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MultiVar-Linear-Regression.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "MultiVar-Linear-Regression.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "authorship_tag": "ABX9TyMiqEVWYIRA+6ObXOk7q2cX" | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "tMsTK2Ig_658", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# **Multiple Variable Linear Regression**\n", | |
| "---\n", | |
| "Multiple variable linear regression คือ โมเดลทางสถิติที่สามารถบอกถึงความสัมพันธ์ ของตัวแปลต้น \"หลายตัว\" และตัวแปรตามได้ \n", | |
| "ซึ่งสามารถเขียน **hypothesis function** ได้ว่า <br>\n", | |
| "\\begin{equation}\n", | |
| "h_\\theta (x) = \\theta _0 + \\theta _1x_1 +\\theta _2x_2 + \\theta_3x_3+...+\\theta_nx_n\n", | |
| "\\end{equation} <br>\n", | |
| "ซึ่งถ้าเรามองว่า $\\theta$ คือ vector <$\\theta _0 , \\theta _1,\\theta _2 , \\theta_3,...,\\theta_n$><br>\n", | |
| "$\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;$ x คือ vector <$x_0 , x_1,x_2 , x_3,...,x_n$><br><br>\n", | |
| "จะได้ว่า\n", | |
| "\\begin{equation}\n", | |
| "h_\\theta (x) = \\theta^Tx \n", | |
| "\\end{equation} <br>\n", | |
| "<br>\n", | |
| " สามารถเขียน **cost function** หรือ loss function ได้ว่า <br><br> \n", | |
| "\\begin{equation}\n", | |
| "J(\\theta) = \\frac{1}{2m}\\sum^m_{i=1}(h_\\theta(x^{(i)}) - y^{(i)})^2\n", | |
| "\\end{equation}<br><br>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ZXNJiLFndjte", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "from sklearn.datasets import load_boston\n", | |
| "pd.set_option('display.max_rows', df.shape[0]+1) " | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "2yFTYsPuEGeg", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "โดยขั้นแรกจะทำการโหลด boston housing datasets ด้วย library sklearn " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "a3DfkMwvd5wO", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "dat = load_boston(return_X_y=False) # boston housing datasets" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "07SsL8bYeGSc", | |
| "colab_type": "code", | |
| "outputId": "b5c8cdad-62d8-4085-c400-52ee3e133026", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 195 | |
| } | |
| }, | |
| "source": [ | |
| "df = pd.DataFrame(dat.data,columns=dat.feature_names) # create dataframe\n", | |
| "df.head()" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>CRIM</th>\n", | |
| " <th>ZN</th>\n", | |
| " <th>INDUS</th>\n", | |
| " <th>CHAS</th>\n", | |
| " <th>NOX</th>\n", | |
| " <th>RM</th>\n", | |
| " <th>AGE</th>\n", | |
| " <th>DIS</th>\n", | |
| " <th>RAD</th>\n", | |
| " <th>TAX</th>\n", | |
| " <th>PTRATIO</th>\n", | |
| " <th>B</th>\n", | |
| " <th>LSTAT</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.00632</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>2.31</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.538</td>\n", | |
| " <td>6.575</td>\n", | |
| " <td>65.2</td>\n", | |
| " <td>4.0900</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>296.0</td>\n", | |
| " <td>15.3</td>\n", | |
| " <td>396.90</td>\n", | |
| " <td>4.98</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.02731</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>7.07</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.469</td>\n", | |
| " <td>6.421</td>\n", | |
| " <td>78.9</td>\n", | |
| " <td>4.9671</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>242.0</td>\n", | |
| " <td>17.8</td>\n", | |
| " <td>396.90</td>\n", | |
