Project - KNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This little project is about using the KNN to finding rocks or mines using the sonar data set.\n",
    "Sonar (sound navigation ranging) is a technique that employs sound propagation to navigate, communicate with, or detect things on or below the surface of the water, such as other vessels. It is typically used underwater, as in submarine navigation.\n",
    "The data collection includes the measurements for responses to 60 different sonar frequencies used against a known minefield (and known rocks). These frequencies bear the name of the item they were blasting sound at (either a rock or a mine).\n",
    "Our main objective is to develop a machine-learning model that can distinguish between a mine and a rock based on the responses to the 60 different sonar frequencies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filePath = 'data/sonar.all-data.csv'\n",
    "data = pd.read_csv(filePath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Freq_1</th>\n",
       "      <th>Freq_2</th>\n",
       "      <th>Freq_3</th>\n",
       "      <th>Freq_4</th>\n",
       "      <th>Freq_5</th>\n",
       "      <th>Freq_6</th>\n",
       "      <th>Freq_7</th>\n",
       "      <th>Freq_8</th>\n",
       "      <th>Freq_9</th>\n",
       "      <th>Freq_10</th>\n",
       "      <th>...</th>\n",
       "      <th>Freq_52</th>\n",
       "      <th>Freq_53</th>\n",
       "      <th>Freq_54</th>\n",
       "      <th>Freq_55</th>\n",
       "      <th>Freq_56</th>\n",
       "      <th>Freq_57</th>\n",
       "      <th>Freq_58</th>\n",
       "      <th>Freq_59</th>\n",
       "      <th>Freq_60</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>0.0428</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0986</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>0.2872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.2280</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.6194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Freq_1  Freq_2  Freq_3  Freq_4  Freq_5  Freq_6  Freq_7  Freq_8  Freq_9  \\\n",
       "0  0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n",
       "1  0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   \n",
       "2  0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   \n",
       "3  0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   \n",
       "4  0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n",
       "\n",
       "   Freq_10  ...  Freq_52  Freq_53  Freq_54  Freq_55  Freq_56  Freq_57  \\\n",
       "0   0.2111  ...   0.0027   0.0065   0.0159   0.0072   0.0167   0.0180   \n",
       "1   0.2872  ...   0.0084   0.0089   0.0048   0.0094   0.0191   0.0140   \n",
       "2   0.6194  ...   0.0232   0.0166   0.0095   0.0180   0.0244   0.0316   \n",
       "3   0.1264  ...   0.0121   0.0036   0.0150   0.0085   0.0073   0.0050   \n",
       "4   0.4459  ...   0.0031   0.0054   0.0105   0.0110   0.0015   0.0072   \n",
       "\n",
       "   Freq_58  Freq_59  Freq_60  Label  \n",
       "0   0.0084   0.0090   0.0032      R  \n",
       "1   0.0049   0.0052   0.0044      R  \n",
       "2   0.0164   0.0095   0.0078      R  \n",
       "3   0.0044   0.0040   0.0117      R  \n",
       "4   0.0048   0.0107   0.0094      R  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(data.corr(),cmap='coolwarm');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 5 correlated frequencies with the target\\label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Target'] = data['Label'].map({'R':0,'M':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Freq_45    0.339406\n",
       "Freq_10    0.341142\n",
       "Freq_49    0.351312\n",
       "Freq_12    0.392245\n",
       "Freq_11    0.432855\n",
       "Target     1.000000\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs(data.corr()['Target']).sort_values().tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train | Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['Target','Label'],axis=1)\n",
    "y = data['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cv, X_test, y_cv, y_test = train_test_split(X, y, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## StandardScaler and KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "operations = [('scaler',scaler),('knn',knn)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(operations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing a grid-search with the pipeline to test various values of k and report back the best performing parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_values = list(range(1,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'knn__n_neighbors': k_values}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_cv_classifier = GridSearchCV(pipe,param_grid,cv=5,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                                       ('knn', KNeighborsClassifier())]),\n",
       "             param_grid={'knn__n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
       "                                              12, 13, 14, 15, 16, 17, 18, 19,\n",
       "                                              20, 21, 22, 23, 24, 25, 26, 27,\n",
       "                                              28, 29]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_cv_classifier.fit(X_cv,y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'memory': None,\n",
       " 'steps': [('scaler', StandardScaler()),\n",
       "  ('knn', KNeighborsClassifier(n_neighbors=1))],\n",
       " 'verbose': False,\n",
       " 'scaler': StandardScaler(),\n",
       " 'knn': KNeighborsClassifier(n_neighbors=1),\n",
       " 'scaler__copy': True,\n",
       " 'scaler__with_mean': True,\n",
       " 'scaler__with_std': True,\n",
       " 'knn__algorithm': 'auto',\n",
       " 'knn__leaf_size': 30,\n",
       " 'knn__metric': 'minkowski',\n",
       " 'knn__metric_params': None,\n",
       " 'knn__n_jobs': None,\n",
       " 'knn__n_neighbors': 1,\n",
       " 'knn__p': 2,\n",
       " 'knn__weights': 'uniform'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_cv_classifier.best_estimator_.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84537696, 0.78065434, 0.77524893, 0.75917496, 0.75931721,\n",
       "       0.74822191, 0.75945946, 0.71664296, 0.7113798 , 0.68421053,\n",
       "       0.70042674, 0.68435277, 0.68449502, 0.67908962, 0.69530583,\n",
       "       0.68990043, 0.7113798 , 0.70042674, 0.72204836, 0.67908962,\n",
       "       0.70071124, 0.69530583, 0.69530583, 0.68463727, 0.68477952,\n",
       "       0.67923186, 0.67411095, 0.65775249, 0.6685633 ])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_cv_classifier.cv_results_['mean_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "scores = full_cv_classifier.cv_results_['mean_test_score']\n",
    "plt.plot(k_values,scores,'o-')\n",
    "plt.xlabel(\"K\")\n",
    "plt.ylabel(\"Accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = full_cv_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12,  1],\n",
       "       [ 1,  7]], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           M       0.92      0.92      0.92        13\n",
      "           R       0.88      0.88      0.88         8\n",
      "\n",
      "    accuracy                           0.90        21\n",
      "   macro avg       0.90      0.90      0.90        21\n",
      "weighted avg       0.90      0.90      0.90        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## THE END"
   ]
  },
  {
   "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
}