02-Scikit-Learn Part 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-Learn - Part 1"
   ]
  },
  {
   "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": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=sns.load_dataset('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": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_iris=iris.drop('species', axis=1)\n",
    "X_iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_iris=iris['species']\n",
    "y_iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng=np.random.RandomState(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d50f0c6710>"
      ]
     },
     "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": [
    "x=10*rng.rand(50)\n",
    "y=2*x-1+rng.randn(50)\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression(fit_intercept=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=x[:,np.newaxis]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.9776566])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9033107255311164"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_fit=np.linspace(-1,11)\n",
    "X_fit=x_fit[:,np.newaxis]\n",
    "y_fit=model.predict(X_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d510d336a0>]"
      ]
     },
     "execution_count": 18,
     "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,y)\n",
    "plt.plot(x_fit,y_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_egitim, X_test, y_egitim, y_test=train_test_split(X_iris, y_iris, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_egitim, y_egitim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_model=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_model)"
   ]
  },
  {
   "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
}