| " <td>9.14</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.02729</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>7.07</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.469</td>\n", | |
| " <td>7.185</td>\n", | |
| " <td>61.1</td>\n", | |
| " <td>4.9671</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>242.0</td>\n", | |
| " <td>17.8</td>\n", | |
| " <td>392.83</td>\n", | |
| " <td>4.03</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.03237</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2.18</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.458</td>\n", | |
| " <td>6.998</td>\n", | |
| " <td>45.8</td>\n", | |
| " <td>6.0622</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>222.0</td>\n", | |
| " <td>18.7</td>\n", | |
| " <td>394.63</td>\n", | |
| " <td>2.94</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.06905</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2.18</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.458</td>\n", | |
| " <td>7.147</td>\n", | |
| " <td>54.2</td>\n", | |
| " <td>6.0622</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>222.0</td>\n", | |
| " <td>18.7</td>\n", | |
| " <td>396.90</td>\n", | |
| " <td>5.33</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " CRIM ZN INDUS CHAS NOX ... RAD TAX PTRATIO B LSTAT\n", | |
| "0 0.00632 18.0 2.31 0.0 0.538 ... 1.0 296.0 15.3 396.90 4.98\n", | |
| "1 0.02731 0.0 7.07 0.0 0.469 ... 2.0 242.0 17.8 396.90 9.14\n", | |
| "2 0.02729 0.0 7.07 0.0 0.469 ... 2.0 242.0 17.8 392.83 4.03\n", | |
| "3 0.03237 0.0 2.18 0.0 0.458 ... 3.0 222.0 18.7 394.63 2.94\n", | |
| "4 0.06905 0.0 2.18 0.0 0.458 ... 3.0 222.0 18.7 396.90 5.33\n", | |
| "\n", | |
| "[5 rows x 13 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 848 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "2_yJzlEREVJB", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "เป้าหมายของเราคือการทำนายราคาของบ้าน จากข้อมูล" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "f5E8yL_2euZn", | |
| "colab_type": "code", | |
| "outputId": "2482572d-390f-49ab-e740-26a8cc453f70", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 195 | |
| } | |
| }, | |
| "source": [ | |
| "target = pd.DataFrame(dat.target.T,columns=['PRICES']) # target prices\n", | |
| "target.head()" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>PRICES</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>24.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>21.6</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>34.7</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>33.4</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>36.2</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " PRICES\n", | |
| "0 24.0\n", | |
| "1 21.6\n", | |
| "2 34.7\n", | |
| "3 33.4\n", | |
| "4 36.2" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 849 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "az8NLroNEfpP", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "โดยจากที่เห็นตัวข้อมูลจะพบว่า ข้อมูลในแต่ละ feature มี scale ที่ต่างกันมาก เลยมากทำการ scaling ด้วย **Z-score Normalization**<br><br>\n", | |
| "\\begin{equation}\n", | |
| "x_j := \\frac{x_j - \\mu_j}{\\sigma_j}\n", | |
| "\\end{equation} <br>\n", | |
| "$\\mu_j$ คือ ค่าเฉลี่ยของ feature นั้นๆ<br>\n", | |
| "$\\sigma_j$ คือ ส่วนเบี่ยงเบนมาตรฐานของ feature นั้นๆ" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "cYXf-wch1pim", | |
| "colab_type": "code", | |
| "outputId": "e1bfa058-9772-4e98-810b-141345e691d4", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 195 | |
| } | |
| }, | |
| "source": [ | |
| "def scaling(x): #scaling input \n", | |
| " std = np.std(x)\n", | |
