cancer-diagnosis-anaylsis.ipynb

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   "source": [
    "**This notebook is an exercise in the [Data Visualization](https://www.kaggle.com/learn/data-visualization) course.  You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/distributions).**\n",
    "\n",
    "---\n"
   ]
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   "source": [
    "In this exercise, you will use your new knowledge to propose a solution to a real-world scenario.  To succeed, you will need to import data into Python, answer questions using the data, and generate **histograms** and **density plots** to understand patterns in the data.\n",
    "\n",
    "## Scenario\n",
    "\n",
    "You'll work with a real-world dataset containing information collected from microscopic images of breast cancer tumors, similar to the image below.\n",
    "\n",
    "![ex4_cancer_image](https://storage.googleapis.com/kaggle-media/learn/images/qUESsJe.png)\n",
    "\n",
    "Each tumor has been labeled as either [**benign**](https://en.wikipedia.org/wiki/Benign_tumor) (_noncancerous_) or **malignant** (_cancerous_).\n",
    "\n",
    "To learn more about how this kind of data is used to create intelligent algorithms to classify tumors in medical settings, **watch the short video [at this link](https://www.youtube.com/watch?v=9Mz84cwVmS0)**."
   ]
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   "source": [
    "\n",
    "\n",
    "## Setup\n",
    "\n",
    "Run the next cell to import and configure the Python libraries that you need to complete the exercise."
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
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   "source": [
    "The questions below will give you feedback on your work. Run the following cell to set up our feedback system."
   ]
  },
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    {
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     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "# Set up code checking\n",
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.data_viz_to_coder.ex5 import *\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
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   "source": [
    "## Step 1: Load the data\n",
    "\n",
    "In this step, you will load the data.\n",
    "- Load the data file into a DataFrame called `cancer_data`.  \n",
    "- The corresponding filepath is `cancer_filepath`.  \n",
    "- Use the `\"Id\"` column to label the rows."
   ]
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    {
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       "<span style=\"color:#33cc33\">Correct</span>"
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       "Correct"
      ]
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   "source": [
    "# Path of the files to read\n",
    "cancer_filepath = \"../input/cancer.csv\"\n",
    "\n",
    "# Fill in the line below to read the file into a variable cancer_data\n",
    "cancer_data = pd.read_csv(cancer_filepath,index_col='Id')\n",
    "\n",
    "# Run the line below with no changes to check that you've loaded the data correctly\n",
    "step_1.check()"
   ]
  },
  {
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   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#step_1.hint()\n",
    "#step_1.solution()"
   ]
  },
  {
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   "source": [
    "## Step 2: Review the data\n",
    "\n",
    "Use a Python command to print the first 5 rows of the data."
   ]
  },
  {
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       "      <th></th>\n",
       "      <th>Diagnosis</th>\n",
       "      <th>Radius (mean)</th>\n",
       "      <th>Texture (mean)</th>\n",
       "      <th>Perimeter (mean)</th>\n",
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       "      <td>96.09</td>\n",
       "      <td>630.5</td>\n",
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      "text/plain": [
       "         Diagnosis  Radius (mean)  Texture (mean)  Perimeter (mean)  \\\n",
       "Id                                                                    \n",
       "8510426          B         13.540           14.36             87.46   \n",
       "8510653          B         13.080           15.71             85.63   \n",
       "8510824          B          9.504           12.44             60.34   \n",
       "854941           B         13.030           18.42             82.61   \n",
       "85713702         B          8.196           16.84             51.71   \n",
       "\n",
       "          Area (mean)  Smoothness (mean)  Compactness (mean)  \\\n",
       "Id                                                             \n",
       "8510426         566.3            0.09779             0.08129   \n",
       "8510653         520.0            0.10750             0.12700   \n",
       "8510824         273.9            0.10240             0.06492   \n",
       "854941          523.8            0.08983             0.03766   \n",
       "85713702        201.9            0.08600             0.05943   \n",
       "\n",
       "          Concavity (mean)  Concave points (mean)  Symmetry (mean)  ...  \\\n",
       "Id                                                                  ...   \n",
       "8510426            0.06664               0.047810           0.1885  ...   \n",
       "8510653            0.04568               0.031100           0.1967  ...   \n",
       "8510824            0.02956               0.020760           0.1815  ...   \n",
       "854941             0.02562               0.029230           0.1467  ...   \n",
       "85713702           0.01588               0.005917           0.1769  ...   \n",
       "\n",
       "          Radius (worst)  Texture (worst)  Perimeter (worst)  Area (worst)  \\\n",
       "Id                                                                           \n",
       "8510426           15.110            19.26              99.70         711.2   \n",
       "8510653           14.500            20.49              96.09         630.5   \n",
       "8510824           10.230            15.66              65.13         314.9   \n",
       "854941            13.300            22.81              84.46         545.9   \n",
       "85713702           8.964            21.96              57.26         242.2   \n",
       "\n",
       "          Smoothness (worst)  Compactness (worst)  Concavity (worst)  \\\n",
       "Id                                                                     \n",
       "8510426              0.14400              0.17730            0.23900   \n",
       "8510653              0.13120              0.27760            0.18900   \n",
       "8510824              0.13240              0.11480            0.08867   \n",
       "854941               0.09701              0.04619            0.04833   \n",
       "85713702             0.12970              0.13570            0.06880   \n",
