxpju/sSHs4BF/tf2GWNO9GexXhV0LRGRjk09X0XEL9V15m4A/s8YUwSsA76HCyAfM8ZcDXwOXOo/dwEwHlgL1PrPxVq7yxjza+Bd/3m/stbu8j+/DngEKAUW+r9EpKNTbTVHPV9FBDBu21rnMWbMGLtkyZJMD0NEErV4sSu90dzsNvp7PG5ZccECF9yIiOQoY8xSa+2YeN+ndl4ikjtUW01E5AAK5kQkd6i2mojIARTMiUjuUG01EZEDKJgTkdyh2moiIgdQMCciuUO11UREDqBgTkRyh2qriYgcINV15kREkku11UREWlEwJyK5p7wcrr4606MQEckKWmYVERERyWEK5kRERERymII5ERERkRymYE5EREQkhymYExEREclhCuZEREREcpiCOREREZEcpjpzItKxeL2uoPCaNa6X68SJrkOEiEgHpWBORDqOxYth/HhoboaaGtfq66abXKuvsWMzPToRkZTQMquIdAxerwvkvF4XyIF7DByvrs7s+EREUkTBnIh0DHPmuBm5UJqb3esiIh2QgjkR6RjWrGmZkWurpgbWrk3veERE0kTBnIh0DMOGuT1yoXg8cOih6R2PiEiaKJgTkY5h4kTIC/OvtLw897qISAekYE5EOoaKCpe1WlHRMkPn8bQcLy/P7PhERFJEpUlEpOMYOxY2b3bJDmvXuqXViRMVyIlIh6ZgTkQ6lvJyuPrqTI9CRCRttMwqIiIiksMUzImIiIjkMC2ziohIZOp3K5LVFMyJiEh46ncrkvW0zCoiIqGp361ITlAwJyIioanfrUhOUDAnIiKhqd+tSE5QMCciIqGp361ITlAwJyIioanfrUhOUDariGQXlcHIHoG+tm2zWfPy1O9WJIsomBOR7KEyGNlH/W5Fsp6x1mZ6DGk1ZswYu2TJkkwPQ0Ta8nqhstI9tlVR4QIKBRAi0oEZY5Zaa8fE+z7tmROR7KAyGCIiCVEwJyLZQWUwREQSomBORLKDymCIiCREwZyIZAeVwRARSUhKgzljzHpjzIfGmGXGmCX+Yz2MMc8bY9b4H7v7jxtjzO+NMWuNMR8YY44Nus4k//lrjDGTgo4f57/+Wv97TSo/j4ikUKAMRkVFywydx9NyXMkPIiIhpaM0yWnW2h1B308FXrTWTjPGTPV/fyswDhjm/zoBeAg4wRjTA/glMAawwFJjzHxr7W7/OT8A3gYWAOcAC9PwmUQkFVQGQ0QkbpmoM3c+cKr/+UzgFVwwdz4wy7paKW8ZY7oZY/r5z33eWrsLwBjzPHCOMeYVoIu19i3/8VnABSiYE8lt5eVw9dWZHoWISM5I9Z45CzxnjFlqjLnGf6yPtbbK/3wL0Mf/vBLYEPTejf5jkY5vDHH8AMaYa4wxS4wxS7Zv396ezyMiIiKSVVI9MzfWWrvJGNMbeN4Y80nwi9Zaa4xJedVia+3DwMPgigan+n4iIjlPbdVEckZKgzlr7Sb/4zZjzFPA8cBWY0w/a22Vfxl1m//0TcDAoLcP8B/bRMuybOD4K/7jA0KcLyKSHXI1IEq0rVqufl6RHJeydl7GGA+QZ631+p8/D/wKOAPYGZQA0cNae4sx5lxgMjAelwDxe2vt8f4EiKVAILv1PeA4a+0uY8w7wI9pSYD4g7V2QaRxqZ2XiKRFqIAo0KA+HX1mYw2s2p43fjyMGBG6rVphIdxzD1x0kfscwddevjyzn1ekA0i0nVcqg7mhwFP+bwuAv1trf2uM6Qk8BgwCPgcu9QdmBrgfl5FaC3zPWhsoZ/JvwO3+a/3WWvs//uNjgEeAUlziww02ygdSMCciKZfpPrOxBpKhzmtqAmOgri70tQsLobERSkrcOR6PO7+5GWprDzxffXVFYpZ1wVy2UjAnIik3YwZMmRK6PZnHA9Onpy5jN9ZAMtJ5yZTqzyvSgSQazKkDhIhIsmWyz+ycOW6WLJTmZvd6tPOSSX11RVJOwZyISLJlss9srIFkpPOSSX11RVJOwZyISLJlss9srIFkpPOKipI3HvXVFUk5BXMiIsmW6j6zXq/bl3frre4xeN9brIFkpPMaGuIfU2mp+1zqqysSVUNTM7c/9SFLP9+dlOspAUJEJFWqq5PfZ7ZtBmpZmXt+0UVw2mnxlQlpe632eOABuPBCmDoVVq2CQw6B0aNh+3bVnBMJsqumgR/9bSnvrN/FwB6lvHTzqRTmu/+xUjZrjBTMiUjOipaBWlYG+fkuaBs1KrZAsroaZs50RYETmZELKC11925shPr61q+p5pwIAGu3efm3R5bwxa5a+nYpYcakMRxZ2fXL1xMN5lLdzktERJIlWgZqoM7b+PGuBEks5UDKy6G42NWPa08wt39/+NcCs36BcWnZVTqhf63ezvV/fw9vXRNHVXZlxqQx9OlSkpRra8+ciEiuiDUDdf9+uP762GvIxXpdY2K7XjiNjS2lUUQ6kVlvrud7j7yLt66J8Uf15bEfnpS0QA4UzImI5I5IGajBmprg0Ufdkuzixe2/blGR2+/2s5/Fdv9w6urgo48Sf79IjmnyNfPLeSv4xbyV+JotN5x+KPdffiy
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"data": {
|
|
|
|
|
|
"image/png": "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
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"for n in range(1, 4):\n",
|
|
|
|
|
|
" plot_and_mse(data_ins_train, data_ins_test, n) "
|
2022-06-12 17:45:18 +02:00
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"author": "Paweł Skórzewski",
|
|
|
|
|
|
"celltoolbar": "Slideshow",
|
|
|
|
|
|
"email": "pawel.skorzewski@amu.edu.pl",
|
|
|
|
|
|
"kernelspec": {
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"display_name": "Python 3 (ipykernel)",
|
2022-06-12 17:45:18 +02:00
|
|
|
|
"language": "python",
|
|
|
|
|
|
"name": "python3"
|
|
|
|
|
|
},
|
|
|
|
|
|
"lang": "pl",
|
|
|
|
|
|
"language_info": {
|
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
|
"version": 3
|
|
|
|
|
|
},
|
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
|
"name": "python",
|
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
|
"pygments_lexer": "ipython3",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"version": "3.8.12"
|
2022-06-12 17:45:18 +02:00
|
|
|
|
},
|
|
|
|
|
|
"livereveal": {
|
|
|
|
|
|
"start_slideshow_at": "selected",
|
|
|
|
|
|
"theme": "white"
|
|
|
|
|
|
},
|
|
|
|
|
|
"subtitle": "5.Regresja wielomianowa. Problem nadmiernego dopasowania[wykład]",
|
|
|
|
|
|
"title": "Uczenie maszynowe",
|
|
|
|
|
|
"year": "2021"
|
|
|
|
|
|
},
|
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
|
"nbformat_minor": 4
|
2022-06-18 11:52:01 +02:00
|
|
|
|
}
|
