KNN-ML.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning features and label variables\n",
    "# First Feature\n",
    "weather=['Sunny','Sunny','Overcast','Rainy','Rainy','Rainy','Overcast','Sunny','Sunny',\n",
    "'Rainy','Sunny','Overcast','Overcast','Rainy']\n",
    "# Second Feature\n",
    "temp=['Hot','Hot','Hot','Mild','Cool','Cool','Cool','Mild','Cool','Mild','Mild','Mild','Hot','Mild']\n",
    "\n",
    "# Label or target varible\n",
    "play=['No','No','Yes','Yes','Yes','No','Yes','No','Yes','Yes','Yes','Yes','Yes','No']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2 0 1 1 1 0 2 2 1 2 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "# Import LabelEncoder\n",
    "from sklearn import preprocessing\n",
    "#creating labelEncoder\n",
    "le = preprocessing.LabelEncoder()\n",
    "# Converting string labels into numbers.\n",
    "weather_encoded=le.fit_transform(weather)\n",
    "print(weather_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting string labels into numbers\n",
    "temp_encoded=le.fit_transform(temp)\n",
    "label=le.fit_transform(play)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combinig weather and temp into single listof tuples\n",
    "features=list(zip(weather_encoded,temp_encoded))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# Train the model using the training sets\n",
    "model.fit(features,label)\n",
    "\n",
    "#Predict Output\n",
    "predicted= model.predict([[0,2]]) # 0:Overcast, 2:Mild\n",
    "print(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nIn the above example, you have given input [0,2], \\nwhere 0 means Overcast weather and 2 means Mild temperature. \\nModel predicts [1], which means play.\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "In the above example, you have given input [0,2], \n",
    "where 0 means Overcast weather and 2 means Mild temperature. \n",
    "Model predicts [1], which means play.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import scikit-learn dataset library\n",
    "from sklearn import datasets\n",
    "\n",
    "#Load dataset\n",
    "wine = datasets.load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\n"
     ]
    }
   ],
   "source": [
    "# print the names of the features\n",
    "print(wine.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['class_0' 'class_1' 'class_2']\n"
     ]
    }
   ],
   "source": [
    "# print the label species(class_0, class_1, class_2)\n",
    "print(wine.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.423e+01 1.710e+00 2.430e+00 1.560e+01 1.270e+02 2.800e+00 3.060e+00\n",
      "  2.800e-01 2.290e+00 5.640e+00 1.040e+00 3.920e+00 1.065e+03]\n",
      " [1.320e+01 1.780e+00 2.140e+00 1.120e+01 1.000e+02 2.650e+00 2.760e+00\n",
      "  2.600e-01 1.280e+00 4.380e+00 1.050e+00 3.400e+00 1.050e+03]\n",
      " [1.316e+01 2.360e+00 2.670e+00 1.860e+01 1.010e+02 2.800e+00 3.240e+00\n",
      "  3.000e-01 2.810e+00 5.680e+00 1.030e+00 3.170e+00 1.185e+03]\n",
      " [1.437e+01 1.950e+00 2.500e+00 1.680e+01 1.130e+02 3.850e+00 3.490e+00\n",
      "  2.400e-01 2.180e+00 7.800e+00 8.600e-01 3.450e+00 1.480e+03]\n",
      " [1.324e+01 2.590e+00 2.870e+00 2.100e+01 1.180e+02 2.800e+00 2.690e+00\n",
      "  3.900e-01 1.820e+00 4.320e+00 1.040e+00 2.930e+00 7.350e+02]]\n"
     ]
    }
   ],
   "source": [
    "# print the wine data (top 5 records)\n",
    "print(wine.data[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "# print the wine labels (0:Class_0, 1:Class_1, 2:Class_3)\n",
    "print(wine.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(178, 13)\n"
     ]
    }
   ],
   "source": [
    "# print data(feature)shape\n",
    "print(wine.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(178,)\n"
     ]
    }
   ],
   "source": [
    "# print target(or label)shape\n",
    "print(wine.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import train_test_split function\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3) # 70% training and 30% test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Let's build KNN classifier model for k=5.\"\"\"\n",
    "\n",
    "#Import knearest neighbors Classifier model\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#Create KNN Classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "#Train the model using the training sets\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6111111111111112\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=7, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Let's build KNN classifier model for k=7.\n",
    "\"\"\"\n",
    "\n",
    "#Import knearest neighbors Classifier model\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#Create KNN Classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=7)\n",
    "\n",
    "#Train the model using the training sets\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6111111111111112\n"
     ]
    }
   ],
   "source": [
    "#Let's again estimate, how accurately the classifier or model can predict the type of cultivars for k=7.\n",
    "\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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