Code-Example-018-kNN.ipynb

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   "source": [
    "# Standard Headers\n",
    "# You are welcome to add additional headers here if you wish\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Enable inline mode for matplotlib so that Jupyter displays graphs\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heart Dataset \n",
    "\n",
    "In this Assignment we will work with some patients dataset. \n",
    "\n",
    "We have access to 303 patients data. The features are listed below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPain</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExAng</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Thal</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>typical</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fixed</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>reversable</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>nonanginal</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>nontypical</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>typical</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>reversable</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>reversable</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>reversable</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>nontypical</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>nonanginal</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex     ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  \\\n",
       "0     63    1       typical     145   233    1        2    150      0   \n",
       "1     67    1  asymptomatic     160   286    0        2    108      1   \n",
       "2     67    1  asymptomatic     120   229    0        2    129      1   \n",
       "3     37    1    nonanginal     130   250    0        0    187      0   \n",
       "4     41    0    nontypical     130   204    0        2    172      0   \n",
       "..   ...  ...           ...     ...   ...  ...      ...    ...    ...   \n",
       "298   45    1       typical     110   264    0        0    132      0   \n",
       "299   68    1  asymptomatic     144   193    1        0    141      0   \n",
       "300   57    1  asymptomatic     130   131    0        0    115      1   \n",
       "301   57    0    nontypical     130   236    0        2    174      0   \n",
       "302   38    1    nonanginal     138   175    0        0    173      0   \n",
       "\n",
       "     Oldpeak  Slope   Ca        Thal Target  \n",
       "0        2.3      3  0.0       fixed     No  \n",
       "1        1.5      2  3.0      normal    Yes  \n",
       "2        2.6      2  2.0  reversable    Yes  \n",
       "3        3.5      3  0.0      normal     No  \n",
       "4        1.4      1  0.0      normal     No  \n",
       "..       ...    ...  ...         ...    ...  \n",
       "298      1.2      2  0.0  reversable    Yes  \n",
       "299      3.4      2  2.0  reversable    Yes  \n",
       "300      1.2      2  1.0  reversable    Yes  \n",
       "301      0.0      2  1.0      normal    Yes  \n",
       "302      0.0      1  NaN      normal     No  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
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   "source": [
    "# Your code here\n",
    "heart_df = pd.read_csv(\"./datasets/Heart.csv\")\n",
    "heart_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Age:** The person’s age in years\n",
    "\n",
    "**Sex:** The person’s sex (1 = male, 0 = female)\n",
    "\n",
    "**ChestPain:** chest pain type\n",
    "\n",
    "* Value 0: asymptomatic\n",
    "* Value 1: atypical angina\n",
    "* Value 2: non-anginal pain\n",
    "* Value 3: typical angina\n",
    "\n",
    "**RestBP:** The person’s resting blood pressure (mm Hg on admission to the hospital)\n",
    "\n",
    "**Chol:** The person’s cholesterol measurement in mg/dl\n",
    "\n",
    "**Fbs:** The person’s fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)\n",
    "restecg: resting electrocardiographic results\n",
    "\n",
    "* Value 0: showing probable or definite left ventricular hypertrophy by Estes’ criteria\n",
    "* Value 1: normal\n",
    "* Value 2: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n",
    "\n",
    "**RestECG:** The person’s maximum heart rate achieved\n",
    "\n",
    "**MaxHR:** Exercise induced angina (1 = yes; 0 = no)\n",
    "\n",
    "**Oldpeak:** ST depression induced by exercise relative to rest (‘ST’ relates to positions on the ECG plot. See more here)\n",
    "\n",
    "**Slope:** the slope of the peak exercise ST segment — 0: downsloping; 1: flat; 2: upsloping\n",
    "\n",
    "* 0: downsloping; \n",
    "* 1: flat; \n",
    "* 2: upsloping\n",
    "\n",
    "**Ca:** The number of major vessels (0–3)\n",
    "\n",
    "**Thal:** A blood disorder called thalassemia Value 0: NULL (dropped from the dataset previously\n",
    "\n",
    "* Value 1: fixed defect (no blood flow in some part of the heart)\n",
