{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "E6Ld9nmrdu11" }, "source": [ "# Phystech@DataScience\n", "## Занятие 2. Введение в машинное обучение. Линейная регрессия.\n", "\n", "*Примечание.* Подробнее про работу с различными библиотеками Питона можно посмотреть в наших туториалах.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2021-02-17T16:30:31.359489Z", "start_time": "2021-02-17T16:30:30.626327Z" }, "id": "x-ocQGTeV0C1" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import scipy.stats as sps\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set(style='darkgrid', font_scale=1.5)\n", "\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error\n", "from sklearn.linear_model import LinearRegression\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "id": "u6JWzXD0E0JQ" }, "source": [ "### Пример построения линейной регрессии\n", "\n", "Скачаем данные, полученные из книги \"Модели и концепции физики: механика.\n", "Лабораторный практикум.\n", "Обработка результатов измерений.\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-02-17T16:30:31.366612Z", "start_time": "2021-02-17T16:30:31.361139Z" }, "id": "9QnN-lBFEV15" }, "outputs": [], "source": [ "data = pd.read_csv(\"lab_data.csv\", index_col='Unnamed: 0') " ] }, { "cell_type": "markdown", "metadata": { "id": "QXbptLGOrPfY" }, "source": [ "Посмотрим на данные" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-02-17T16:30:31.395716Z", "start_time": "2021-02-17T16:30:31.368333Z" }, "colab": { "base_uri": "https://localhost:8080/", "height": 514 }, "id": "XUu3AQFeYHm8", "outputId": "ef78b98e-6bd0-4d9c-efe6-a9d5c9760182" }, "outputs": [ { "data": { "text/html": [ "
\n", " | n | \n", "h | \n", "T | \n", "I | \n", "
---|---|---|---|---|
0 | \n", "1.0 | \n", "0.0 | \n", "2.48 | \n", "1.78 | \n", "
1 | \n", "2.0 | \n", "0.5 | \n", "2.47 | \n", "1.74 | \n", "
2 | \n", "3.0 | \n", "1.0 | \n", "2.50 | \n", "1.86 | \n", "
3 | \n", "4.0 | \n", "1.5 | \n", "2.54 | \n", "2.00 | \n", "
4 | \n", "5.0 | \n", "2.0 | \n", "2.62 | \n", "2.30 | \n", "
5 | \n", "6.0 | \n", "2.5 | \n", "2.71 | \n", "2.63 | \n", "
6 | \n", "7.0 | \n", "3.0 | \n", "2.79 | \n", "2.97 | \n", "
7 | \n", "8.0 | \n", "3.5 | \n", "2.91 | \n", "3.46 | \n", "
8 | \n", "9.0 | \n", "4.0 | \n", "3.05 | \n", "4.07 | \n", "
9 | \n", "10.0 | \n", "4.5 | \n", "3.16 | \n", "4.56 | \n", "
10 | \n", "11.0 | \n", "5.0 | \n", "3.31 | \n", "5.26 | \n", "
11 | \n", "12.0 | \n", "5.5 | \n", "3.47 | \n", "6.05 | \n", "
12 | \n", "13.0 | \n", "6.0 | \n", "3.62 | \n", "6.82 | \n", "
13 | \n", "14.0 | \n", "6.5 | \n", "3.80 | \n", "7.79 | \n", "
14 | \n", "15.0 | \n", "7.0 | \n", "3.95 | \n", "8.63 | \n", "