03-Scikit-Learn Part 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-Learn Part 2"
   ]
  },
  {
   "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 scikit-learn.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris=load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_2D=model.transform(X_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris['PCA1']=X_2D[:,0]\n",
    "iris['PCA2']=X_2D[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>-2.684126</td>\n",
       "      <td>0.319397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>-2.714142</td>\n",
       "      <td>-0.177001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>-2.888991</td>\n",
       "      <td>-0.144949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>-2.745343</td>\n",
       "      <td>-0.318299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>-2.728717</td>\n",
       "      <td>0.326755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species      PCA1  \\\n",
       "0           5.1          3.5           1.4          0.2  setosa -2.684126   \n",
       "1           4.9          3.0           1.4          0.2  setosa -2.714142   \n",
       "2           4.7          3.2           1.3          0.2  setosa -2.888991   \n",
       "3           4.6          3.1           1.5          0.2  setosa -2.745343   \n",
       "4           5.0          3.6           1.4          0.2  setosa -2.728717   \n",
       "\n",
       "       PCA2  \n",
       "0  0.319397  \n",
       "1 -0.177001  \n",
       "2 -0.144949  \n",
       "3 -0.318299  \n",
       "4  0.326755  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x285b0a9a9e8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 444.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('PCA1','PCA2', hue='species', data=iris, fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=GaussianMixture(n_components=3, covariance_type='full')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianMixture(covariance_type='full', init_params='kmeans', max_iter=100,\n",
       "        means_init=None, n_components=3, n_init=1, precisions_init=None,\n",
       "        random_state=None, reg_covar=1e-06, tol=0.001, verbose=0,\n",
       "        verbose_interval=10, warm_start=False, weights_init=None)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_gmm=model.predict(X_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris['kumeleme']=y_gmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "      <th>kumeleme</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>-2.684126</td>\n",
       "      <td>0.319397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>-2.714142</td>\n",
       "      <td>-0.177001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>-2.888991</td>\n",
       "      <td>-0.144949</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>-2.745343</td>\n",
       "      <td>-0.318299</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>-2.728717</td>\n",
       "      <td>0.326755</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species      PCA1  \\\n",
       "0           5.1          3.5           1.4          0.2  setosa -2.684126   \n",
       "1           4.9          3.0           1.4          0.2  setosa -2.714142   \n",
       "2           4.7          3.2           1.3          0.2  setosa -2.888991   \n",
       "3           4.6          3.1           1.5          0.2  setosa -2.745343   \n",
       "4           5.0          3.6           1.4          0.2  setosa -2.728717   \n",
       "\n",
       "       PCA2  kumeleme  \n",
       "0  0.319397         0  \n",
       "1 -0.177001         0  \n",
       "2 -0.144949         0  \n",
       "3 -0.318299         0  \n",
       "4  0.326755         0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x285b18eb2e8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1164.75x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('PCA1','PCA2', hue='species', data=iris,col='kumeleme', fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits=load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 8, 8)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes=plt.subplots(10,10, figsize=(8,8), subplot_kw={'xticks':[],'yticks':[]},\n",
    "                       gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(digits.target[i]),transform=ax.transAxes, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64)\n",
      "(1797,)\n"
     ]
    }
   ],
   "source": [
    "X=digits.data\n",
    "y=digits.target\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import Isomap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso=Isomap(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Isomap(eigen_solver='auto', max_iter=None, n_components=2, n_jobs=None,\n",
       "    n_neighbors=5, neighbors_algorithm='auto', path_method='auto', tol=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=iso.transform(X)\n",
    "data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x285b642be10>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data2[:,0],data2[:,1],c=digits.target, alpha=0.5,\n",
    "           cmap=plt.cm.get_cmap('tab10',10))\n",
    "plt.colorbar(label='digit etiket',ticks=range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_egitim,X_test, y_egitim, y_test=train_test_split(X,y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_egitim,y_egitim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_model=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat=confusion_matrix(y_test,y_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(91.68, 0.5, 'Gerçek Değer')"
      ]
     },
     "execution_count": 42,
     "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": [
    "sns.heatmap(mat,square=True, annot=True, cbar=False)\n",
    "plt.xlabel('Tahmin Değer')\n",
    "plt.ylabel('Gerçek Değer')"
   ]
  },
  {
   "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
}