    "* Value 2: normal blood flow\n",
    "* Value 3: reversible defect (a blood flow is observed but it is not normal)\n",
    "\n",
    "**Target:** Heart disease (1 = no, 0= yes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classiification with kNN\n",
    "\n",
    "Predict **Target** using the following features:\n",
    "   * **Age** \n",
    "   * **Sex** \n",
    "   * **ChestPain** \n",
    "   * **RestBP** \n",
    "   * **Chol** \n",
    "   * **Fbs** \n",
    "   * **RestECG** \n",
    "   * **MaxHR**    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPain</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>No</td>\n",
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       "      <td>150</td>\n",
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       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
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       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>nonanginal</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>nontypical</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
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       "      <td>172</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Yes</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>Yes</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
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       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>Yes</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>nontypical</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>No</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>nonanginal</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
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       "<p>303 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Target  Age  Sex     ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR\n",
       "0       No   63    1       typical     145   233    1        2    150\n",
       "1      Yes   67    1  asymptomatic     160   286    0        2    108\n",
       "2      Yes   67    1  asymptomatic     120   229    0        2    129\n",
       "3       No   37    1    nonanginal     130   250    0        0    187\n",
       "4       No   41    0    nontypical     130   204    0        2    172\n",
       "..     ...  ...  ...           ...     ...   ...  ...      ...    ...\n",
       "298    Yes   45    1       typical     110   264    0        0    132\n",
       "299    Yes   68    1  asymptomatic     144   193    1        0    141\n",
       "300    Yes   57    1  asymptomatic     130   131    0        0    115\n",
       "301    Yes   57    0    nontypical     130   236    0        2    174\n",
       "302     No   38    1    nonanginal     138   175    0        0    173\n",
       "\n",
       "[303 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_col = 'Target'\n",
    "\n",
    "df = heart_df[['Target', 'Age', 'Sex', 'ChestPain',  'RestBP', 'Chol', 'Fbs', 'RestECG', 'MaxHR']]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Target       object\n",
       "Age           int64\n",
       "Sex           int64\n",
       "ChestPain    object\n",
       "RestBP        int64\n",
       "Chol          int64\n",
       "Fbs           int64\n",
       "RestECG       int64\n",
       "MaxHR         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Imputation to fill in missing values\n",
    "\n",
    "kNN doesn't work when all of the attributes are not valid all of the attribute columns, so fill in all the missing values in data with the mean of that column.  \n",
    "\n",
    "We save the mean of each column in a dictionary, `dict_imputation`, whose key is the column name, so we can apply the same imputation to the test set later. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           typical\n",
       "1      asymptomatic\n",
       "2      asymptomatic\n",
       "3        nonanginal\n",
       "4        nontypical\n",
       "           ...     \n",
       "298         typical\n",
       "299    asymptomatic\n",
       "300    asymptomatic\n",
       "301      nontypical\n",
       "302      nonanginal\n",
       "Name: ChestPain, Length: 303, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ChestPain']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['ChestPain'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if Contains NaN\n",
    "df['ChestPain'].isnull().values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['ChestPain'] = data.locdata['ChestPain'].map({1: 'typical', 2: 'asymptomatic', 3: 'nonanginal', 4: 'nontypical'})\n",
    "# Improve the following code with better pandas conde. \n",
    "\n",
    "import warnings\n",
    "\n",
    "from pandas.core.common import SettingWithCopyWarning\n",
    "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
    "\n",
    "df.loc[df.ChestPain=='typical', 'ChestPain']=1\n",
    "df.loc[df.ChestPain=='asymptomatic', 'ChestPain']=2\n",
