Multiple+linear_regression.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression \n",
    "Goal\n",
    "Produce the best fit plane that willl minimize loss function for our dataset\n",
    "\n",
    "\n",
    "\n",
    "y = b0 +b1X1 +b2X2 + ...+bnXn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('50_Startup.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>NewYork</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.34</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend   Administration  Marketing       State     Profit\n",
       "0    165349.20       136897.80  471784.10    New York  192261.83\n",
       "1    162597.70       151377.59  443898.53  California  191792.06\n",
       "2    153441.51       101145.55  407934.54     Florida  191050.39\n",
       "3    144372.41       118671.85  383199.62     NewYork  182901.99\n",
       "4    142107.34        91391.34  366168.42     Florida  166187.94"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[: , :-1].values\n",
    "y = dataset.iloc[:, :-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[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, 'NewYork'],\n",
       "       [142107.34, 91391.34, 366168.42, 'Florida']], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[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, 'NewYork'],\n",
       "       [142107.34, 91391.34, 366168.42, 'Florida']], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x15ecf676310>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# categorical to convert  \n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct = ColumnTransformer(transformers= [('encoder', OneHotEncoder() , [3])], remainder= \"passthrough\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(ct.fit_transform(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(ct.fit_transform(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0, 0.0, 1.0, 0.0, 165349.2, 136897.8, 471784.1],\n",
       "       [1.0, 0.0, 0.0, 0.0, 162597.7, 151377.59, 443898.53],\n",
       "       [0.0, 1.0, 0.0, 0.0, 153441.51, 101145.55, 407934.54],\n",
       "       [0.0, 0.0, 0.0, 1.0, 144372.41, 118671.85, 383199.62],\n",
       "       [0.0, 1.0, 0.0, 0.0, 142107.34, 91391.34, 366168.42]], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 136897.8,\n",
       "        471784.1],\n",
       "       [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 151377.59,\n",
       "        443898.53],\n",
       "       [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 101145.55,\n",
       "        407934.54],\n",
       "       [1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 118671.85,\n",
       "        383199.62],\n",
       "       [1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 91391.34,\n",
       "        366168.42]], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "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": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 101145.55,\n",
       "        407934.54]], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 136897.8,\n",
       "        471784.1],\n",
       "       [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 151377.59,\n",
       "        443898.53],\n",
       "       [1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 118671.85,\n",
       "        383199.62],\n",
       "       [1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 91391.34,\n",
       "        366168.42]], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0, 0.0, 1.0, 0.0, 165349.2, 136897.8, 471784.1],\n",
       "       [1.0, 0.0, 0.0, 0.0, 162597.7, 151377.59, 443898.53],\n",
       "       [0.0, 0.0, 0.0, 1.0, 144372.41, 118671.85, 383199.62],\n",
       "       [0.0, 1.0, 0.0, 0.0, 142107.34, 91391.34, 366168.42]], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.62334562e-01,  8.13137495e-01,  5.46790569e-01,\n",
       "        -1.97593502e-01,  1.51041913e+05,  1.01145550e+05,\n",
       "         4.07934540e+05]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0, 1.0, 0.0, 0.0, 153441.51, 101145.55, 407934.54]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.set_printoptions(precision = 2)\n",
    "# y_pred = y_pred.reshape(len(y_pred),1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Profit = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.17e-01  1.17e-01  9.98e-02 -1.56e-01  0.00e+00 -6.12e-02  1.17e-01\n",
      "   9.98e-02 -6.12e-02 -1.56e-01  2.30e-05 -6.88e-06]\n",
      " [-9.98e-02  9.98e-02  8.49e-02 -1.33e-01  0.00e+00 -5.21e-02  9.98e-02\n",
      "   8.49e-02 -5.21e-02 -1.33e-01 -2.25e-05  2.80e-06]\n",
      " [ 6.12e-02 -6.12e-02 -5.21e-02  8.14e-02  0.00e+00  3.19e-02 -6.12e-02\n",
      "  -5.21e-02  3.19e-02  8.14e-02 -2.01e-05  1.65e-05]\n",
      " [ 1.56e-01 -1.56e-01 -1.33e-01  2.07e-01  0.00e+00  8.14e-02 -1.56e-01\n",
      "  -1.33e-01  8.14e-02  2.07e-01  1.95e-05 -1.25e-05]\n",
      " [-6.28e+02  6.28e+02  5.34e+02 -8.35e+02  0.00e+00 -3.28e+02  6.28e+02\n",
      "   5.34e+02 -3.28e+02 -8.35e+02  4.90e-02  2.15e-01]\n",
      " [-2.30e-05  2.30e-05 -2.25e-05  1.95e-05  0.00e+00 -2.01e-05  2.30e-05\n",
      "  -2.25e-05 -2.01e-05  1.95e-05  1.00e+00  1.75e-09]\n",
      " [ 6.88e-06 -6.88e-06  2.80e-06 -1.25e-05  0.00e+00  1.65e-05 -6.88e-06\n",
      "   2.80e-06  1.65e-05 -1.25e-05  1.75e-09  1.00e+00]]\n",
      "[ 3.31e-01  1.96e+00 -4.18e+00  2.89e+00  5.84e+04 -1.93e-04  1.99e-04]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.coef_)\n",
    "print(regressor.intercept_)"
   ]
  }
 ],
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