17-Principal Component Analysis (PCA).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis (PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to talk about PCA.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng=np.random.RandomState(1)\n",
    "X=np.dot(rng.rand(2,2),rng.randn(2,200)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x241783bfb38>"
      ]
     },
     "execution_count": 9,
     "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": [
    "plt.scatter(X[:,0],X[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca=PCA(n_components=2)\n",
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.94446029 -0.32862557]\n",
      " [-0.32862557  0.94446029]]\n",
      "[0.7625315 0.0184779]\n"
     ]
    }
   ],
   "source": [
    "print(pca.components_)\n",
    "print(pca.explained_variance_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca=PCA(n_components=1)\n",
    "pca.fit(X)\n",
    "X_pca=pca.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 2)\n",
      "(200, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X_pca.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2417adbb6a0>"
      ]
     },
     "execution_count": 15,
     "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": [
    "X_yeni=pca.inverse_transform(X_pca)\n",
    "plt.scatter(X[:,0],X[:,1],alpha=0.2)\n",
    "plt.scatter(X_yeni[:,0],X_yeni[:,1],alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits=load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca=PCA(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pca=pca.fit_transform(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca=PCA().fit(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2417b625c18>]"
      ]
     },
     "execution_count": 25,
     "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": [
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
 "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}