| " std[0] = 1\n", | |
| " mean = np.mean(x)\n", | |
| " mean[0] = 0\n", | |
| " return (x-mean)/std\n", | |
| "df = scaling(df)\n", | |
| "df.head()" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>CRIM</th>\n", | |
| " <th>ZN</th>\n", | |
| " <th>INDUS</th>\n", | |
| " <th>CHAS</th>\n", | |
| " <th>NOX</th>\n", | |
| " <th>RM</th>\n", | |
| " <th>AGE</th>\n", | |
| " <th>DIS</th>\n", | |
| " <th>RAD</th>\n", | |
| " <th>TAX</th>\n", | |
| " <th>PTRATIO</th>\n", | |
| " <th>B</th>\n", | |
| " <th>LSTAT</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.00632</td>\n", | |
| " <td>0.284830</td>\n", | |
| " <td>-1.287909</td>\n", | |
| " <td>-0.272599</td>\n", | |
| " <td>-0.144217</td>\n", | |
| " <td>0.413672</td>\n", | |
| " <td>-0.120013</td>\n", | |
| " <td>0.140214</td>\n", | |
| " <td>-0.982843</td>\n", | |
| " <td>-0.666608</td>\n", | |
| " <td>-1.459000</td>\n", | |
| " <td>0.441052</td>\n", | |
| " <td>-1.075562</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.02731</td>\n", | |
| " <td>-0.487722</td>\n", | |
| " <td>-0.593381</td>\n", | |
| " <td>-0.272599</td>\n", | |
| " <td>-0.740262</td>\n", | |
| " <td>0.194274</td>\n", | |
| " <td>0.367166</td>\n", | |
| " <td>0.557160</td>\n", | |
| " <td>-0.867883</td>\n", | |
| " <td>-0.987329</td>\n", | |
| " <td>-0.303094</td>\n", | |
| " <td>0.441052</td>\n", | |
| " <td>-0.492439</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.02729</td>\n", | |
| " <td>-0.487722</td>\n", | |
| " <td>-0.593381</td>\n", | |
| " <td>-0.272599</td>\n", | |
| " <td>-0.740262</td>\n", | |
| " <td>1.282714</td>\n", | |
| " <td>-0.265812</td>\n", | |
| " <td>0.557160</td>\n", | |
| " <td>-0.867883</td>\n", | |
| " <td>-0.987329</td>\n", | |
| " <td>-0.303094</td>\n", | |
| " <td>0.396427</td>\n", | |
| " <td>-1.208727</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.03237</td>\n", | |
| " <td>-0.487722</td>\n", | |
| " <td>-1.306878</td>\n", | |
| " <td>-0.272599</td>\n", | |
| " <td>-0.835284</td>\n", | |
| " <td>1.016303</td>\n", | |
| " <td>-0.809889</td>\n", | |
| " <td>1.077737</td>\n", | |
| " <td>-0.752922</td>\n", | |
| " <td>-1.106115</td>\n", | |
| " <td>0.113032</td>\n", | |
| " <td>0.416163</td>\n", | |
| " <td>-1.361517</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.06905</td>\n", | |
| " <td>-0.487722</td>\n", | |
| " <td>-1.306878</td>\n", | |
| " <td>-0.272599</td>\n", | |
| " <td>-0.835284</td>\n", | |
| " <td>1.228577</td>\n", | |
| " <td>-0.511180</td>\n", | |
| " <td>1.077737</td>\n", | |
| " <td>-0.752922</td>\n", | |
| " <td>-1.106115</td>\n", | |
| " <td>0.113032</td>\n", | |
| " <td>0.441052</td>\n", | |
| " <td>-1.026501</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " CRIM ZN INDUS ... PTRATIO B LSTAT\n", | |
| "0 0.00632 0.284830 -1.287909 ... -1.459000 0.441052 -1.075562\n", | |
| "1 0.02731 -0.487722 -0.593381 ... -0.303094 0.441052 -0.492439\n", | |
| "2 0.02729 -0.487722 -0.593381 ... -0.303094 0.396427 -1.208727\n", | |
| "3 0.03237 -0.487722 -1.306878 ... 0.113032 0.416163 -1.361517\n", | |
| "4 0.06905 -0.487722 -1.306878 ... 0.113032 0.441052 -1.026501\n", | |
| "\n", | |
| "[5 rows x 13 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 850 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "5zi88A1sGbjZ", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "ต่อจากนั้นก็มาทำสมการ **hypothesis function**, **cost function** และ **gradient descent** <br><br>\n", | |
| "\\begin{equation}\n", | |
| "Hypothesis\\;Function :\\;\\;\n", | |
| "h_\\theta (x) = \\theta^Tx \n", | |
| "\\end{equation} <br>\n", | |
