From 3109096cff11bb876181bec39b755bad91c2ec6c Mon Sep 17 00:00:00 2001
From: Padb95 <pedralexdombar@gmail.com>
Date: Sat, 27 Jul 2024 15:44:46 +0100
Subject: [PATCH] Lab Done

---
 .ipynb_checkpoints/Untitled-checkpoint.ipynb  |    6 +
 ...predictions with logistic regression.ipynb | 1465 +++++++++++++++++
 2 files changed, 1471 insertions(+)
 create mode 100644 .ipynb_checkpoints/Untitled-checkpoint.ipynb
 create mode 100644 Lab  Making predictions with logistic regression.ipynb

diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Lab  Making predictions with logistic regression.ipynb b/Lab  Making predictions with logistic regression.ipynb
new file mode 100644
index 0000000..257f11f
--- /dev/null
+++ b/Lab  Making predictions with logistic regression.ipynb	
@@ -0,0 +1,1465 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "163a4f29-06a2-4834-a0fa-7f62f3d65d96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b52e3478-e835-4d1a-bd87-ec83a3e2263e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>male</th>\n",
+       "      <th>age</th>\n",
+       "      <th>education</th>\n",
+       "      <th>currentSmoker</th>\n",
+       "      <th>cigsPerDay</th>\n",
+       "      <th>BPMeds</th>\n",
+       "      <th>prevalentStroke</th>\n",
+       "      <th>prevalentHyp</th>\n",
+       "      <th>diabetes</th>\n",
+       "      <th>totChol</th>\n",
+       "      <th>sysBP</th>\n",
+       "      <th>diaBP</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>heartRate</th>\n",
+       "      <th>glucose</th>\n",
+       "      <th>TenYearCHD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4238 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      male  age  education  currentSmoker  cigsPerDay  BPMeds  \\\n",
+       "0        1   39        4.0              0         0.0     0.0   \n",
+       "1        0   46        2.0              0         0.0     0.0   \n",
+       "2        1   48        1.0              1        20.0     0.0   \n",
+       "3        0   61        3.0              1        30.0     0.0   \n",
+       "4        0   46        3.0              1        23.0     0.0   \n",
+       "...    ...  ...        ...            ...         ...     ...   \n",
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+       "4236     0   44        1.0              1        15.0     0.0   \n",
+       "4237     0   52        2.0              0         0.0     0.0   \n",
+       "\n",
+       "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
+       "0                   0             0         0    195.0  106.0   70.0  26.97   \n",
+       "1                   0             0         0    250.0  121.0   81.0  28.73   \n",
+       "2                   0             0         0    245.0  127.5   80.0  25.34   \n",
+       "3                   0             1         0    225.0  150.0   95.0  28.58   \n",
+       "4                   0             0         0    285.0  130.0   84.0  23.10   \n",
+       "...               ...           ...       ...      ...    ...    ...    ...   \n",
+       "4233                0             1         0    313.0  179.0   92.0  25.97   \n",
+       "4234                0             0         0    207.0  126.5   80.0  19.71   \n",
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+       "4236                0             0         0    210.0  126.5   87.0  19.16   \n",
+       "4237                0             0         0    269.0  133.5   83.0  21.47   \n",
+       "\n",
+       "      heartRate  glucose  TenYearCHD  \n",
+       "0          80.0     77.0           0  \n",
+       "1          95.0     76.0           0  \n",
+       "2          75.0     70.0           0  \n",
+       "3          65.0    103.0           1  \n",
+       "4          85.0     85.0           0  \n",
+       "...         ...      ...         ...  \n",
+       "4233       66.0     86.0           1  \n",
+       "4234       65.0     68.0           0  \n",
+       "4235       84.0     86.0           0  \n",
+       "4236       86.0      NaN           0  \n",
+       "4237       80.0    107.0           0  \n",
+       "\n",
+       "[4238 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes = pd.read_csv(r\"C:\\Users\\HP\\Downloads\\framingham.csv\")\n",
+    "diabetes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d78edee0-39c8-434c-84aa-440833c70ae8",
+   "metadata": {},
+   "outputs": [
+    {
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+       "      <td>25.97</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4234</th>\n",
