\n",
+ "\n",
+ "\n",
+ "# SVM (Support Vector Machines)\n",
+ "\n",
+ "\n",
+ "Estimated time needed: **15** minutes\n",
+ " \n",
+ "\n",
+ "## Objectives\n",
+ "\n",
+ "After completing this lab you will be able to:\n",
+ "\n",
+ "* Use scikit-learn to Support Vector Machine to classify\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this notebook, you will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.\n",
+ "\n",
+ "SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "The example is based on a dataset that is publicly available from the UCI Machine Learning Repository (Asuncion and Newman, 2007)[http://mlearn.ics.uci.edu/MLRepository.html]. The dataset consists of several hundred human cell sample records, each of which contains the values of a set of cell characteristics. The fields in each record are:\n",
+ "\n",
+ "|Field name|Description|\n",
+ "|--- |--- |\n",
+ "|ID|Clump thickness|\n",
+ "|Clump|Clump thickness|\n",
+ "|UnifSize|Uniformity of cell size|\n",
+ "|UnifShape|Uniformity of cell shape|\n",
+ "|MargAdh|Marginal adhesion|\n",
+ "|SingEpiSize|Single epithelial cell size|\n",
+ "|BareNuc|Bare nuclei|\n",
+ "|BlandChrom|Bland chromatin|\n",
+ "|NormNucl|Normal nucleoli|\n",
+ "|Mit|Mitoses|\n",
+ "|Class|Benign or malignant|\n",
+ "\n",
+ " \n",
+ " \n",
+ "\n",
+ "For the purposes of this example, we're using a dataset that has a relatively small number of predictors in each record. To download the data, we will use `!wget` to download it from IBM Object Storage. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2025-11-19 04:37:48-- https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv\n",
+ "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
+ "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
+ "200 OKequest sent, awaiting response... \n",
+ "Length: 19975 (20K) [text/csv]\n",
+ "Saving to: ‘cell_samples.csv’\n",
+ "\n",
+ "cell_samples.csv 100%[===================>] 19.51K --.-KB/s in 0s \n",
+ "\n",
+ "2025-11-19 04:37:48 (73.0 MB/s) - ‘cell_samples.csv’ saved [19975/19975]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Click here and press Shift+Enter\n",
+ "!wget -O cell_samples.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load Data From CSV File \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "text/plain": [
+ " ID Clump UnifSize UnifShape MargAdh SingEpiSize BareNuc \\\n",
+ "0 1000025 5 1 1 1 2 1 \n",
+ "1 1002945 5 4 4 5 7 10 \n",
+ "2 1015425 3 1 1 1 2 2 \n",
+ "3 1016277 6 8 8 1 3 4 \n",
+ "4 1017023 4 1 1 3 2 1 \n",
+ "\n",
+ " BlandChrom NormNucl Mit Class \n",
+ "0 3 1 1 2 \n",
+ "1 3 2 1 2 \n",
+ "2 3 1 1 2 \n",
+ "3 3 7 1 2 \n",
+ "4 3 1 1 2 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cell_df = pd.read_csv(\"cell_samples.csv\")\n",
+ "cell_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The ID field contains the patient identifiers. The characteristics of the cell samples from each patient are contained in fields Clump to Mit. The values are graded from 1 to 10, with 1 being the closest to benign.\n",
+ "\n",
+ "The Class field contains the diagnosis, as confirmed by separate medical procedures, as to whether the samples are benign (value = 2) or malignant (value = 4).\n",
+ "\n",
+ "Let's look at the distribution of the classes based on Clump thickness and Uniformity of cell size:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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vvFAZGRmtfr4nn3xS//Ef/6GMjAxFRUXpvvvu07Zt2xQRERGoRQIAoEVcViBOighh1dXVio6OVlVVlaKiovzmDh48qJKSErnd7gBsjAtVe05Mkuw4L8YO+/bt06mnnqr58+fr1ltvtTtOPYFdvwCAYGps+30s9sgETLKcXmA2btyob7/9VhdccIGqqqo0Z84cSdKVV15pczIAQEdFkUGLPPHEE9q8ebM6d+6s888/Xx999JF69uxpdywAQAdFkUGzDRs2TJ999pndMQAA8OFTSwAAwFgUGSkgXwKH0MN6BQDn69BFpu4baPfv329zErSHuvV6/DcNAwCco0OfIxMWFqaYmBjf7wB169aNL3dzAMuytH//fu3atUsxMTH1fqYBAOAcHbrISFKfPn0kiR81dKCYmBjf+gUAOFOHLzIul0t9+/ZVr169dPjwYbvjIEA6derEnhgA6AA6fJGpExYWxoYPAADDdOiTfQEAgNkoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY4V0kfF6vZo9e7bcbre6du2qAQMG6KGHHpJlWXZHA9BhFEhaIanQ7iBtUlBQoRUrtqiwsNLuKEBAhdsdoDGPPfaYFi1apGXLlmnIkCFav369br75ZkVHR2vq1Kl2xwPgaBWSMiV5jhlLl5QlKdaWRK1RUXFAmZnvyuMp9Y2lpycqKytDsbER9gUDAiSk98h88sknuvLKKzV+/HglJibq2muv1SWXXKJ169bZHQ2A42VKyjluLEfSBBuytF5m5rvKySnzG8vJKdOECe/YlAgIrJAuMiNHjtSqVatUUFAgSfr888/18ccf69JLLz3hfQ4dOqTq6mq/CwC0TIFq98R4jxv3Hh034zBTQUGFPJ5Seb3+h+O9XkseTymHmeAIIX1oaebMmaqurtagQYMUFhYmr9erhx9+WBMnTjzhfebNm6cHH3wwiCkBOE9xE/NFkpKDEaRNiot3NzpfVFSp5GRzDpMBDQnpPTKvvPKKXnrpJb388svasGGDli1bpieeeELLli074X1mzZqlqqoq32Xbtm1BTAzAGQY0MZ8UlBRtNWBATKPzSUmUGJgvpPfI/PrXv9bMmTN1ww03SJLOPvtslZWVad68ebrpppsavE+XLl3UpUuXYMYE4DgDVXtib478Dy+FSUqTCXtjJGngwB5KT09UTk6Z3+GlsDCX0tL6szcGjhDSe2T279+vk07yjxgWFqaamhqbEgHoOLJUW1qOlXZ03BxZWRlKS+vvN5aW1l9ZWRk2JQICK6T3yFx++eV6+OGH1a9fPw0ZMkQbN27Uk08+qVtuucXuaAAcL1bSStWe2Fuk2sNJZuyJOVZsbIRWrrxWhYWVKiqqVFJSLHti4CguK4S/XW7Pnj2aPXu23njjDe3atUvx8fGaMGGCHnjgAXXu3LlZj1FdXa3o6GhVVVUpKiqqnRMDAIBAaO72O6SLTCBQZAAAME9zt98hfY4MAABAYygyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWOF2BwACpaCgQsXFu5WUFKvk5Fi74wBoB7zPQ4vHU6K8vB1KTY3X2LGJtmSgyMB4FRUHlJn5rjyeUt9YenqisrIyFBsbYV8wAAHD+zy0FBdXKiXlJZWXH/SNxcVFKD//RrndMUHNwqElGC8z813l5JT5jeXklGnChHdsSgQg0Hifh5bjS4wklZcf1IgRLwY9C0UGRisoqJDHUyqv1/Ib93oteTylKiystCkZgEDhfR5aPJ6SeiWmTnn5QWVnlwY1D0UGRisu3t3ofFERf+AA0/E+Dy15eTsanc/N3R6kJLUoMjDagAExjc4nJXEyIGA63uehJSWlb6PzqanxQUpSiyIDow0c2EPp6YkKC3P5jYeFuZSensinGgAH4H0eWtLT3YqLa/gE67i4iKB/eokiA+NlZWUoLa2/31haWn9lZWXYlAhAoPE+Dy35+TfWKzN1n1oKNpdlWVbTNzNXdXW1oqOjVVVVpaioKLvjoB0VFlaqqKiS75cAHIz3eWjJzi5Vbu72dvkemeZuvykyAAAg5DR3+82hJQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMFari8zu3bv13HPPadasWaqoqJAkbdiwQd99913AwgEAADQmvDV3+uKLL5SWlqbo6GiVlpbqtttuU48ePfT6669r69atev755wOdE2hSQUGFiot3KykpVsnJsXbH6fCcsz4KJBVLSpKUbHMWOIVT3h8eT4ny8nYoNTVeY8cm2pKhVUVmxowZmjRpkh5//HFFRkb6xi+77DJlZmYGLJwkfffdd/rNb36jFStWaP/+/UpKStKSJUs0fPjwgD4PzFVRcUCZme/K4yn1jaWnJyorK0OxsRH2BeugnLM+KiRlSvIcM5YuKUuSuRse2Msp74/i4kqlpLyk8vKDvrG4uAjl598otzsmqFladWgpPz9fv/rVr+qNn3rqqdq5c2ebQ9WprKzUqFGj1KlTJ61YsUJff/215s+fr9hY/ojg3zIz31VOTpnfWE5OmSZMeMemRB2bc9ZHpqSc48ZyJE2wIQucwinvj+NLjCSVlx/UiBEvBj1Lq/bIdOnSRdXV1fXGCwoKdMopp7Q5VJ3HHntMCQkJWrJkiW/M7XY3ep9Dhw7p0KFDvusN5YRzFBRU+P3Lpo7Xa8njKVVhYaXRu21N45z1USD/PTF1vEfHC8VhJrSUU94fHk9JvRJTp7z8oLKzS4N6mKlVe2SuuOIKzZkzR4cPH5YkuVwubd26Vb/5zW/0s5/9LGDh3n77bQ0fPlzXXXedevXqpWHDhunZZ59t9D7z5s1TdHS075KQkBCwPAg9xcW7G50vKqoMThBIctL6KG5ivigoKeAsTnl/5OXtaHQ+N3d7kJLUalWRmT9/vvbu3atevXrpwIEDuvjii5WUlKTIyEg9/PDDAQu3ZcsWLVq0SMnJyfJ4PLrjjjs0depULVu27IT3mTVrlqqqqnyXbdu2BSwPQs+AATGNziclhf6/bpzEOetjQBPzSUFJAWdxyvsjJaVvo/OpqfFBSlLLZVmW1do7f/zxx/riiy+0d+9enXfeeUpLSwtkNnXu3FnDhw/XJ5984hubOnWq8vPzlZub26zHqK6uVnR0tKqqqhQVFRXQfAgN48a9ppycMnm9/34ph4W5lJbWXytXXmtjso7JOetjnGrPifEeMxYmKU3SSlsSwXxOeX/07PnHBg8vxcVF6IcfpgTkOZq7/W7VHpmtW7fq0KFD+tGPfqQ777xT9913n9LS0mRZlrZu3drq0Mfr27evzjzzTL+xwYMHB/Q5YL6srAylpfX3G0tL66+srAybEnVszlkfWaotLcdKOzoOtI5T3h/5+TcqLs7/U1Z1n1oKtlbtkTnppJM0ePBgvf322xow4N+7YL///nvFx8fL6/U2cu/my8zM1LZt2/TRRx/5xu6++27l5eX57aVpDHtkOo7CwkoVFVUa/70MTuGc9VGo2nNi+B4ZBI5T3h/Z2aXKzd3eLt8j09ztd6uLzDXXXKPVq1frlVde0ZgxYyTVFpm+ffuqpqam9cmPkZ+fr5EjR+rBBx/U9ddfr3Xr1um2227T4sWLNXHixGY9BkUGAADztOuhJZfLpT/96U/67W9/q/Hjx2vBggV+c4EyYsQIvfHGG8rKytJZZ52lhx56SE899VSzSwwAAHC2Vn2PTN1OnLvvvluDBg3ShAkT9OWXX+qBBx4IaDhJysjIUEaGWccOAQBAcLSqyBzr0ksv1SeffKIrrrhC69atC0QmAACAZmnVoaWLL75YnTt39l0/88wzlZeXp5iYGLXh09wAAAAt0qbvkTEBJ/sCAGCe5m6/m31oqbq62vdATf1+EYUBAAAEQ7OLTGxsrHbs2KFevXopJiamwU8nWZYll8sVsO+RAQAAaEyzi8z777+vHj16SJJWr17dboEAAACai3NkAABAyGmXL8T74YcfVFZW5jf2j3/8QzfffLOuv/56vfzyy61LCwAA0AotKjJ33XWX37f47tq1Sz/+8Y+Vn5+vQ4cOadKkSXrhhRcCHhIAAKAhLSoyn376qa644grf9eeff149evTQpk2b9NZbb+mRRx7RwoULAx4SAACgIS0qMjt37lRiYqLv+vvvv69rrrlG4eG15wxfccUVKiwsDGhAAACAE2lRkYmKitLu3bt919etW6eUlBTfdZfLpUOHDgUsHAAAQGNaVGQuvPBCLViwQDU1NXrttde0Z88e/fSnP/XNFxQUKCEhIeAhAQAAGtKiH4186KGHNGbMGL344os6cuSI7r//fsXGxvrmly9frosvvjjgIQEAABrSoiIzdOhQffPNN1q7dq369Onjd1hJkm644QadeeaZAQ0IAABwIs0+tNSjRw/98MMP6tmzp956660GC8v48ePldrsDGhAAAOBEml1k/u///s/3Y5HLli3TwYMH2y0UAABAczT70FJqaqquuuoqnX/++bIsS1OnTlXXrl0bvO1f//rXgAUEAAA4kWYXmRdffFG///3vVVxcLJfLpaqqKvbKAAAAW7XqRyPdbrfWr1+vuLi49sgUUPxoJAAA5mnu9rtFn1qqU1JS0upgAAAAgdLsIrNgwQLdfvvtioiI8PvhyIZMnTq1zcEAAACa0uxDS8ceTmrsI9Yul0tbtmwJWMC24tASAADmCfihpWMPJ3FoCQAAhIIW/dYSAABAKGnVyb5er1dLly7VqlWrtGvXLtXU1PjNv//++wEJBwAA0JhWFZlp06Zp6dKlGj9+vM466yy5XK5A50IQFRRUqLh4t5KSYpWcHNv0HUKUU5bDKVgfoYX1EVo8nhLl5e1Qamq8xo5NtDtOq4XCcrSqyCxfvlyvvPKKLrvsskDnQRBVVBxQZua78nhKfWPp6YnKyspQbGyEfcFayCnL4RSsj9DC+ggtxcWVSkl5SeXl//5C2bi4COXn3yi3O8a+YC0USsvRqnNkOnfurKSkpEBnQZBlZr6rnJwyv7GcnDJNmPCOTYlaxynL4RSsj9DC+ggtx2/8Jam8/KBGjHjRpkStE0rL0aoic8899+jpp59WK74UGCGioKBCHk+pvF7/dej1WvJ4SlVYWGlTspZxynI4BesjtLA+QovHU1Jv41+nvPygsrNLgxuolUJtOVp1aOnjjz/W6tWrtWLFCg0ZMkSdOnXym3/99dcDEg7tp7h4d6PzRUWVRhxHd8pyOAXrI7SwPkJLXt6ORudzc7cbcb5MqC1Hq4pMTEyMrr766kBnQRANGBDT6HxSkhl/3JyyHE7B+ggtrI/QkpLSt9H51NT4ICVpm1BbjlYVmSVLlgQ6B4Js4MAeSk9PVE5Omd9u57Awl9LS+hvzrzSnLIdTsD5CC+sjtKSnuxUXF9HgYZm4uAgj9sZIobccLTpHJjY2Vj169Kh3cbvdSk9PV3Z2dnvlRDvIyspQWlp/v7G0tP7KysqwKVHrOGU5nIL1EVpYH6ElP/9GxcX5f1qs7tM+Jgml5Wj2by1J0rJlyxoc3717tz777DP97W9/02uvvabLL788YAHbit9aalphYaWKiiqN/34JpyyHU7A+QgvrI7RkZ5cqN3e78d8j057L0dztd4uKTFOefPJJvfbaa/rkk08C9ZBtRpEBAMA8zd1+B/S3ljIyMvTtt98G8iEBAABOKKBF5tChQ+rcuXMgHxIAAOCEAlpk/vKXv+jcc88N5EMCAACcUIs+fj1jxowGx6uqqrRhwwYVFBToww8/DEgwAACAprSoyGzcuLHB8aioKI0dO1avv/663G53QIIBAAA0pUVFZvXq1e2VAwAAoMUCeo4MAABAMFFkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMJZRRebRRx+Vy+XS9OnT7Y4CoAklJeuUn/9XlZautzsKJBUUVGjFii0qLKy0O0qbsByhxeMp0Zw5nyg7u9S2DOG2PXML5efn65lnntHQoUPtjgKgEZWV36m4eLyGD/9cbnft2Pr15ygpaYViYvraG64Dqqg4oMzMd+XxlPrG0tMTlZWVodjYCPuCtRDLEVqKiyuVkvKSyssP+sbi4iKUn3+j3O6YoGYxYo/M3r17NXHiRD377LOKjY21Ow6ARhQXj9e5537hN3buuV+oqOhSmxJ1bJmZ7yonp8xvLCenTBMmvGNTotZhOULL8SVGksrLD2rEiBeDnsWIIjN58mSNHz9eaWlpTd720KFDqq6u9rsACI6SknUaPvxzhYdbfuPh4ZaGD/+cw0xBVlBQIY+nVF6v//rwei15PKXGHNZgOUKLx1NSr8TUKS8/GPTDTCFfZJYvX64NGzZo3rx5zbr9vHnzFB0d7bskJCS0c0IAdX744atG5//1ry8anUdgFRfvbnS+qMiMDSfLEVry8nY0Op+buz1ISWqFdJHZtm2bpk2bppdeekkREc07djhr1ixVVVX5Ltu2bWvnlADq9Ox5VqPzp5zCOW7BNGBATKPzSUlmHKpnOUJLSkrj57qlpsYHKUmtkC4yn332mXbt2qXzzjtP4eHhCg8P15o1a7RgwQKFh4fL6/XWu0+XLl0UFRXldwEQHG73BVq//hwdOeLyGz9yxKX1689RYuJwm5J1TAMH9lB6eqLCwvzXR1iYS+npiUpONmPDyXKElvR0t+LiGt65EBcXobFjE4OaJ6SLzJgxY/Tll19q06ZNvsvw4cM1ceJEbdq0SWFhYXZHBHCcpKQV2rTJf8/Lpk1DlZS0wqZEHVtWVobS0vr7jaWl9VdWVoZNiVqH5Qgt+fk31iszdZ9aCjaXZVlW0zcLHaNHj9a5556rp556qlm3r66uVnR0tKqqqtg7AwRRael6/etfX+iUU4ayJyYEFBZWqqioUklJscb8y78hLEdoyc4uVW7udqWmxgd8T0xzt98UGQAAEHKau/025gvx6nzwwQd2RwAAACEipM+RAQAAaAxFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGCvc7gDmKpBULClJUrLNWdqmoKBCxcW7lZQUq+TkWLvjtJpTlsMpFi36m8rKNsrtPl+/+tV1dsdpNae8rjyeEuXl7VBqarzGjk20O06rOWV9OEUovK4oMi1WISlTkueYsXRJWZLMelNVVBxQZua78nhKfWPp6YnKyspQbGyEfcFayCnL4RQffvi59u+/XnfcUeAbW7lyoKKiXtPIkWfbmKxlnPK6Ki6uVErKSyovP+gbi4uLUH7+jXK7Y+wL1kJOWR9OEUqvKw4ttVimpJzjxnIkTbAhS9tkZr6rnJwyv7GcnDJNmPCOTYlaxynL4RT791+vtLRCv7G0tEJVV19rU6LWccrr6viNjSSVlx/UiBEv2pSodZyyPpwilF5XFJkWKVDtnhjvcePeo+OF9e4RqgoKKuTxlMrrtfzGvV5LHk+pCgsrbUrWMk5ZDqdYtOhvGjeuQOHh/usjPNzSuHEFeuaZV21K1jJOeV15PCX1NjZ1yssPKju7NLiBWskp68MpQu11RZFpkeIm5ouCkiIQiot3NzpfVGTGHwanLIdTlJVtbHS+pOSzICVpG6e8rvLydjQ6n5u7PUhJ2sYp68MpQu11RZFpkQFNzCcFJUUgDBgQ0+h8UpIZ5/s4ZTmcon//YY3Ou93nBylJ2zjldZWS0rfR+dTU+CAlaRunrA+nCLXXFUWmRQaq9sTesOPGw46Om/PppYEDeyg9PVFhYS6/8bAwl9LTE435NIBTlsMp7rjj51q5cqCOHPFfH0eOuLRy5UBjPr3klNdVerpbcXENnwgbFxdhzKeXnLI+nCLUXlcUmRbLkpR23Fja0XGzZGVlKC2tv99YWlp/ZWVl2JSodZyyHE4RFfWacnL8S31OTrKiol6zKVHrOOV1lZ9/Y72NTt2nS0zilPXhFKH0unJZlmU1fTNzVVdXKzo6WlVVVYqKigrgIxeq9pwY879HprCwUkVFlcZ/L4NTlsMpnnnmVZWUfGb898g45XWVnV2q3Nztxn+PjFPWh1O05+uqudtvigwAAAg5zd1+c2gJAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjhXSRmTdvnkaMGKHIyEj16tVLV111lTZv3mx3LAcqkLRCUqHdQdqI5QglH320Si+/PF9r1662O0obOWN9OIXHU6I5cz5Rdnap3VEQIsLtDtCYNWvWaPLkyRoxYoSOHDmi+++/X5dccom+/vprde/e3e54DlAhKVOS55ixdElZkmJtSdQ6LEcoKSnZoi1bMjRmzDe+sVWrBisp6T31759oX7AWc8b6cIri4kqlpLyk8vKDvrG4uAjl598otzvGvmCwncuyLMvuEM31r3/9S7169dKaNWt00UUXNes+1dXVio6OVlVVlaKioto5oWnGScqR5D1mLExSmqSVtiRqHZYjlKxadaYuvvhbhYf/+0/LkSMurVkzSGPGfG1jspZyxvpwip49/+hXYurExUXohx+m2JAI7a252++QPrR0vKqqKklSjx49TnibQ4cOqbq62u+ChhSo9l+a3uPGvUfHTdmNznKEko8+WqUxY77xKzGSFB5uacyYbww6zOSM9eEUHk9JgyVGksrLD3KYqYMzpsjU1NRo+vTpGjVqlM4666wT3m7evHmKjo72XRISEoKY0iTFTcwXBSVF27EcoWTbtk2NzpeVbQhOkDZzxvpwiry8HY3O5+ZuD1IShCJjiszkyZP11Vdfafny5Y3ebtasWaqqqvJdtm3bFqSEphnQxHxSUFK0HcsRShISzm10vn//84ITpM2csT6cIiWlb6PzqanxQUqCUGREkZkyZYreeecdrV69Wqeddlqjt+3SpYuioqL8LmjIQNWeuBh23HjY0fHkoCdqHZYjlPz4x2O0atVgHTni8hs/csSlVasGa9Son9iUrKWcsT6cIj3drbi4iAbn4uIiNHZsYnADIaSEdJGxLEtTpkzRG2+8offff19ut9vuSA6TpdoTF4+VdnTcJCxHKElKek9r1gzyG1uzZpCSkt6zKVFrOWN9OEV+/o31ykzdp5bQsYX0p5buvPNOvfzyy3rrrbd0xhln+Majo6PVtWvXZj0Gn1pqjkLVHvNPktn/0mQ5QsnatatVVrZB/fufZ9CemIY4Y304RXZ2qXJztys1NZ49MQ7X3O13SBcZl8vV4PiSJUs0adKkZj0GRQYAAPM0d/sd0l+IF8IdCwAAhICQPkcGAACgMRQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIpMqyWo9n9fP7uDBMBISd0l/cjuIG10taTekq61O0gbOWU5/iLpF5KW2pyjrQokrZBUaHcQAA2gyLTYTZJckv4pyZK07ej12+0M1UpzVJs9V9J+SWuPXn/UzlCtsEi1ud+UtEvS349e/4uNmVrDKcvxmaTOkv6fpBcl3Xz0+iYbM7VGhaRxks6QdJmkgUevV9oZCsBxXJZlWXaHaE/V1dWKjo5WVVWVoqKiAvCIrkbmTPtf6ZRlYTlCS2dJhxsY7yTp/4KcpS3GScqR5D1mLExSmqSVtiQCOpLmbr/ZI9MiCU3Mm3SYaWQT86YcZrq6iXlTDs84ZTn+ooZLjI6OLw1elDYpkOSRf4nR0esecZgJCB0UmRb5ron5fwYlRWB83sT8xqCkaLtPmpj/KCgp2s4py/FBE/OrghEiAIqbmC8KSgoATaPItMipTcyfFpQUgXFOE/PDgpKi7Zras/TjoKRoO6csx+gm5scEI0QADGhiPikoKQA0jXNkWswp5zFIzlkWliO0cI4MgLbjHJl2c1sLx0PZvBaOh6rnWjgeqpyyHOtUW1qO1enouEmyVFtajpV2dBxAqGCPTKv1U+05MadJ2hrAx7XDj1R7TswwSR/bnKUtrlXtuSQ/lvSazVnawinLsVS158SMkTTJ1iRtU6jac2KSJCXbnAXoOJq7/abIAACAkMOhJQAA4HgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgrHC7A7S3ul9gqK6utjkJAABorrrtdlO/pOT4IrNnzx5JUkJCgs1JAABAS+3Zs0fR0dEnnHf8j0bW1NRo+/btioyMlMvlsjtOSKqurlZCQoK2bdvGD2uGANZHaGF9hBbWR2hpz/VhWZb27Nmj+Ph4nXTSic+EcfwemZNOOkmnnXaa3TGMEBUVxR+GEML6CC2sj9DC+ggt7bU+GtsTU4eTfQEAgLEoMgAAwFgUGahLly76r//6L3Xp0sXuKBDrI9SwPkIL6yO0hML6cPzJvgAAwLnYIwMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMh3UvHnzNGLECEVGRqpXr1666qqrtHnzZrtj4ahHH31ULpdL06dPtztKh/bdd9/pxhtvVFxcnLp27aqzzz5b69evtztWh+T1ejV79my53W517dpVAwYM0EMPPdTk7/AgMD788ENdfvnlio+Pl8vl0ptvvuk3b1mWHnjgAfXt21ddu3ZVWlqaCgsLg5KNItNBrVmzRpMnT9ann36q7OxsHT58WJdccon27dtnd7QOLz8/X88884yGDh1qd5QOrbKyUqNGjVKnTp20YsUKff3115o/f75iY2PtjtYhPfbYY1q0aJH++Mc/6ptvvtFjjz2mxx9/XH/4wx/sjtYh7Nu3T+ecc44WLlzY4Pzjjz+uBQsW6M9//rPy8vLUvXt3paen6+DBg+2ejY9fQ5L0r3/9S7169dKaNWt00UUX2R2nw9q7d6/OO+88/elPf9LcuXN17rnn6qmnnrI7Voc0c+ZMrV27Vh999JHdUSApIyNDvXv31l/+8hff2M9+9jN17dpVL774oo3JOh6Xy6U33nhDV111laTavTHx8fG65557dO+990qSqqqq1Lt3by1dulQ33HBDu+Zhjwwk1b7oJKlHjx42J+nYJk+erPHjxystLc3uKB3e22+/reHDh+u6665Tr169NGzYMD377LN2x+qwRo4cqVWrVqmgoECS9Pnnn+vjjz/WpZdeanMylJSUaOfOnX5/t6Kjo5WSkqLc3Nx2f37H/2gkmlZTU6Pp06dr1KhROuuss+yO02EtX75cGzZsUH5+vt1RIGnLli1atGiRZsyYofvvv1/5+fmaOnWqOnfurJtuusnueB3OzJkzVV1drUGDBiksLExer1cPP/ywJk6caHe0Dm/nzp2SpN69e/uN9+7d2zfXnigy0OTJk/XVV1/p448/tjtKh7Vt2zZNmzZN2dnZioiIsDsOVFvwhw8frkceeUSSNGzYMH311Vf685//TJGxwSuvvKKXXnpJL7/8soYMGaJNmzZp+vTpio+PZ310cBxa6uCmTJmid955R6tXr9Zpp51md5wO67PPPtOuXbt03nnnKTw8XOHh4VqzZo0WLFig8PBweb1euyN2OH379tWZZ57pNzZ48GBt3brVpkQd269//WvNnDlTN9xwg84++2z94he/0N1336158+bZHa3D69OnjyTp+++/9xv//vvvfXPtiSLTQVmWpSlTpuiNN97Q+++/L7fbbXekDm3MmDH68ssvtWnTJt9l+PDhmjhxojZt2qSwsDC7I3Y4o0aNqveVBAUFBerfv79NiTq2/fv366ST/DdZYWFhqqmpsSkR6rjdbvXp00erVq3yjVVXVysvL0+pqant/vwcWuqgJk+erJdffllvvfWWIiMjfccxo6Oj1bVrV5vTdTyRkZH1zk/q3r274uLiOG/JJnfffbdGjhypRx55RNdff73WrVunxYsXa/HixXZH65Auv/xyPfzww+rXr5+GDBmijRs36sknn9Qtt9xid7QOYe/evSoqKvJdLykp0aZNm9SjRw/169dP06dP19y5c5WcnCy3263Zs2crPj7e98mmdmWhQ5LU4GXJkiV2R8NRF198sTVt2jS7Y3Ro//M//2OdddZZVpcuXaxBgwZZixcvtjtSh1VdXW1NmzbN6tevnxUREWGdfvrp1n/+539ahw4dsjtah7B69eoGtxk33XSTZVmWVVNTY82ePdvq3bu31aVLF2vMmDHW5s2bg5KN75EBAADG4hwZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAIcXlcunNN9+0OwYAQ1BkAATVzp07ddddd+n0009Xly5dlJCQoMsvv9zvB+cAoLn40UgAQVNaWqpRo0YpJiZGv/vd73T22Wfr8OHD8ng8mjx5sr799lu7IwIwDHtkAATNnXfeKZfLpXXr1ulnP/uZBg4cqCFDhmjGjBn69NNP693+gw8+kMvl0u7du31jmzZtksvlUmlpqSRp6dKliomJ0TvvvKMzzjhD3bp107XXXqv9+/dr2bJlSkxMVGxsrKZOnSqv