iris_ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6cd6dcb9",
   "metadata": {},
   "source": [
    "# Import Iris Dataset from sklearn.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "97fc2632",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1538ea71",
   "metadata": {},
   "source": [
    "### Assign variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5476dd28",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "feature_names = iris.feature_names\n",
    "target_names = iris.target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444ff1ba",
   "metadata": {},
   "source": [
    "### Create a train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cd026694",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120, 4)\n",
      "(30, 4)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7807c853",
   "metadata": {},
   "source": [
    "### Model: KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3a59c4f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=4)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "2b8f23e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2a0d8286",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.accuracy_score(y_test, y_pred)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a625eed4",
   "metadata": {},
   "source": [
    "### Predict new sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e2b0d774",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions:  ['versicolor', 'virginica']\n"
     ]
    }
   ],
   "source": [
    "sample = [[3,5,4,2], [2,3,5,4]]\n",
    "predictions = knn.predict(sample)\n",
    "pred_species = [iris.target_names[p] for p in predictions]\n",
    "print(\"predictions: \", pred_species)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e996200",
   "metadata": {},
   "source": [
    "### Save Model using joblib\n",
    "This is required on large scale uses in an implementation where model doesn't have to be trained over and over again as this takes time and alot of computing power. In ML applications for web/mobile, these saved models can be implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cb64a0b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mlbrain.joblib']"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(knn, 'mlbrain.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f6c4d1d1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = load('mlbrain.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f8761e7",
   "metadata": {},
   "source": [
    "### Test saved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e19192ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions:  ['versicolor', 'virginica']\n"
     ]
    }
   ],
   "source": [
    "model.predict(X_test)\n",
    "sample = [[3,5,4,2], [2,3,5,4]]\n",
    "predictions = model.predict(sample)\n",
    "pred_species = [iris.target_names[p] for p in predictions]\n",
    "print(\"predictions: \", pred_species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3cfca1a",
   "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": 5
}