+       "      <td>1</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>207.0</td>\n",
+       "      <td>126.5</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>19.71</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4235</th>\n",
+       "      <td>0</td>\n",
+       "      <td>48</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>248.0</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>22.00</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4236</th>\n",
+       "      <td>0</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>210.0</td>\n",
+       "      <td>126.5</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>19.16</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4237</th>\n",
+       "      <td>0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>269.0</td>\n",
+       "      <td>133.5</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>21.47</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4238 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      male  age  education  currentsmoker  cigsperday  bpmeds  \\\n",
+       "0        1   39        4.0              0         0.0     0.0   \n",
+       "1        0   46        2.0              0         0.0     0.0   \n",
+       "2        1   48        1.0              1        20.0     0.0   \n",
+       "3        0   61        3.0              1        30.0     0.0   \n",
+       "4        0   46        3.0              1        23.0     0.0   \n",
+       "...    ...  ...        ...            ...         ...     ...   \n",
+       "4233     1   50        1.0              1         1.0     0.0   \n",
+       "4234     1   51        3.0              1        43.0     0.0   \n",
+       "4235     0   48        2.0              1        20.0     NaN   \n",
+       "4236     0   44        1.0              1        15.0     0.0   \n",
+       "4237     0   52        2.0              0         0.0     0.0   \n",
+       "\n",
+       "      prevalentstroke  prevalenthyp  diabetes  totchol  sysbp  diabp    bmi  \\\n",
+       "0                   0             0         0    195.0  106.0   70.0  26.97   \n",
+       "1                   0             0         0    250.0  121.0   81.0  28.73   \n",
+       "2                   0             0         0    245.0  127.5   80.0  25.34   \n",
+       "3                   0             1         0    225.0  150.0   95.0  28.58   \n",
+       "4                   0             0         0    285.0  130.0   84.0  23.10   \n",
+       "...               ...           ...       ...      ...    ...    ...    ...   \n",
+       "4233                0             1         0    313.0  179.0   92.0  25.97   \n",
+       "4234                0             0         0    207.0  126.5   80.0  19.71   \n",
+       "4235                0             0         0    248.0  131.0   72.0  22.00   \n",
+       "4236                0             0         0    210.0  126.5   87.0  19.16   \n",
+       "4237                0             0         0    269.0  133.5   83.0  21.47   \n",
+       "\n",
+       "      heartrate  glucose  tenyearchd  \n",
+       "0          80.0     77.0           0  \n",
+       "1          95.0     76.0           0  \n",
+       "2          75.0     70.0           0  \n",
+       "3          65.0    103.0           1  \n",
+       "4          85.0     85.0           0  \n",
+       "...         ...      ...         ...  \n",
+       "4233       66.0     86.0           1  \n",
+       "4234       65.0     68.0           0  \n",
+       "4235       84.0     86.0           0  \n",
+       "4236       86.0      NaN           0  \n",
+       "4237       80.0    107.0           0  \n",
+       "\n",
+       "[4238 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols = []\n",
+    "for i in range(len(diabetes.columns)): \n",
+    "    cols.append(diabetes.columns[i].lower().replace(' ', '_'))\n",
+    "diabetes.columns = cols\n",
+    "diabetes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "da1066cf-41bc-428f-86c6-c17a8c66c95e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "male                 0\n",
+       "age                  0\n",
+       "education          105\n",
+       "currentsmoker        0\n",
+       "cigsperday          29\n",
+       "bpmeds              53\n",
+       "prevalentstroke      0\n",
+       "prevalenthyp         0\n",
+       "diabetes             0\n",
+       "totchol             50\n",
+       "sysbp                0\n",