1+t7nMTERD300EOaMGGCunfvrlNPPVULFy5s7/8FAAKMIgMgKCoqKrRy5UpNnjxZ3bt3rzcfExPT6sfev3+/FixYoOXLl2vlypX64IMPdPXVV+u9997Te++9pxdeeEHPPPOMXnvtNb/7/e53v9M555yjjRs3aubMmZo2bZqys7NbnQNA8HFoCUBQFBUVybIsDRo0KOCPffjwYS1atEgDBgyQJF177bV64YUX9P333+vkk0/WmWeeqZ/85CdavXq1fv7zn/vuN2rUKM2cOVOSNHDgQK1du1a///3vNXbs2IBnBNA+2CMDICgsy2q3x+7WrZuvxEhS7969lZiYqJNPPtlvbNeuXX73S01NrXf9m2++abecAAKPIgMgKJKTk+VyuVp0Qu9JJ9X+iTq2BB0+fLje7Tp16uR33eVyNThWU1PTksgADECRARAUPXr0UHp6uhYuXKh9+/bVmz/2hN46p5xyiiRpx44dvrFNmzYFLNPxJxh/+umnGjx4cMAeH0D7o8gACJqFCxfK6/Xqggsu0N///ncVFhbqm2++0YIFC+od5pGkpKQkJSQk6L//+79VWFiod999V/Pnzw9YnrVr1+rxxx9XQUGBFi5cqFdffVXTpk0L2OMDaH8UGQBBc/rpp2vDhg36yU9+onvuuUdnnXWWxo4dq1WrVmnRokX1bt+pUydlZWXp22+/1dChQ/XYY49p7ty5Actzzz33aP369Ro2bJjmzp2rJ598Uunp6QF7fADtz2W15xl4ABCiEhMTNX36dE2fPt3uKADagD0yAADAWBQZAABgLA4tAQAAY7FHBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAw1v8HllQG/SRRQtUAAAAASUVORK5CYII=",
+ "text/plain": [
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The SVM algorithm offers a choice of kernel functions for performing its processing. Basically, mapping data into a higher dimensional space is called kernelling. The mathematical function used for the transformation is known as the kernel function, and can be of different types, such as:\n",
+ "\n",
+ " 1.Linear\n",
+ " 2.Polynomial\n",
+ " 3.Radial basis function (RBF)\n",
+ " 4.Sigmoid\n",
+ "Each of these functions has its characteristics, its pros and cons, and its equation, but as there's no easy way of knowing which function performs best with any given dataset. We usually choose different functions in turn and compare the results. Let's just use the default, RBF (Radial Basis Function) for this lab.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
SVC()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\n",
+ "\n",
+ "\n",
+ "# SVM (Support Vector Machines)\n",
+ "\n",
+ "\n",
+ "Estimated time needed: **15** minutes\n",
+ " \n",
+ "\n",
+ "## Objectives\n",
+ "\n",
+ "After completing this lab you will be able to:\n",
+ "\n",
+ "* Use scikit-learn to Support Vector Machine to classify\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this notebook, you will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.\n",
+ "\n",
+ "SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "The example is based on a dataset that is publicly available from the UCI Machine Learning Repository (Asuncion and Newman, 2007)[http://mlearn.ics.uci.edu/MLRepository.html]. The dataset consists of several hundred human cell sample records, each of which contains the values of a set of cell characteristics. The fields in each record are:\n",
+ "\n",
+ "|Field name|Description|\n",
+ "|--- |--- |\n",
+ "|ID|Clump thickness|\n",
+ "|Clump|Clump thickness|\n",
+ "|UnifSize|Uniformity of cell size|\n",
+ "|UnifShape|Uniformity of cell shape|\n",
+ "|MargAdh|Marginal adhesion|\n",
+ "|SingEpiSize|Single epithelial cell size|\n",
+ "|BareNuc|Bare nuclei|\n",
+ "|BlandChrom|Bland chromatin|\n",
+ "|NormNucl|Normal nucleoli|\n",
+ "|Mit|Mitoses|\n",
+ "|Class|Benign or malignant|\n",
+ "\n",
+ " \n",
+ " \n",
+ "\n",
+ "For the purposes of this example, we're using a dataset that has a relatively small number of predictors in each record. To download the data, we will use `!wget` to download it from IBM Object Storage. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2025-11-19 04:37:48-- https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv\n",
+ "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
+ "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
+ "200 OKequest sent, awaiting response... \n",
+ "Length: 19975 (20K) [text/csv]\n",
+ "Saving to: ‘cell_samples.csv’\n",
+ "\n",
+ "cell_samples.csv 100%[===================>] 19.51K --.-KB/s in 0s \n",
+ "\n",
+ "2025-11-19 04:37:48 (73.0 MB/s) - ‘cell_samples.csv’ saved [19975/19975]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Click here and press Shift+Enter\n",
