04-FEATURE ENGINEERING.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "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 feature engineering.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[{'not':85,'kardes':4,'ders':'mat'},\n",
    "     {'not':70,'kardes':3,'ders':'ing'},\n",
    "     {'not':65,'kardes':3,'ders':'mat'},\n",
    "     {'not':60,'kardes':2,'ders':'fiz'}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "vek=DictVectorizer(sparse=False,dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  1,  4, 85],\n",
       "       [ 0,  1,  0,  3, 70],\n",
       "       [ 0,  0,  1,  3, 65],\n",
       "       [ 1,  0,  0,  2, 60]], dtype=int32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vek.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ders=fiz', 'ders=ing', 'ders=mat', 'kardes', 'not']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vek.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4x5 sparse matrix of type '<class 'numpy.int32'>'\n",
       "\twith 12 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vek=DictVectorizer(sparse=True,dtype=int)\n",
    "vek.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri=['hava iyi',\n",
    "     'iyi insan',\n",
    "     'hava bozuk']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "vek=CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3x4 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 6 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=vek.fit_transform(veri)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>bozuk</th>\n",
       "      <th>hava</th>\n",
       "      <th>insan</th>\n",
       "      <th>iyi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bozuk  hava  insan  iyi\n",
       "0      0     1      0    1\n",
       "1      0     0      1    1\n",
       "2      1     1      0    0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X.toarray(),columns=vek.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([1,2,3,4,5])\n",
    "y=np.array([5,3,1,2,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e292774588>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jXmL7sp3nkt4nqX0VWunvxcp+y7s7/BJibd+sUUR8WtJXtH139nlJP5T00SHIdLukP7D9hqSmpDvK/gfcckTSRySttq6PStKfS/rFc7INfL4K5qpizi6XdL/tEW1/o/hiRDxq+68kLUfEI9r+hvMvtp/X9tnjHSVnKprrj23fKumNVq47B5DrpwzBXBXJVcVcvUvSw61zkX2SHoyIx23/vjS49yKfnASAZIb5UgkAoAOKGwCSobgBIBmKGwCSobgBIBmKGwCSobgBIBmKGwCS+T+7W0TFpy2RvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=x[:,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression().fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_fit=model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e293988630>]"
      ]
     },
     "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.scatter(x,y)\n",
    "plt.plot(x,y_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pol=PolynomialFeatures(degree=3, include_bias=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   1.,   1.],\n",
       "       [  2.,   4.,   8.],\n",
       "       [  3.,   9.,  27.],\n",
       "       [  4.,  16.,  64.],\n",
       "       [  5.,  25., 125.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2=pol.fit_transform(X)\n",
    "X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression().fit(X2,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_fit=model.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e293f79550>]"
      ]
     },
     "execution_count": 36,
     "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,y_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=np.array([[1,nan,3],\n",
    "          [5,6,9],\n",
    "          [4,5,2],\n",
    "          [4,6,nan],\n",
    "          [9,8,1]])\n",
    "y=np.array([10,13,-2,7,-6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp=Imputer(strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2=imp.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.  , 6.25, 3.  ],\n",
       "       [5.  , 6.  , 9.  ],\n",
       "       [4.  , 5.  , 2.  ],\n",
       "       [4.  , 6.  , 3.75],\n",
       "       [9.  , 8.  , 1.  ]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.59852491, 13.45464873, -1.31399837,  4.99263952, -5.7318148 ])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression().fit(X2,y)\n",
    "model.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=make_pipeline(Imputer(strategy='mean'),\n",
    "                   PolynomialFeatures(degree=2),\n",
    "                   LinearRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 13 -2  7 -6]\n",
      "[10. 13. -2.  7. -6.]\n"
     ]
    }
   ],
   "source": [
    "model.fit(X,y)\n",
    "print(y)\n",
    "print(model.predict(X))"
   ]
  },
  {
   "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
}