+       "diabp                0\n",
+       "bmi                 19\n",
+       "heartrate            1\n",
+       "glucose            388\n",
+       "tenyearchd           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "973bb9f3-3bd3-4672-a400-66a0518fe3e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
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+       "      <th></th>\n",
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+       "      <th>3</th>\n",
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+       "    <tr>\n",
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+       "      <th>4237</th>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4238 rows × 0 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]\n",
+       "\n",
+       "[4238 rows x 0 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorical = diabetes.select_dtypes(include='object')\n",
+    "categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8a7b9d8a-c059-4257-889d-321c965c4999",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dia_drop_na = diabetes.dropna(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f01f93b2-302e-4db4-81d4-f2fc96488dac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "male               0\n",
+       "age                0\n",
+       "education          0\n",
+       "currentsmoker      0\n",
+       "cigsperday         0\n",
+       "bpmeds             0\n",
+       "prevalentstroke    0\n",
+       "prevalenthyp       0\n",
+       "diabetes           0\n",
+       "totchol            0\n",
+       "sysbp              0\n",
+       "diabp              0\n",
+       "bmi                0\n",
+       "heartrate          0\n",
+       "glucose            0\n",
+       "tenyearchd         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dia_drop_na.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2d7728fa-a307-4076-9b32-af85a48ec216",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>male</th>\n",
+       "      <th>age</th>\n",
+       "      <th>education</th>\n",
+       "      <th>currentsmoker</th>\n",
+       "      <th>cigsperday</th>\n",
+       "      <th>bpmeds</th>\n",
+       "      <th>prevalentstroke</th>\n",
+       "      <th>prevalenthyp</th>\n",
+       "      <th>diabetes</th>\n",
+       "      <th>totchol</th>\n",
+       "      <th>sysbp</th>\n",
+       "      <th>diabp</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>heartrate</th>\n",
+       "      <th>glucose</th>\n",
+       "      <th>tenyearchd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>39</td>\n",
+       "      <td>4.0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>26.97</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>28.73</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>48</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>245.0</td>\n",
+       "      <td>127.5</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>25.34</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>61</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>28.58</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>285.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>23.10</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>4231</th>\n",
+       "      <td>1</td>\n",
+       "      <td>58</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>187.0</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>24.96</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4232</th>\n",
+       "      <td>1</td>\n",
+       "      <td>68</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>168.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>23.14</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4233</th>\n",
+       "      <td>1</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>313.0</td>\n",
+       "      <td>179.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>25.97</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4234</th>\n",
+       "      <td>1</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>207.0</td>\n",
+       "      <td>126.5</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>19.71</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4237</th>\n",
+       "      <td>0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>269.0</td>\n",
+       "      <td>133.5</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>21.47</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3656 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      male  age  education  currentsmoker  cigsperday  bpmeds  \\\n",