| "\n", | |
| "\\begin{equation}\n", | |
| "Cost\\;Function\\;:\\;\\;\n", | |
| "J(\\theta) = \\frac{1}{2m}\\sum^m_{i=1}(h_\\theta(x^{(i)}) - y^{(i)})^2\n", | |
| "\\end{equation}<br>\n", | |
| "\\begin{equation}\n", | |
| "Gradient\\;Descent\\;:\\;\\;\n", | |
| "\\theta_j := \\theta_j-\\alpha\\frac{1}{m}\\sum^m_{i=1}(h_\\theta(x^{(i)}) - y^{(i)})x^{(i)}_j\n", | |
| "\\end{equation}<br><br>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "SlfVM0ughBMH", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "ones = pd.DataFrame({'ones':np.ones(df.shape[0]).T}) \n", | |
| "df = ones.join(df)\n", | |
| "m = df.shape[0]\n", | |
| "n = df.shape[1]\n", | |
| "s = np.zeros(n)\n", | |
| "sT = np.array([s]).T #all parameters\n", | |
| "\n", | |
| "def hypo(x,sT): #hypothesis function\n", | |
| " return np.dot(x,sT)\n", | |
| "\n", | |
| "def costFunction(x,sT): #cost function\n", | |
| " h = hypo(x,sT)\n", | |
| " return (1/(2*m))*sum(np.array((h-target))**2)\n", | |
| " \n", | |
| "def gradientCost(x,sT): #gradient descent\n", | |
| " h = hypo(x,sT)\n", | |
| " return (1/(m))*np.dot(df.T,(h-target))" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "JIS3w31RIPQA", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "กำหนดให้ learning rate = 0.01 <br>\n", | |
| "และ เริ่มทำการ train" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "SXZkpeYVnr8T", | |
| "colab_type": "code", | |
| "outputId": "f0645810-795f-42fc-abe7-40476821471b", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 67 | |
| } | |
| }, | |
| "source": [ | |
| "alpha = 0.01 # learning rate\n", | |
| "last = costFunction(df,sT)+1\n", | |
| "while last - costFunction(df,sT)>0.001: #loop until change < 0.001\n", | |
| " last = costFunction(df,sT)\n", | |
| " sT = sT - alpha*(gradientCost(df,sT))\n", | |
| "print('result :',sT.T[0])" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "result : [22.65365393 -0.06590317 0.64831977 -0.18460845 0.76100326 -1.12507124\n", | |
| " 3.03972749 -0.11594876 -2.25233247 0.81150778 -0.56628214 -1.82116539\n", | |
| " 0.88059889 -3.70432229]\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "e9lA8SbCItDe", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "สรุป เปรียบเทียบผลการทำนาย" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "6RZewRxQsIQv", | |
| "colab_type": "code", | |
| "outputId": "50df3762-66ba-4ea7-862c-54c8ed2ce52a", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 195 | |
| } | |
| }, | |
| "source": [ | |
| "compare = pd.DataFrame({'Target Prices':np.array(target).T[0],'Predicted Prices':np.dot(df,sT).T[0]}) # comparing result with target \n", | |
| "compare.head()" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>Target Prices</th>\n", | |
| " <th>Predicted Prices</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>24.0</td>\n", | |
| " <td>30.595625</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>21.6</td>\n", | |
| " <td>24.982998</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>34.7</td>\n", | |
| " <td>30.979016</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>33.4</td>\n", | |
| " <td>29.284144</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>36.2</td>\n", | |
| " <td>28.673260</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Target Prices Predicted Prices\n", | |
| "0 24.0 30.595625\n", | |
| "1 21.6 24.982998\n", | |
| "2 34.7 30.979016\n", | |
| "3 33.4 29.284144\n", | |
| "4 36.2 28.673260" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 853 | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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