+ "!wget -O cell_samples.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load Data From CSV File \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
ID
\n",
+ "
Clump
\n",
+ "
UnifSize
\n",
+ "
UnifShape
\n",
+ "
MargAdh
\n",
+ "
SingEpiSize
\n",
+ "
BareNuc
\n",
+ "
BlandChrom
\n",
+ "
NormNucl
\n",
+ "
Mit
\n",
+ "
Class
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
1000025
\n",
+ "
5
\n",
+ "
1
\n",
+ "
1
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+ "
1
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+ "
2
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+ "
1
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+ "
3
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+ "
1
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+ "
1
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+ "
2
\n",
+ "
\n",
+ "
\n",
+ "
1
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+ "
1002945
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+ "
5
\n",
+ "
4
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+ "
4
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+ "
5
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+ "
7
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+ "
10
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+ "
3
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+ "
2
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+ "
1
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+ "
2
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+ "
\n",
+ "
\n",
+ "
2
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+ "
1015425
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+ "
3
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+ "
1
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+ "
1
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+ "
1
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+ "
2
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+ "
2
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+ "
3
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+ "
1
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+ "
1
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+ "
2
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+ "
\n",
+ "
\n",
+ "
3
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+ "
1016277
\n",
+ "
6
\n",
+ "
8
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+ "
8
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+ "
1
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+ "
3
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+ "
4
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+ "
3
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+ "
7
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+ "
1
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+ "
2
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+ "
\n",
+ "
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+ "
4
\n",
+ "
1017023
\n",
+ "
4
\n",
+ "
1
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+ "
1
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+ "
3
\n",
+ "
2
\n",
+ "
1
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+ "
3
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+ "
1
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+ "
1
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+ "
2
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+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID Clump UnifSize UnifShape MargAdh SingEpiSize BareNuc \\\n",
+ "0 1000025 5 1 1 1 2 1 \n",
+ "1 1002945 5 4 4 5 7 10 \n",
+ "2 1015425 3 1 1 1 2 2 \n",
+ "3 1016277 6 8 8 1 3 4 \n",
+ "4 1017023 4 1 1 3 2 1 \n",
+ "\n",
+ " BlandChrom NormNucl Mit Class \n",
+ "0 3 1 1 2 \n",
+ "1 3 2 1 2 \n",
+ "2 3 1 1 2 \n",
+ "3 3 7 1 2 \n",
+ "4 3 1 1 2 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cell_df = pd.read_csv(\"cell_samples.csv\")\n",