+       "0        1   39        4.0              0         0.0     0.0   \n",
+       "1        0   46        2.0              0         0.0     0.0   \n",
+       "2        1   48        1.0              1        20.0     0.0   \n",
+       "3        0   61        3.0              1        30.0     0.0   \n",
+       "4        0   46        3.0              1        23.0     0.0   \n",
+       "...    ...  ...        ...            ...         ...     ...   \n",
+       "4231     1   58        3.0              0         0.0     0.0   \n",
+       "4232     1   68        1.0              0         0.0     0.0   \n",
+       "4233     1   50        1.0              1         1.0     0.0   \n",
+       "4234     1   51        3.0              1        43.0     0.0   \n",
+       "4237     0   52        2.0              0         0.0     0.0   \n",
+       "\n",
+       "      prevalentstroke  prevalenthyp  diabetes  totchol  sysbp  diabp    bmi  \\\n",
+       "0                   0             0         0    195.0  106.0   70.0  26.97   \n",
+       "1                   0             0         0    250.0  121.0   81.0  28.73   \n",
+       "2                   0             0         0    245.0  127.5   80.0  25.34   \n",
+       "3                   0             1         0    225.0  150.0   95.0  28.58   \n",
+       "4                   0             0         0    285.0  130.0   84.0  23.10   \n",
+       "...               ...           ...       ...      ...    ...    ...    ...   \n",
+       "4231                0             1         0    187.0  141.0   81.0  24.96   \n",
+       "4232                0             1         0    176.0  168.0   97.0  23.14   \n",
+       "4233                0             1         0    313.0  179.0   92.0  25.97   \n",
+       "4234                0             0         0    207.0  126.5   80.0  19.71   \n",
+       "4237                0             0         0    269.0  133.5   83.0  21.47   \n",
+       "\n",
+       "      heartrate  glucose  tenyearchd  \n",
+       "0          80.0     77.0           0  \n",
+       "1          95.0     76.0           0  \n",
+       "2          75.0     70.0           0  \n",
+       "3          65.0    103.0           1  \n",
+       "4          85.0     85.0           0  \n",
+       "...         ...      ...         ...  \n",
+       "4231       80.0     81.0           0  \n",
+       "4232       60.0     79.0           1  \n",
+       "4233       66.0     86.0           1  \n",
+       "4234       65.0     68.0           0  \n",
+       "4237       80.0    107.0           0  \n",
+       "\n",
+       "[3656 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numerical = dia_drop_na.select_dtypes(exclude='object')\n",
+    "numerical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f0bff0cc-795c-486a-b9f5-b65bb9f00227",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Outliers detected by IQR method:\n",
+      "       male  age  education  currentsmoker  cigsperday  bpmeds  \\\n",
+      "3        0   61        3.0              1        30.0     0.0   \n",
+      "6        0   63        1.0              0         0.0     0.0   \n",
+      "13       0   41        3.0              0         0.0     1.0   \n",
+      "15       0   38        2.0              1        20.0     0.0   \n",
+      "17       0   46        2.0              1        20.0     0.0   \n",
+      "...    ...  ...        ...            ...         ...     ...   \n",
+      "4223     1   56        4.0              0         0.0     1.0   \n",
+      "4226     1   58        1.0              0         0.0     0.0   \n",
+      "4228     0   50        1.0              0         0.0     0.0   \n",
+      "4232     1   68        1.0              0         0.0     0.0   \n",
+      "4233     1   50        1.0              1         1.0     0.0   \n",
+      "\n",
+      "      prevalentstroke  prevalenthyp  diabetes  totchol  sysbp  diabp    bmi  \\\n",
+      "3                   0             1         0    225.0  150.0   95.0  28.58   \n",
+      "6                   0             0         0    205.0  138.0   71.0  33.11   \n",
+      "13                  0             1         0    332.0  124.0   88.0  31.31   \n",
+      "15                  0             1         0    221.0  140.0   90.0  21.35   \n",
+      "17                  0             0         0    291.0  112.0   78.0  23.38   \n",
