11_polynomialReg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of bad fit plot line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# load data and assign x and y \n",
    "X= [89,43,36,36,95,10,66,34,38,20,26,29,48,64,6,5,36,66,72,40]\n",
    "y= [21,46,3,35,67,95,53,72,58,10,26,34,90,33,38,20,56,2,47,15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#look into it\n",
    "mymodel= np.poly1d(np.polyfit(X,y ,3))\n",
    "myline= np.linspace(2,95,100)\n",
    "plt.scatter(X,y)\n",
    "plt.plot(myline,mymodel(myline))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.009952707566680652\n"
     ]
    }
   ],
   "source": [
    "#r squared for bad fit\n",
    "from sklearn.metrics import r2_score\n",
    "X= [89,43,36,36,95,10,66,34,38,20,26,29,48,64,6,5,36,66,72,40]\n",
    "y= [21,46,3,35,67,95,53,72,58,10,26,34,90,33,38,20,56,2,47,15]\n",
    "model= np.poly1d(np.polyfit(X,y ,3))\n",
    "print(r2_score(y,model(X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# polynomial regression with numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#step 1 look in to data\n",
    "\n",
    "X = [1,2,3,5,6,7,8,9,10,12,13,14,15,16,178,19,21,22]\n",
    "y=[100,90,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]\n",
    "\n",
    "plt.scatter(X,y,color='green')\n",
    "plt.xlim(0,25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#step 2 draw the line\n",
    "mymodel= np.poly1d(np.polyfit(X,y ,3))\n",
    "myline= np.linspace(1,22,50)\n",
    "plt.scatter(X,y)\n",
    "plt.plot(myline,mymodel(myline))\n",
    "plt.xlim(0,25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7855684300264447\n"
     ]
    }
   ],
   "source": [
    "#step3 r-squared\n",
    "model= np.poly1d(np.polyfit(X,y ,3))\n",
    "print(r2_score(y,model(X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85.74417683003931\n"
     ]
    }
   ],
   "source": [
    "#step 4 prediction\n",
    "speed = mymodel(18)\n",
    "print(speed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "dataset= pd.read_csv('https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/position_salaries.csv')\n",
    "X= dataset.iloc[:, 1:2].values\n",
    "y= dataset.iloc[:, 2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Position</th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position  Level  Salary\n",
       "0   Business Analyst      1   45000\n",
       "1  Junior Consultant      2   50000\n",
       "2  Senior Consultant      3   60000\n",
       "3            Manager      4   80000\n",
       "4    Country Manager      5  110000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training set and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test =train_test_split(X,y,test_size=0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Linear Regression to thr dataset\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing the linear regression results\n",
    "\n",
    "def viz_linear():\n",
    "    plt.scatter(X,y,color=\"red\")\n",
    "    plt.plot(X,lin_reg.predict(X),color=\"blue\")\n",
    "    plt.title(\"Truth or Bluff(Linear Regression\")\n",
    "    plt.xlabel('Position Level')\n",
    "    plt.ylabel('Salary')\n",
    "    plt.show()\n",
    "    return\n",
    "viz_linear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fitting polynomial regression to the dataset\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_reg = PolynomialFeatures(degree=4)\n",
    "X_poly= poly_reg.fit_transform(X)\n",
    "pol_reg= LinearRegression()\n",
    "pol_reg.fit(X_poly,y)\n",
    "\n",
    "#visualizing the polynomial regression results\n",
    "\n",
    "def viz_polynomial():\n",
    "    plt.scatter(X,y,color=\"red\")\n",
    "    plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)),color=\"blue\")\n",
    "    plt.title(\"Truth or Bluff(Linear Regression)\")\n",
    "    plt.xlabel('Position Level')\n",
    "    plt.ylabel('Salary')\n",
    "    plt.show()\n",
    "    return\n",
    "viz_polynomial()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict a new result with linear regression\n",
    "pred_linear = lin_reg.predict([[11]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict a new result with polynomial regression\n",
    "pred_polynomial = pol_reg.predict(poly_reg.fit_transform([[11]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regressionresult =  [694333.33333333]\n",
      "Linear Regressionresult =  [1780833.3333332]\n",
      "Difference is = [-1086499.99999986]\n"
     ]
    }
   ],
   "source": [
    "print('Linear Regressionresult = ', pred_linear)\n",
    "print('Linear Regressionresult = ', pred_polynomial)\n",
    "\n",
    "print('Difference is =', pred_linear - pred_polynomial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "___\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## another example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>job</th>\n",
       "      <th>degree</th>\n",
       "      <th>salary_more_then_100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company                  job     degree  salary_more_then_100k\n",
       "0       google      sales executive  bachelors                      0\n",
       "1       google      sales executive    masters                      0\n",
       "2       google     business manager  bachelors                      1\n",
       "3       google     business manager    masters                      1\n",
       "4       google  computer programmer  bachelors                      0\n",
       "5       google  computer programmer    masters                      1\n",
       "6   abc pharma      sales executive    masters                      0\n",
       "7   abc pharma  computer programmer  bachelors                      0\n",
       "8   abc pharma     business manager  bachelors                      0\n",
       "9   abc pharma     business manager    masters                      1\n",
       "10    facebook      sales executive  bachelors                      1\n",
       "11    facebook      sales executive    masters                      1\n",
       "12    facebook     business manager  bachelors                      1\n",
       "13    facebook     business manager    masters                      1\n",
       "14    facebook  computer programmer  bachelors                      1\n",
       "15    facebook  computer programmer    masters                      1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#importing the dataset\n",
    "df = pd.read_csv(\"data/salaries.csv\")\n",
    "df[\"company\"]= df[\"company\"].replace({1:'google', 2:'abc pharma', 3:'facebook'})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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