+ "cell_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The ID field contains the patient identifiers. The characteristics of the cell samples from each patient are contained in fields Clump to Mit. The values are graded from 1 to 10, with 1 being the closest to benign.\n",
+ "\n",
+ "The Class field contains the diagnosis, as confirmed by separate medical procedures, as to whether the samples are benign (value = 2) or malignant (value = 4).\n",
+ "\n",
+ "Let's look at the distribution of the classes based on Clump thickness and Uniformity of cell size:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The SVM algorithm offers a choice of kernel functions for performing its processing. Basically, mapping data into a higher dimensional space is called kernelling. The mathematical function used for the transformation is known as the kernel function, and can be of different types, such as:\n",
+ "\n",
+ " 1.Linear\n",
+ " 2.Polynomial\n",
+ " 3.Radial basis function (RBF)\n",
+ " 4.Sigmoid\n",
+ "Each of these functions has its characteristics, its pros and cons, and its equation, but as there's no easy way of knowing which function performs best with any given dataset. We usually choose different functions in turn and compare the results. Let's just use the default, RBF (Radial Basis Function) for this lab.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
SVC()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\n",
+ "\n",
+ "# Decision Trees\n",
+ "\n",
+ "Estimated time needed: **15** minutes\n",
+ "\n",
+ "## Objectives\n",
+ "\n",
+ "After completing this lab you will be able to:\n",
+ "\n",
+ "* Develop a classification model using Decision Tree Algorithm\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this lab exercise, you will learn a popular machine learning algorithm, Decision Trees. You will use this classification algorithm to build a model from the historical data of patients, and their response to different medications. Then you will use the trained decision tree to predict the class of an unknown patient, or to find a proper drug for a new patient.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ " Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y. \n",
+ " \n",
+ " \n",
+ " Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The features of this dataset are Age, Sex, Blood Pressure, and the Cholesterol of the patients, and the target is the drug that each patient responded to.\n",
+ " \n",
+ " \n",
+ " It is a sample of multiclass classifier, and you can use the training part of the dataset \n",
+ " to build a decision tree, and then use it to predict the class of an unknown patient, or to prescribe a drug to a new patient.\n",
+ "
\n",
+ "The train_test_split will need the parameters: \n",
+ "X, y, test_size=0.3, and random_state=3.
\n",
+ "The X and y are the arrays required before the split, the test_size represents the ratio of the testing dataset, and the random_state ensures that we obtain the same splits.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Practice
\n",
+ "Print the shape of X_trainset and y_trainset. Ensure that the dimensions match.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of X training set (140, 5) & Size of Y training set (140,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click here for the solution\n",
+ "\n",
+ "```python\n",
+ "print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))\n",
+ "\n",
+ "```\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Print the shape of X_testset and y_testset. Ensure that the dimensions match.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of X test set (60, 5) & Size of y test set (60,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Shape of X test set {}'.format(X_testset.shape),'&','Size of y test set {}'.format(y_testset.shape))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click here for the solution\n",
+ "\n",
+ "```python\n",
+ "print('Shape of X test set {}'.format(X_testset.shape),'&','Size of y test set {}'.format(y_testset.shape))\n",