+      "...               ...           ...       ...      ...    ...    ...    ...   \n",
+      "4223                0             1         0    287.0  149.0   98.0  21.68   \n",
+      "4226                0             0         0    233.0  125.5   84.0  26.05   \n",
+      "4228                0             1         1    260.0  190.0  130.0  43.67   \n",
+      "4232                0             1         0    176.0  168.0   97.0  23.14   \n",
+      "4233                0             1         0    313.0  179.0   92.0  25.97   \n",
+      "\n",
+      "      heartrate  glucose  tenyearchd  \n",
+      "3          65.0    103.0           1  \n",
+      "6          60.0     85.0           1  \n",
+      "13         65.0     84.0           0  \n",
+      "15         95.0     70.0           1  \n",
+      "17         80.0     89.0           1  \n",
+      "...         ...      ...         ...  \n",
+      "4223       90.0     75.0           1  \n",
+      "4226       67.0     76.0           1  \n",
+      "4228       85.0    260.0           0  \n",
+      "4232       60.0     79.0           1  \n",
+      "4233       66.0     86.0           1  \n",
+      "\n",
+      "[961 rows x 16 columns]\n",
+      "\n",
+      "DataFrame after removing outliers:\n",
+      "       male  age  education  currentsmoker  cigsperday  bpmeds  \\\n",
+      "0        1   39        4.0              0         0.0     0.0   \n",
+      "1        0   46        2.0              0         0.0     0.0   \n",
+      "2        1   48        1.0              1        20.0     0.0   \n",
+      "4        0   46        3.0              1        23.0     0.0   \n",
+      "5        0   43        2.0              0         0.0     0.0   \n",
+      "...    ...  ...        ...            ...         ...     ...   \n",
+      "4225     1   45        4.0              1        43.0     0.0   \n",
+      "4227     1   43        4.0              1        20.0     0.0   \n",
+      "4231     1   58        3.0              0         0.0     0.0   \n",
+      "4234     1   51        3.0              1        43.0     0.0   \n",
+      "4237     0   52        2.0              0         0.0     0.0   \n",
+      "\n",
+      "      prevalentstroke  prevalenthyp  diabetes  totchol  sysbp  diabp    bmi  \\\n",
+      "0                   0             0         0    195.0  106.0   70.0  26.97   \n",
+      "1                   0             0         0    250.0  121.0   81.0  28.73   \n",
+      "2                   0             0         0    245.0  127.5   80.0  25.34   \n",
+      "4                   0             0         0    285.0  130.0   84.0  23.10   \n",
+      "5                   0             1         0    228.0  180.0  110.0  30.30   \n",
+      "...               ...           ...       ...      ...    ...    ...    ...   \n",
+      "4225                0             0         0    216.0  137.5   85.0  24.24   \n",
+      "4227                0             0         0    187.0  129.5   88.0  25.62   \n",
+      "4231                0             1         0    187.0  141.0   81.0  24.96   \n",
+      "4234                0             0         0    207.0  126.5   80.0  19.71   \n",
+      "4237                0             0         0    269.0  133.5   83.0  21.47   \n",
+      "\n",
+      "      heartrate  glucose  tenyearchd  \n",
+      "0          80.0     77.0           0  \n",
+      "1          95.0     76.0           0  \n",
+      "2          75.0     70.0           0  \n",
+      "4          85.0     85.0           0  \n",
+      "5          77.0     99.0           0  \n",
+      "...         ...      ...         ...  \n",
+      "4225       83.0    105.0           0  \n",
+      "4227       80.0     75.0           0  \n",
+      "4231       80.0     81.0           0  \n",
+      "4234       65.0     68.0           0  \n",
+      "4237       80.0    107.0           0  \n",
+      "\n",
+      "[2695 rows x 16 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "Q1 = numerical.quantile(0.25)\n",
+    "Q3 = numerical.quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "is_outlier = (numerical < (Q1 - 1.5 * IQR)) | (numerical > (Q3 + 1.5 * IQR))\n",
+    "\n",
+    "outliers = numerical[is_outlier.any(axis=1)]\n",
+    "print(\"\\nOutliers detected by IQR method:\\n\", outliers)\n",
+    "\n",
+    "dia_no_outliers = numerical[~is_outlier.any(axis=1)]\n",
+    "print(\"\\nDataFrame after removing outliers:\\n\", firo_no_outliers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "fe824c1e-153b-4204-80bb-9cba29a7c5de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Min-Max Normalized DataFrame:\n",