       "\n",
       "          Concave points (worst)  Symmetry (worst)  Fractal dimension (worst)  \n",
       "Id                                                                             \n",
       "8510426                  0.12880            0.2977                    0.07259  \n",
       "8510653                  0.07283            0.3184                    0.08183  \n",
       "8510824                  0.06227            0.2450                    0.07773  \n",
       "854941                   0.05013            0.1987                    0.06169  \n",
       "85713702                 0.02564            0.3105                    0.07409  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first five rows of the data\n",
    "cancer_data.head()"
   ]
  },
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   "source": [
    "In the dataset, each row corresponds to a different image.  The dataset has 31 different columns, corresponding to:\n",
    "- 1 column (`'Diagnosis'`) that classifies tumors as either benign (which appears in the dataset as **`B`**) or malignant (__`M`__), and\n",
    "- 30 columns containing different measurements collected from the images.\n",
    "\n",
    "Use the first 5 rows of the data to answer the questions below."
   ]
  },
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    "papermill": {
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     "exception": false,
     "start_time": "2024-06-16T23:35:32.443285",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.5, \"interactionType\": 1, \"questionType\": 1, \"questionId\": \"2_ReviewData\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
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       "<IPython.core.display.Javascript object>"
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    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fill in the line below: In the first five rows of the data, what is the\n",
    "# largest value for 'Perimeter (mean)'?\n",
    "max_perim = 87.46\n",
    "\n",
    "# Fill in the line below: What is the value for 'Radius (mean)' for the tumor with Id 8510824?\n",
    "mean_radius = 9.504\n",
    "\n",
    "# Check your answers\n",
    "step_2.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c2a32a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-16T23:35:32.487063Z",
     "iopub.status.busy": "2024-06-16T23:35:32.486287Z",
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    },
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     "duration": 0.017953,
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     "exception": false,
     "start_time": "2024-06-16T23:35:32.475413",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#step_2.hint()\n",
    "#step_2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d73f263",
   "metadata": {
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     "duration": 0.009417,
     "end_time": "2024-06-16T23:35:32.513223",
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     "start_time": "2024-06-16T23:35:32.503806",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 3: Investigating differences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd470113",
   "metadata": {
    "papermill": {
     "duration": 0.009538,
     "end_time": "2024-06-16T23:35:32.532567",
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     "start_time": "2024-06-16T23:35:32.523029",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Part A\n",
    "\n",
    "Use the code cell below to create two histograms that show the distribution in values for `'Area (mean)'`, separately for both benign and malignant tumors.  (_To permit easy comparison, create a single figure containing both histograms in the code cell below._)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0531a840",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-16T23:35:32.553842Z",
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     "duration": 0.608782,
     "end_time": "2024-06-16T23:35:33.151148",
     "exception": false,
     "start_time": "2024-06-16T23:35:32.542366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
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     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograms for benign and maligant tumors\n",
    "sns.histplot(cancer_data, x='Area (mean)', hue='Diagnosis')\n",
    "# Check your answer\n",
    "step_3.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d3779f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-16T23:35:33.174505Z",
     "iopub.status.busy": "2024-06-16T23:35:33.174090Z",
     "iopub.status.idle": "2024-06-16T23:35:33.179467Z",
     "shell.execute_reply": "2024-06-16T23:35:33.178191Z"
    },
    "papermill": {
     "duration": 0.020262,
     "end_time": "2024-06-16T23:35:33.182165",
     "exception": false,
     "start_time": "2024-06-16T23:35:33.161903",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#step_3.a.hint()\n",
    "#step_3.a.solution_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d312c1",
   "metadata": {
    "papermill": {
     "duration": 0.011075,
     "end_time": "2024-06-16T23:35:33.203952",
     "exception": false,
     "start_time": "2024-06-16T23:35:33.192877",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Part B\n",
    "\n",
    "A researcher approaches you for help with identifying how the `'Area (mean)'` column can be used to understand the difference between benign and malignant tumors.  Based on the histograms above, \n",
    "- Do malignant tumors have higher or lower values for `'Area (mean)'` (relative to benign tumors), on average?\n",
    "- Which tumor type seems to have a larger range of potential values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9ad2785",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-16T23:35:33.227181Z",
     "iopub.status.busy": "2024-06-16T23:35:33.226742Z",
     "iopub.status.idle": "2024-06-16T23:35:33.232050Z",
     "shell.execute_reply": "2024-06-16T23:35:33.230916Z"
    },
    "papermill": {
     "duration": 0.019829,
     "end_time": "2024-06-16T23:35:33.234516",
     "exception": false,
     "start_time": "2024-06-16T23:35:33.214687",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#step_3.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ccbdb6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-16T23:35:33.257826Z",
     "iopub.status.busy": "2024-06-16T23:35:33.257407Z",
     "iopub.status.idle": "2024-06-16T23:35:33.267318Z",
     "shell.execute_reply": "2024-06-16T23:35:33.266136Z"
    },
    "papermill": {
     "duration": 0.024564,
     "end_time": "2024-06-16T23:35:33.269900",
     "exception": false,
     "start_time": "2024-06-16T23:35:33.245336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 4, \"questionId\": \"3.2_ThinkHist\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc99\">Solution:</span> Malignant tumors have higher values for `'Area (mean)'`, on average. Malignant tumors have a larger range of potential values."
      ],
      "text/plain": [
       "Solution: Malignant tumors have higher values for `'Area (mean)'`, on average. Malignant tumors have a larger range of potential values."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check your answer (Run this code cell to receive credit!)\n",
    "step_3.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b28d97",
   "metadata": {
    "papermill": {
     "duration": 0.011051,
     "end_time": "2024-06-16T23:35:33.291922",
     "exception": false,
     "start_time": "2024-06-16T23:35:33.280871",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 4: A very useful column\n",
    "\n",
    "#### Part A\n",
    "\n",
    "Use the code cell below to create two KDE plots that show the distribution in values for `'Radius (worst)'`, separately for both benign and malignant tumors.  (_To permit easy comparison, create a single figure containing both KDE plots in the code cell below._)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e960455",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-16T23:35:33.316862Z",
     "iopub.status.busy": "2024-06-16T23:35:33.315843Z",
     "iopub.status.idle": "2024-06-16T23:35:33.724231Z",
     "shell.execute_reply": "2024-06-16T23:35:33.722998Z"
    },
    "papermill": {
     "duration": 0.4237,
     "end_time": "2024-06-16T23:35:33.726846",
     "exception": false,
     "start_time": "2024-06-16T23:35:33.303146",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.5, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"4.1_PlotThreshold\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KDE plots for benign and malignant tumors\n",
    "sns.kdeplot(cancer_data, x='Radius (worst)', hue='Diagnosis', fill=True)\n",
    "# Check your answer\n",
    "step_4.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "074a443e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-16T23:35:33.752925Z",
     "iopub.status.busy": "2024-06-16T23:35:33.752522Z",
     "iopub.status.idle": "2024-06-16T23:35:33.757454Z",
     "shell.execute_reply": "2024-06-16T23:35:33.756158Z"
    },
    "papermill": {
     "duration": 0.020912,
     "end_time": "2024-06-16T23:35:33.759907",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#step_4.a.hint()\n",
    "#step_4.a.solution_plot()"
   ]
  },
  {
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   "source": [
    "#### Part B\n",
    "\n",
    "A hospital has recently started using an algorithm that can diagnose tumors with high accuracy.  Given a tumor with a value for `'Radius (worst)'` of 25, do you think the algorithm is more likely to classify the tumor as benign or malignant?"
   ]
  },
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   "execution_count": 14,
   "id": "4d45c078",
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   "outputs": [],
   "source": [
    "#step_4.b.hint()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
   "id": "0e2790c8",
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    "execution": {
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   "outputs": [
    {
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       "<span style=\"color:#33cc99\">Solution:</span> The algorithm is more likely to classify the tumor as malignant. This is because the curve for malignant tumors is much higher than the curve for benign tumors around a value of 25 -- and an algorithm that gets high accuracy is likely to make decisions based on this pattern in the data."
      ],
      "text/plain": [
       "Solution: The algorithm is more likely to classify the tumor as malignant. This is because the curve for malignant tumors is much higher than the curve for benign tumors around a value of 25 -- and an algorithm that gets high accuracy is likely to make decisions based on this pattern in the data."
      ]
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   "source": [
    "# Check your answer (Run this code cell to receive credit!)\n",
    "step_4.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50d5fa4b",
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    "tags": []
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   "source": [
    "## Keep going\n",
    "\n",
    "Review all that you've learned and explore how to further customize your plots in the **[next tutorial](https://www.kaggle.com/alexisbcook/choosing-plot-types-and-custom-styles)**!"
   ]
  },
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   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/data-visualization/discussion) to chat with other learners.*"
   ]
  }
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