Basic_approach_ML_sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes\n",
    "A general approach to some machine learning in python using sklearn follows:\n",
    "- import a required model\n",
    "- need a classifier\n",
    "- fit it or train with training dataset\n",
    "- create the vector of prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "#Simple Naive Bayes from sklearn.naive_bayees documentation\n",
    "import numpy as np\n",
    "X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
    "Y = np.array([1, 1, 1, 2, 2, 2])\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "clf = GaussianNB() # creates the classifier\n",
    "clf.fit(X, Y) # Fit it or train with training dataset\n",
    "pred = clf.predict([[-0.8, -1]]) # Create vector of predictions\n",
    "print(pred)# Print result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}