+      "   male       age  education  currentsmoker  cigsperday  bpmeds  \\\n",
+      "0   1.0  0.184211   1.000000            0.0    0.000000     0.0   \n",
+      "1   0.0  0.368421   0.333333            0.0    0.000000     0.0   \n",
+      "2   1.0  0.421053   0.000000            1.0    0.285714     0.0   \n",
+      "3   0.0  0.763158   0.666667            1.0    0.428571     0.0   \n",
+      "4   0.0  0.368421   0.666667            1.0    0.328571     0.0   \n",
+      "\n",
+      "   prevalentstroke  prevalenthyp  diabetes   totchol     sysbp     diabp  \\\n",
+      "0              0.0           0.0       0.0  0.168378  0.106383  0.232804   \n",
+      "1              0.0           0.0       0.0  0.281314  0.177305  0.349206   \n",
+      "2              0.0           0.0       0.0  0.271047  0.208038  0.338624   \n",
+      "3              0.0           1.0       0.0  0.229979  0.314421  0.497354   \n",
+      "4              0.0           0.0       0.0  0.353183  0.219858  0.380952   \n",
+      "\n",
+      "        bmi  heartrate   glucose  tenyearchd  \n",
+      "0  0.277024   0.363636  0.104520         0.0  \n",
+      "1  0.319680   0.515152  0.101695         0.0  \n",
+      "2  0.237518   0.313131  0.084746         0.0  \n",
+      "3  0.316045   0.212121  0.177966         1.0  \n",
+      "4  0.183228   0.414141  0.127119         0.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler = MinMaxScaler()\n",
+    "\n",
+    "dia_normalized = pd.DataFrame(scaler.fit_transform(numerical), columns=numerical.columns)\n",
+    "\n",
+    "\n",
+    "print(\"\\nMin-Max Normalized DataFrame:\")\n",
+    "print(dia_normalized.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "fea34360-462b-48a3-bb8d-52c9877c08b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Split features and target variable\n",
+    "X = dia_normalized.drop('diabetes',axis = 1)\n",
+    "y = dia_normalized['diabetes']\n",
+    "\n",
+    "# Split data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "f510840b-bfcd-4fcf-865b-7be672b68813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9836065573770492"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "LR = LogisticRegression()\n",
+    "LR.fit(X_train, y_train)\n",
+    "\n",
+    "LR.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f936661b-84c1-47c5-afc0-8d5f44e90056",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "precision:  1.0\n",
+      "recall:  0.29411764705882354\n",
+      "f1:  0.45454545454545453\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.98      1.00      0.99       715\n",
+      "         1.0       1.00      0.29      0.45        17\n",
+      "\n",
+      "    accuracy                           0.98       732\n",
+      "   macro avg       0.99      0.65      0.72       732\n",
+      "weighted avg       0.98      0.98      0.98       732\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score\n",
+    "from sklearn.metrics import recall_score\n",
+    "from sklearn.metrics import f1_score\n",
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "pred = LR.predict(X_test)\n",
+    "\n",
+    "print(\"precision: \",precision_score(y_test,pred))\n",
+    "print(\"recall: \",recall_score(y_test,pred))\n",
+    "print(\"f1: \",f1_score(y_test,pred))\n",
+    "\n",
+    "\n",
+    "print(classification_report(y_test, pred))\n",
+    "\n",
+    "# to predict the probabilities instead of a class\n",
+    "predicted_probabilities = LR.predict_proba(X_test)[:,1]\n",
+    "predicted_probabilities\n",
+    "\n",
+    "# Then you need to define it by hand\n",
+    "custom_threshold = 0.7  # For example, use a threshold of 0.7\n",
+    "custom_predictions = (predicted_probabilities >= custom_threshold).astype(int)\n",
+    "custom_predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e657fcc-7649-4ac5-ab9c-007f4c2317b9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5077f7bc-65c3-453a-8eb0-feaaead8d50b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31fe8236-34c4-4651-865f-9636a4a0cbb3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
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+   "mimetype": "text/x-python",
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+   "pygments_lexer": "ipython3",
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+ },
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+ "nbformat_minor": 5
+}