KNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Nearest Neighbors (KNN) Algorithm\n",
    "\n",
    "## Step 1: Importing the necessary library and dataset\n",
    "We start by importing the pandas library,and use the `pd.read_csv()` function to read the CSV file 'framinghamCVD_cleaned.csv' and store the data in a DataFrame `df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('Dataset_cleaned.csv')\n",
    "\n",
    "df_target_0 = df[df['TenYearCHD'] == 0] \n",
    "df_target_1 = df[df['TenYearCHD'] == 1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Filtering and Data Splitting\n",
    "We filter the DataFrame `df` based on the 'TenYearCHD' column values. Rows where 'TenYearCHD' is 0 are stored in `df_target_0` and rows where 'TenYearCHD' is 1 are stored in `df_target_1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Rows :  2924\n",
      "Testing Data Rows :  732\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "X=df.drop(['TenYearCHD'],axis='columns')\n",
    "y=df.TenYearCHD\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "print('Training Data Rows : ',len(X_train))\n",
    "print('Testing Data Rows : ',len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Define a range of k values to try\n",
    "k_values = list(range(1, 21))\n",
    "\n",
    "# Initialize empty lists to store accuracy scores\n",
    "accuracy_scores = []\n",
    "\n",
    "# Evaluate kNN for different values of k\n",
    "for k in k_values:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    y_pred = knn.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracy_scores.append(accuracy)\n",
    "\n",
    "# Plot accuracy scores against k values\n",
    "plt.plot(k_values, accuracy_scores, marker='o')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy vs. k')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "at k=7.5 we get the most accuracy so we train the model with k=7.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-9 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-9 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-9 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-9 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=8)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=8)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=8)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=8)\n",
    "\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 83.46994535519126 %\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy*100,\"%\")"
   ]
  }
 ],
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