    "df.loc[df.ChestPain=='nonanginal', 'ChestPain']=3\n",
    "df.loc[df.ChestPain=='nontypical', 'ChestPain']=4\n",
    "\n",
    "\n",
    "# Convert Targert to 0 or 1 \n",
    "\n",
    "df.loc[df.Target=='Yes', 'Target']=1\n",
    "df.loc[df.Target=='No', 'Target']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>132</td>\n",
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       "      <th>299</th>\n",
       "      <td>1</td>\n",
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       "      <td>144</td>\n",
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       "      <td>141</td>\n",
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       "      <th>300</th>\n",
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       "      <td>1</td>\n",
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       "      <td>174</td>\n",
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       "      <th>302</th>\n",
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       "      <td>138</td>\n",
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       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Target  Age  Sex ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR\n",
       "0        0   63    1         1     145   233    1        2    150\n",
       "1        1   67    1         2     160   286    0        2    108\n",
       "2        1   67    1         2     120   229    0        2    129\n",
       "3        0   37    1         3     130   250    0        0    187\n",
       "4        0   41    0         4     130   204    0        2    172\n",
       "..     ...  ...  ...       ...     ...   ...  ...      ...    ...\n",
       "298      1   45    1         1     110   264    0        0    132\n",
       "299      1   68    1         2     144   193    1        0    141\n",
       "300      1   57    1         2     130   131    0        0    115\n",
       "301      1   57    0         4     130   236    0        2    174\n",
       "302      0   38    1         3     138   175    0        0    173\n",
       "\n",
       "[303 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_col = 'Target'\n",
    "\n",
    "dict_imputation = dict()\n",
    "for col in df:\n",
    "    if col != target_col:\n",
    "        dict_imputation[col] = df[col].mean(skipna=True)\n",
    "        df[col].fillna(value=dict_imputation[col],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>130</td>\n",
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       "      <td>174</td>\n",
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       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Target  Age  Sex  ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR\n",
       "0        0   63    1          1     145   233    1        2    150\n",
       "1        1   67    1          2     160   286    0        2    108\n",
       "2        1   67    1          2     120   229    0        2    129\n",
       "3        0   37    1          3     130   250    0        0    187\n",
       "4        0   41    0          4     130   204    0        2    172\n",
       "..     ...  ...  ...        ...     ...   ...  ...      ...    ...\n",
       "298      1   45    1          1     110   264    0        0    132\n",
       "299      1   68    1          2     144   193    1        0    141\n",
       "300      1   57    1          2     130   131    0        0    115\n",
       "301      1   57    0          4     130   236    0        2    174\n",
       "302      0   38    1          3     138   175    0        0    173\n",
       "\n",
       "[303 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Target       0\n",
       "Age          0\n",
       "Sex          0\n",
       "ChestPain    0\n",
       "RestBP       0\n",
       "Chol         0\n",
       "Fbs          0\n",
       "RestECG      0\n",
       "MaxHR        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if we still have NaN\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q3 Normalize all the attributes to be between [0,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_normalize = dict()\n",
    "for col in df.columns:\n",
    "    if col != target_col:\n",
    "        dict_normalize[col] = (df[col].min(),df[col].max())\n",
    "        df[col] = (df[col] - dict_normalize[col][0])/(dict_normalize[col][1]-dict_normalize[col][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.339623</td>\n",
       "      <td>0.178082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.770992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.150943</td>\n",
       "      <td>0.315068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.465649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.471698</td>\n",
       "      <td>0.152968</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.339623</td>\n",
       "      <td>0.011416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.335878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>1</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.339623</td>\n",
       "      <td>0.251142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.786260</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>0</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.415094</td>\n",
       "      <td>0.111872</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.778626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Target       Age  Sex  ChestPain    RestBP      Chol  Fbs  RestECG  \\\n",
       "0        0  0.708333  1.0   0.000000  0.481132  0.244292  1.0      1.0   \n",
       "1        1  0.791667  1.0   0.333333  0.622642  0.365297  0.0      1.0   \n",
       "2        1  0.791667  1.0   0.333333  0.245283  0.235160  0.0      1.0   \n",
       "3        0  0.166667  1.0   0.666667  0.339623  0.283105  0.0      0.0   \n",
       "4        0  0.250000  0.0   1.000000  0.339623  0.178082  0.0      1.0   \n",
       "..     ...       ...  ...        ...       ...       ...  ...      ...   \n",
       "298      1  0.333333  1.0   0.000000  0.150943  0.315068  0.0      0.0   \n",
       "299      1  0.812500  1.0   0.333333  0.471698  0.152968  1.0      0.0   \n",
       "300      1  0.583333  1.0   0.333333  0.339623  0.011416  0.0      0.0   \n",
       "301      1  0.583333  0.0   1.000000  0.339623  0.251142  0.0      1.0   \n",
       "302      0  0.187500  1.0   0.666667  0.415094  0.111872  0.0      0.0   \n",
       "\n",
       "        MaxHR  \n",
       "0    0.603053  \n",
       "1    0.282443  \n",
       "2    0.442748  \n",
       "3    0.885496  \n",
       "4    0.770992  \n",
       "..        ...  \n",
       "298  0.465649  \n",
       "299  0.534351  \n",
       "300  0.335878  \n",
       "301  0.786260  \n",
       "302  0.778626  \n",
       "\n",
       "[303 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Test/Train\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# df2=df.drop(['Target'], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df, df['Target'], test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Chol</th>\n",
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       "      <td>0.333333</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.116438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.694656</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "<p>242 rows × 9 columns</p>\n",
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      "text/plain": [
       "    Target       Age  Sex  ChestPain    RestBP      Chol  Fbs  RestECG  \\\n",
       "102      0  0.583333  0.0   0.333333  0.320755  0.404110  0.0      1.0   \n",
       "261      1  0.604167  0.0   1.000000  0.396226  0.440639  1.0      1.0   \n",
       "228      1  0.520833  1.0   0.333333  0.150943  0.182648  0.0      1.0   \n",
       "288      0  0.562500  1.0   1.000000  0.339623  0.216895  0.0      1.0   \n",
       "78       0  0.395833  1.0   1.000000  0.339623  0.271689  0.0      1.0   \n",
       "..     ...       ...  ...        ...       ...       ...  ...      ...   \n",
       "106      1  0.625000  1.0   0.333333  0.433962  0.116438  0.0      0.0   \n",
       "83       1  0.812500  1.0   0.666667  0.811321  0.337900  1.0      1.0   \n",
       "17       0  0.520833  1.0   0.333333  0.433962  0.257991  0.0      0.0   \n",
       "230      0  0.479167  0.0   0.666667  0.396226  0.159817  0.0      1.0   \n",
       "98       0  0.479167  1.0   1.000000  0.377358  0.171233  0.0      0.0   \n",
       "\n",
       "        MaxHR  \n",
       "102  0.671756  \n",
       "261  0.618321  \n",
       "228  0.282443  \n",
       "288  0.702290  \n",
       "78   0.832061  \n",
       "..        ...  \n",
       "106  0.694656  \n",
       "83   0.603053  \n",
       "17   0.679389  \n",
       "230  0.748092  \n",
       "98   0.664122  \n",
       "\n",
       "[242 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def knn_class(df_train, k, target_col, observation ):\n",
    "    df_train['distance'] = df_train.apply( lambda x : np.linalg.norm( x.drop(index=target_col) - observation), axis=1 )\n",
    "    df_train = df_train.sort_values(by='distance').reset_index(drop=True)\n",
    "    df_k = df_train[0:k]\n",
    "    return df_k[target_col].value_counts().index[0] \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the actual Prediction using different k \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.320755</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.687023</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
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       "      <td>0.245283</td>\n",
       "      <td>0.488584</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.770992</td>\n",
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       "      <td>0.283019</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>42</th>\n",
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       "<p>61 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "    Target       Age  ChestPain    RestBP      Chol  Fbs  RestECG     MaxHR\n",
       "11       0  0.562500   1.000000  0.433962  0.383562  0.0      1.0  0.625954\n",
       "292      1  0.312500   0.333333  0.245283  0.098174  0.0      0.0  0.557252\n",
       "269      0  0.270833   0.666667  0.339623  0.123288  0.0      0.0  0.603053\n",
       "268      1  0.229167   0.333333  0.547170  0.221461  0.0      0.0  0.839695\n",
       "94       0  0.708333   0.666667  0.386792  0.287671  0.0      1.0  0.770992\n",
       "..     ...       ...        ...       ...       ...  ...      ...       ...\n",
       "95       1  0.479167   0.333333  0.320755  0.294521  0.0      0.0  0.687023\n",
       "26       0  0.604167   0.666667  0.245283  0.488584  0.0      0.0  0.770992\n",
       "259      1  0.583333   1.000000  0.283019  0.308219  0.0      0.0  0.534351\n",
       "143      1  0.729167   0.666667  0.292453  0.417808  0.0      0.0  0.458015\n",
       "42       0  0.875000   1.000000  0.622642  0.401826  0.0      0.0  0.694656\n",
       "\n",
       "[61 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_droped = X_train.drop([ 'Sex'], axis=1)\n",
    "X_train_droped = X_test.drop(['Sex'], axis=1)\n",
    "\n",
    "X_train_droped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k =  1 Acc= 0.5409836065573771\n",
      "k =  3 Acc= 0.5409836065573771\n",
      "k =  8 Acc= 0.5409836065573771\n",
      "k =  13 Acc= 0.5409836065573771\n",
      "k =  17 Acc= 0.5409836065573771\n",
      "k =  25 Acc= 0.5409836065573771\n",
      "k =  30 Acc= 0.5409836065573771\n",
      "k =  40 Acc= 0.5409836065573771\n",
      "k =  50 Acc= 0.5409836065573771\n",
      "k =  60 Acc= 0.5409836065573771\n",
      "k =  75 Acc= 0.5409836065573771\n",
      "k =  90 Acc= 0.5409836065573771\n",
      "k =  100 Acc= 0.5409836065573771\n",
      "k =  150 Acc= 0.5409836065573771\n",
      "k =  200 Acc= 0.5409836065573771\n"
     ]
    }
   ],
   "source": [
    "poss_k = [1,3, 8, 13,17, 25, 30, 40, 50, 60, 75, 90, 100, 150, 200]\n",
    "# poss_k = [30]\n",
    "\n",
    "acc_k = np.zeros(len(poss_k))\n",
    "\n",
    "# this takes a few mins ... would probably change the classifier to take a vector of k's to make it faster\n",
    "acc_indx = 0\n",
    "for k in poss_k:\n",
    "\n",
    "    predictions = X_test.apply( lambda x : knn_class(X_train_droped.copy(), k, target_col, x.drop(index=target_col)), axis=1)\n",
    "    \n",
    "    correct = np.sum(predictions == X_test[target_col])\n",
    "    acc_k[acc_indx] = correct/len(predictions)\n",
    "    \n",
    "    print(\"k = \", k , \"Acc=\", acc_k[acc_indx])\n",
    "    acc_indx = acc_indx + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poss_k,acc_k,'g-*')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy on Test Set for Varied k')\n",
    "plt.grid('on')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use k = 30 as it generates the best Acc.  \n",
    "\n",
    "We just have 303 samples here \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using sklearn.neighbors.KNeighborsClassifier\n",
    "\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\n",
    "    \n",
    "First we prepare the data for the Scikit-learn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPain</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.383562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.625954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>1</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.098174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.557252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>0</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.339623</td>\n",
       "      <td>0.123288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.603053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.547170</td>\n",
       "      <td>0.221461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.839695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.386792</td>\n",
       "      <td>0.287671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.770992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.320755</td>\n",
       "      <td>0.294521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.687023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0.604167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.488584</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.770992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>1</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.283019</td>\n",
       "      <td>0.308219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1</td>\n",
       "      <td>0.729167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.292453</td>\n",
       "      <td>0.417808</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.622642</td>\n",
       "      <td>0.401826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.694656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Target       Age  Sex  ChestPain    RestBP      Chol  Fbs  RestECG  \\\n",
       "11       0  0.562500  0.0   1.000000  0.433962  0.383562  0.0      1.0   \n",
       "292      1  0.312500  1.0   0.333333  0.245283  0.098174  0.0      0.0   \n",
       "269      0  0.270833  1.0   0.666667  0.339623  0.123288  0.0      0.0   \n",
       "268      1  0.229167  1.0   0.333333  0.547170  0.221461  0.0      0.0   \n",
       "94       0  0.708333  0.0   0.666667  0.386792  0.287671  0.0      1.0   \n",
       "..     ...       ...  ...        ...       ...       ...  ...      ...   \n",
       "95       1  0.479167  1.0   0.333333  0.320755  0.294521  0.0      0.0   \n",
       "26       0  0.604167  0.0   0.666667  0.245283  0.488584  0.0      0.0   \n",
       "259      1  0.583333  1.0   1.000000  0.283019  0.308219  0.0      0.0   \n",
       "143      1  0.729167  1.0   0.666667  0.292453  0.417808  0.0      0.0   \n",
       "42       0  0.875000  0.0   1.000000  0.622642  0.401826  0.0      0.0   \n",
       "\n",
       "        MaxHR  \n",
       "11   0.625954  \n",
       "292  0.557252  \n",
       "269  0.603053  \n",
       "268  0.839695  \n",
       "94   0.770992  \n",
       "..        ...  \n",
       "95   0.687023  \n",
       "26   0.770992  \n",
       "259  0.534351  \n",
       "143  0.458015  \n",
       "42   0.694656  \n",
       "\n",
       "[61 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPain</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.666667</td>\n",
       "      <td>0.811321</td>\n",
       "      <td>0.337900</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age  Sex  ChestPain    RestBP      Chol  Fbs  RestECG     MaxHR\n",
       "102  0.583333  0.0   0.333333  0.320755  0.404110  0.0      1.0  0.671756\n",
       "261  0.604167  0.0   1.000000  0.396226  0.440639  1.0      1.0  0.618321\n",
       "228  0.520833  1.0   0.333333  0.150943  0.182648  0.0      1.0  0.282443\n",
       "288  0.562500  1.0   1.000000  0.339623  0.216895  0.0      1.0  0.702290\n",
       "78   0.395833  1.0   1.000000  0.339623  0.271689  0.0      1.0  0.832061\n",
       "..        ...  ...        ...       ...       ...  ...      ...       ...\n",
       "106  0.625000  1.0   0.333333  0.433962  0.116438  0.0      0.0  0.694656\n",
       "83   0.812500  1.0   0.666667  0.811321  0.337900  1.0      1.0  0.603053\n",
       "17   0.520833  1.0   0.333333  0.433962  0.257991  0.0      0.0  0.679389\n",
       "230  0.479167  0.0   0.666667  0.396226  0.159817  0.0      1.0  0.748092\n",
       "98   0.479167  1.0   1.000000  0.377358  0.171233  0.0      0.0  0.664122\n",
       "\n",
       "[242 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.drop(['Target'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrain=np.array(y_train).astype('int')\n",
    "ytest=np.array(y_test).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc= 0.6229508196721312\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "neigh.fit(X_train.drop(['Target'], axis=1), ytrain)\n",
    "\n",
    "predictions= neigh.predict(X_test.drop(['Target'], axis=1))\n",
    "\n",
    "correct = np.sum(predictions == ytest)\n",
    "\n",
    "print(\"Acc=\", correct/len(predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "def knn(k, X_train, X_test, ytrain, ytest):\n",
    "    neigh = KNeighborsClassifier(n_neighbors=k)\n",
    "    neigh.fit(X_train, ytrain)\n",
    "    predictions= neigh.predict(X_test)\n",
    "    correct = np.sum(predictions == ytest)\n",
    "    return correct/len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k =  1 Acc= 0.6229508196721312\n",
      "k =  3 Acc= 0.6229508196721312\n",
      "k =  8 Acc= 0.639344262295082\n",
      "k =  13 Acc= 0.7377049180327869\n",
      "k =  17 Acc= 0.7213114754098361\n",
      "k =  25 Acc= 0.7213114754098361\n",
      "k =  30 Acc= 0.7540983606557377\n",
      "k =  40 Acc= 0.7049180327868853\n",
      "k =  50 Acc= 0.7377049180327869\n",
      "k =  60 Acc= 0.7213114754098361\n",
      "k =  75 Acc= 0.7377049180327869\n",
      "k =  90 Acc= 0.7377049180327869\n",
      "k =  100 Acc= 0.7540983606557377\n",
      "k =  150 Acc= 0.7540983606557377\n",
      "k =  200 Acc= 0.6065573770491803\n"
     ]
    }
   ],
   "source": [
    "poss_k = [1,3, 8, 13,17, 25,30, 40, 50, 60, 75, 90, 100, 150, 200]\n",
    "acc_k = np.zeros(len(poss_k))\n",
    "\n",
    "# this takes a few mins ... would probably change the classifier to take a vector of k's to make it faster\n",
    "acc_indx = 0\n",
    "\n",
    "for k in poss_k:\n",
    "    acc_k[acc_indx] = knn(k, X_train.drop(['Target'], axis=1), X_test.drop(['Target'], axis=1), ytrain, ytest)\n",
    "    print(\"k = \", k , \"Acc=\", acc_k[acc_indx])\n",
    "    acc_indx = acc_indx + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poss_k,acc_k,'g-*')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy on Test Set for Varied k')\n",
    "plt.grid('on')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}