Fouquier12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b09f708",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn import linear_model\n",
    "from scipy.special import expit\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91a5fa7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the file, “csv.csv”, and create a dataframe.\n",
    "df = pd.read_csv('heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b49fa876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trtbps', 'chol', 'fbs', 'restecg', 'thalachh',\n",
       "       'exng', 'oldpeak', 'slp', 'caa', 'thall', 'output'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "facd4b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['output']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c41f124b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['age', 'sex', 'cp', 'trtbps', 'chol', 'fbs', 'restecg', 'thalachh',\n",
    "       'exng', 'oldpeak', 'slp', 'caa', 'thall']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "19644423",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Follow the Column Key table for guidance on features and target.\n",
    "# Need to split the data into training set and test set. Hint: use train_test_split with test_size of .3 and random_state=25\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "407e1a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea2fbbfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2756"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many samples are you using for training and for testing?\n",
    "X_train.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "94684c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=5000)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the model object using LogisticRegression.\n",
    "lrm = linear_model.LogisticRegression(max_iter=5000)\n",
    "lrm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0ff15539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=5000)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the classifier. If the learning doesn’t converge, add max_iter = 500 as an argument when creating the model in step 5.\n",
    "lrm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "573dd04f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00555492, -0.9550283 ,  0.94277611, -0.00973355, -0.00270063,\n",
       "        -0.05813984,  0.16980979,  0.02199382, -0.60722896, -0.55263209,\n",
       "         0.42905335, -0.80762212, -1.32383434]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the model’s coefficients and intercept?\n",
    "lrm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "189dcd36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.90020824])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1e364236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions using the test dataset.\n",
    "predictions = lrm.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b03c480e",
   "metadata": {},
   "outputs": [
    {
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       "<p>91 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "     age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
       "281   52    1   0     128   204    1        1       156     1      1.0    1   \n",
       "81    45    1   1     128   308    0        0       170     0      0.0    2   \n",
       "40    51    0   2     140   308    0        0       142     0      1.5    2   \n",
       "175   40    1   0     110   167    0        0       114     1      2.0    1   \n",
       "58    34    1   3     118   182    0        0       174     0      0.0    2   \n",
       "..   ...  ...  ..     ...   ...  ...      ...       ...   ...      ...  ...   \n",
       "114   55    1   1     130   262    0        1       155     0      0.0    2   \n",
       "180   55    1   0     132   353    0        1       132     1      1.2    1   \n",
       "200   44    1   0     110   197    0        0       177     0      0.0    2   \n",
       "30    41    0   1     105   198    0        1       168     0      0.0    2   \n",
       "117   56    1   3     120   193    0        0       162     0      1.9    1   \n",
       "\n",
       "     caa  thall  \n",
       "281    0      0  \n",
       "81     0      2  \n",
       "40     1      2  \n",
       "175    0      3  \n",
       "58     0      2  \n",
       "..   ...    ...  \n",
       "114    0      2  \n",
       "180    1      3  \n",
       "200    1      2  \n",
       "30     1      2  \n",
       "117    0      3  \n",
       "\n",
       "[91 rows x 13 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe with the test data, predicted results, and the actual values for heart disease.\n",
    "results = X_test\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "09997f94",
   "metadata": {},
   "outputs": [
    {
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       "      <td>353</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <th>88</th>\n",
       "      <td>44</td>\n",
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       "      <td>0</td>\n",
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       "      <td>105</td>\n",
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       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
       "0    52    1   0     128   204    1        1       156     1      1.0    1   \n",
       "1    45    1   1     128   308    0        0       170     0      0.0    2   \n",
       "2    51    0   2     140   308    0        0       142     0      1.5    2   \n",
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       "4    34    1   3     118   182    0        0       174     0      0.0    2   \n",
       "..  ...  ...  ..     ...   ...  ...      ...       ...   ...      ...  ...   \n",
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       "90   56    1   3     120   193    0        0       162     0      1.9    1   \n",
       "\n",
       "    caa  thall  \n",
       "0     0      0  \n",
       "1     0      2  \n",
       "2     1      2  \n",
       "3     0      3  \n",
       "4     0      2  \n",
       "..  ...    ...  \n",
       "86    0      2  \n",
       "87    1      3  \n",
       "88    1      2  \n",
       "89    1      2  \n",
       "90    0      3  \n",
       "\n",
       "[91 rows x 13 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = results.reset_index(drop=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f3d9facd",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
       "0    52    1   0     128   204    1        1       156     1      1.0    1   \n",
       "1    45    1   1     128   308    0        0       170     0      0.0    2   \n",
       "2    51    0   2     140   308    0        0       142     0      1.5    2   \n",
       "3    40    1   0     110   167    0        0       114     1      2.0    1   \n",
       "4    34    1   3     118   182    0        0       174     0      0.0    2   \n",
       "..  ...  ...  ..     ...   ...  ...      ...       ...   ...      ...  ...   \n",
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       "90   56    1   3     120   193    0        0       162     0      1.9    1   \n",
       "\n",
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     "execution_count": 91,
     "metadata": {},
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   "source": [
    "results['prediction'] = predictions\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0138c9b3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>198</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>120</td>\n",
       "      <td>193</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
       "0    52    1   0     128   204    1        1       156     1      1.0    1   \n",
       "1    45    1   1     128   308    0        0       170     0      0.0    2   \n",
       "2    51    0   2     140   308    0        0       142     0      1.5    2   \n",
       "3    40    1   0     110   167    0        0       114     1      2.0    1   \n",
       "4    34    1   3     118   182    0        0       174     0      0.0    2   \n",
       "..  ...  ...  ..     ...   ...  ...      ...       ...   ...      ...  ...   \n",
       "86   55    1   1     130   262    0        1       155     0      0.0    2   \n",
       "87   55    1   0     132   353    0        1       132     1      1.2    1   \n",
       "88   44    1   0     110   197    0        0       177     0      0.0    2   \n",
       "89   41    0   1     105   198    0        1       168     0      0.0    2   \n",
       "90   56    1   3     120   193    0        0       162     0      1.9    1   \n",
       "\n",
       "    caa  thall  prediction  actual  \n",
       "0     0      0           1       1  \n",
       "1     0      2           1       1  \n",
       "2     1      2           1       1  \n",
       "3     0      3           0       1  \n",
       "4     0      2           1       1  \n",
       "..  ...    ...         ...     ...  \n",
       "86    0      2           1       1  \n",
       "87    1      3           0       1  \n",
       "88    1      2           1       1  \n",
       "89    1      2           1       1  \n",
       "90    0      3           1       1  \n",
       "\n",
       "[91 rows x 15 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['actual'] = df['output']\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "917ae275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8131868131868132"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using score method to get the accuracy of the model. Hint: should be ~.83 or 83%.\n",
    "score = lrm.score(X_test, y_test)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9bd8675",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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