+ "\n",
+ "```\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
Modeling
\n",
+ " We will first create an instance of the DecisionTreeClassifier called drugTree. \n",
+ " Inside of the classifier, specify criterion=\"entropy\" so we can see the information gain of each node.\n",
+ "
\n",
+ "\n",
+ "# K-Nearest Neighbors\n",
+ "\n",
+ "Estimated time needed: **25** minutes\n",
+ "\n",
+ "## Objectives\n",
+ "\n",
+ "After completing this lab you will be able to:\n",
+ "\n",
+ "* Use K Nearest neighbors to classify data\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this Lab you will load a customer dataset, fit the data, and use K-Nearest Neighbors to predict a data point. But what is **K-Nearest Neighbors**?\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**K-Nearest Neighbors** is a supervised learning algorithm. Where the data is 'trained' with data points corresponding to their classification. To predict the class of a given data point, it takes into account the classes of the 'K' nearest data points and chooses the class in which the majority of the 'K' nearest data points belong to as the predicted class.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Here's an visualization of the K-Nearest Neighbors algorithm.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, we have data points of Class A and B. We want to predict what the star (test data point) is. If we consider a k value of 3 (3 nearest data points), we will obtain a prediction of Class B. Yet if we consider a k value of 6, we will obtain a prediction of Class A.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this sense, it is important to consider the value of k. Hopefully from this diagram, you should get a sense of what the K-Nearest Neighbors algorithm is. It considers the 'K' Nearest Neighbors (data points) when it predicts the classification of the test point.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Imagine a telecommunications provider has segmented its customer base by service usage patterns, categorizing the customers into four groups. If demographic data can be used to predict group membership, the company can customize offers for individual prospective customers. It is a classification problem. That is, given the dataset, with predefined labels, we need to build a model to be used to predict class of a new or unknown case.\n",
+ "\n",
+ "The example focuses on using demographic data, such as region, age, and marital, to predict usage patterns.\n",
+ "\n",
+ "The target field, called **custcat**, has four possible values that correspond to the four customer groups, as follows:\n",
+ "1- Basic Service\n",
+ "2- E-Service\n",
+ "3- Plus Service\n",
+ "4- Total Service\n",
+ "\n",
+ "Our objective is to build a classifier, to predict the class of unknown cases. We will use a specific type of classification called K nearest neighbour.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load Data \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's read the data using pandas library and print the first five rows.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
![\"Skills](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\")
\n",
+ " \n",
+ "![](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/images/KNN_Diagram.png\")
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, we have data points of Class A and B. We want to predict what the star (test data point) is. If we consider a k value of 3 (3 nearest data points), we will obtain a prediction of Class B. Yet if we consider a k value of 6, we will obtain a prediction of Class A.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this sense, it is important to consider the value of k. Hopefully from this diagram, you should get a sense of what the K-Nearest Neighbors algorithm is. It considers the 'K' Nearest Neighbors (data points) when it predicts the classification of the test point.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "