03_multiple_linear_regression.ipynb

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   "source": [
    "# Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pOyqYHTk_Q57"
   },
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T_YHJjnD_Tja"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vgC61-ah_WIz"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
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   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UrxyEKGn_ez7"
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   "outputs": [],
   "source": [
    "dataset = pd.read_csv('50_Startups.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values"
   ]
  },
  {
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     "text": [
      "[[165349.2 136897.8 471784.1 'New York']\n",
      " [162597.7 151377.59 443898.53 'California']\n",
      " [153441.51 101145.55 407934.54 'Florida']\n",
      " [144372.41 118671.85 383199.62 'New York']\n",
      " [142107.34 91391.77 366168.42 'Florida']\n",
      " [131876.9 99814.71 362861.36 'New York']\n",
      " [134615.46 147198.87 127716.82 'California']\n",
      " [130298.13 145530.06 323876.68 'Florida']\n",
      " [120542.52 148718.95 311613.29 'New York']\n",
      " [123334.88 108679.17 304981.62 'California']\n",
      " [101913.08 110594.11 229160.95 'Florida']\n",
      " [100671.96 91790.61 249744.55 'California']\n",
      " [93863.75 127320.38 249839.44 'Florida']\n",
      " [91992.39 135495.07 252664.93 'California']\n",
      " [119943.24 156547.42 256512.92 'Florida']\n",
      " [114523.61 122616.84 261776.23 'New York']\n",
      " [78013.11 121597.55 264346.06 'California']\n",
      " [94657.16 145077.58 282574.31 'New York']\n",
      " [91749.16 114175.79 294919.57 'Florida']\n",
      " [86419.7 153514.11 0.0 'New York']\n",
      " [76253.86 113867.3 298664.47 'California']\n",
      " [78389.47 153773.43 299737.29 'New York']\n",
      " [73994.56 122782.75 303319.26 'Florida']\n",
      " [67532.53 105751.03 304768.73 'Florida']\n",
      " [77044.01 99281.34 140574.81 'New York']\n",
      " [64664.71 139553.16 137962.62 'California']\n",
      " [75328.87 144135.98 134050.07 'Florida']\n",
      " [72107.6 127864.55 353183.81 'New York']\n",
      " [66051.52 182645.56 118148.2 'Florida']\n",
      " [65605.48 153032.06 107138.38 'New York']\n",
      " [61994.48 115641.28 91131.24 'Florida']\n",
      " [61136.38 152701.92 88218.23 'New York']\n",
      " [63408.86 129219.61 46085.25 'California']\n",
      " [55493.95 103057.49 214634.81 'Florida']\n",
      " [46426.07 157693.92 210797.67 'California']\n",
      " [46014.02 85047.44 205517.64 'New York']\n",
      " [28663.76 127056.21 201126.82 'Florida']\n",
      " [44069.95 51283.14 197029.42 'California']\n",
      " [20229.59 65947.93 185265.1 'New York']\n",
      " [38558.51 82982.09 174999.3 'California']\n",
      " [28754.33 118546.05 172795.67 'California']\n",
      " [27892.92 84710.77 164470.71 'Florida']\n",
      " [23640.93 96189.63 148001.11 'California']\n",
      " [15505.73 127382.3 35534.17 'New York']\n",
      " [22177.74 154806.14 28334.72 'California']\n",
      " [1000.23 124153.04 1903.93 'New York']\n",
      " [1315.46 115816.21 297114.46 'Florida']\n",
      " [0.0 135426.92 0.0 'California']\n",
      " [542.05 51743.15 0.0 'New York']\n",
      " [0.0 116983.8 45173.06 'California']]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VadrvE7s_lS9"
   },
   "source": [
    "## Encoding categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wV3fD1mbAvsh"
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder='passthrough')\n",
    "X = np.array(ct.fit_transform(X))"
   ]
  },
  {
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   "execution_count": 5,
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     "text": [
      "[[0.0 0.0 1.0 165349.2 136897.8 471784.1]\n",
      " [1.0 0.0 0.0 162597.7 151377.59 443898.53]\n",
      " [0.0 1.0 0.0 153441.51 101145.55 407934.54]\n",
      " [0.0 0.0 1.0 144372.41 118671.85 383199.62]\n",
      " [0.0 1.0 0.0 142107.34 91391.77 366168.42]\n",
      " [0.0 0.0 1.0 131876.9 99814.71 362861.36]\n",
      " [1.0 0.0 0.0 134615.46 147198.87 127716.82]\n",
      " [0.0 1.0 0.0 130298.13 145530.06 323876.68]\n",
      " [0.0 0.0 1.0 120542.52 148718.95 311613.29]\n",
      " [1.0 0.0 0.0 123334.88 108679.17 304981.62]\n",
      " [0.0 1.0 0.0 101913.08 110594.11 229160.95]\n",
      " [1.0 0.0 0.0 100671.96 91790.61 249744.55]\n",
      " [0.0 1.0 0.0 93863.75 127320.38 249839.44]\n",
      " [1.0 0.0 0.0 91992.39 135495.07 252664.93]\n",
      " [0.0 1.0 0.0 119943.24 156547.42 256512.92]\n",
      " [0.0 0.0 1.0 114523.61 122616.84 261776.23]\n",
      " [1.0 0.0 0.0 78013.11 121597.55 264346.06]\n",
      " [0.0 0.0 1.0 94657.16 145077.58 282574.31]\n",
      " [0.0 1.0 0.0 91749.16 114175.79 294919.57]\n",
      " [0.0 0.0 1.0 86419.7 153514.11 0.0]\n",
      " [1.0 0.0 0.0 76253.86 113867.3 298664.47]\n",
      " [0.0 0.0 1.0 78389.47 153773.43 299737.29]\n",
      " [0.0 1.0 0.0 73994.56 122782.75 303319.26]\n",
      " [0.0 1.0 0.0 67532.53 105751.03 304768.73]\n",
      " [0.0 0.0 1.0 77044.01 99281.34 140574.81]\n",
      " [1.0 0.0 0.0 64664.71 139553.16 137962.62]\n",
      " [0.0 1.0 0.0 75328.87 144135.98 134050.07]\n",
      " [0.0 0.0 1.0 72107.6 127864.55 353183.81]\n",
      " [0.0 1.0 0.0 66051.52 182645.56 118148.2]\n",
      " [0.0 0.0 1.0 65605.48 153032.06 107138.38]\n",
      " [0.0 1.0 0.0 61994.48 115641.28 91131.24]\n",
      " [0.0 0.0 1.0 61136.38 152701.92 88218.23]\n",
      " [1.0 0.0 0.0 63408.86 129219.61 46085.25]\n",
      " [0.0 1.0 0.0 55493.95 103057.49 214634.81]\n",
      " [1.0 0.0 0.0 46426.07 157693.92 210797.67]\n",
      " [0.0 0.0 1.0 46014.02 85047.44 205517.64]\n",
      " [0.0 1.0 0.0 28663.76 127056.21 201126.82]\n",
      " [1.0 0.0 0.0 44069.95 51283.14 197029.42]\n",
      " [0.0 0.0 1.0 20229.59 65947.93 185265.1]\n",
      " [1.0 0.0 0.0 38558.51 82982.09 174999.3]\n",
      " [1.0 0.0 0.0 28754.33 118546.05 172795.67]\n",
      " [0.0 1.0 0.0 27892.92 84710.77 164470.71]\n",
      " [1.0 0.0 0.0 23640.93 96189.63 148001.11]\n",
      " [0.0 0.0 1.0 15505.73 127382.3 35534.17]\n",
      " [1.0 0.0 0.0 22177.74 154806.14 28334.72]\n",
      " [0.0 0.0 1.0 1000.23 124153.04 1903.93]\n",
      " [0.0 1.0 0.0 1315.46 115816.21 297114.46]\n",
      " [1.0 0.0 0.0 0.0 135426.92 0.0]\n",
      " [0.0 0.0 1.0 542.05 51743.15 0.0]\n",
      " [1.0 0.0 0.0 0.0 116983.8 45173.06]]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WemVnqgeA70k"
   },
   "source": [
    "## Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kb_v_ae-A-20"
   },
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k-McZVsQBINc"
   },
   "source": [
    "## Training the Multiple Linear Regression model on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "colab_type": "code",
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     "timestamp": 1586353664008,
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   "outputs": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xNkXL1YQBiBT"
   },
   "source": [
    "## Predicting the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    "colab": {
     "base_uri": "https://localhost:8080/",
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    "colab_type": "code",
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     "status": "ok",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[103015.2  103282.38]\n",
      " [132582.28 144259.4 ]\n",
      " [132447.74 146121.95]\n",
      " [ 71976.1   77798.83]\n",
      " [178537.48 191050.39]\n",
      " [116161.24 105008.31]\n",
      " [ 67851.69  81229.06]\n",
      " [ 98791.73  97483.56]\n",
      " [113969.44 110352.25]\n",
      " [167921.07 166187.94]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = regressor.predict(X_test)\n",
    "np.set_printoptions(precision=2)\n",
    "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
   ]
  }
 ],
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   "name": "Multiple Linear Regression",
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