MultipleLinearRegression_python.ipynb

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   "source": [
    "## Multiple Linear Regression with python\n",
    "This is a Multiple Linear Regression analysis using Concrete Slump Data.\n",
    "\n",
    "The UCI Machine Learning Repository has this dataset containing diferent variables (Cement, Slag, Fly ash, Water, SP, Coarse Aggr., Fine Aggr., SLUMP, FLOW, 28-day Compressive Strength). \n",
    "\n",
    "The dataset is maintained on their site, where it can be found by the title \"Concrete Slump Test Data Set\".\n",
    "\n",
    "<b>Citation:</b>\n",
    "Yeh, I-Cheng, \"Modeling slump flow of concrete using second-order regressions and artificial neural networks,\" Cement and Concrete Composites, Vol.29, No. 6, 474-480, 2007."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    932.0      \n",
       "4                                                 978.4      \n",
       "...                                                 ...      \n",
       "1025                                              870.1      \n",
       "1026                                              817.9      \n",
       "1027                                              892.4      \n",
       "1028                                              989.6      \n",
       "1029                                              864.5      \n",
       "\n",
       "      Fine Aggregate (component 7)(kg in a m^3 mixture)  Age (day)  \\\n",
       "0                                                 676.0         28   \n",
       "1                                                 676.0         28   \n",
       "2                                                 594.0        270   \n",
       "3                                                 594.0        365   \n",
       "4                                                 825.5        360   \n",
       "...                                                 ...        ...   \n",
       "1025                                              768.3         28   \n",
       "1026                                              813.4         28   \n",
       "1027                                              780.0         28   \n",
       "1028                                              788.9         28   \n",
       "1029                                              761.5         28   \n",
       "\n",
       "      Concrete compressive strength(MPa, megapascals)   \n",
       "0                                            79.986111  \n",
       "1                                            61.887366  \n",
       "2                                            40.269535  \n",
       "3                                            41.052780  \n",
       "4                                            44.296075  \n",
       "...                                                ...  \n",
       "1025                                         44.284354  \n",
       "1026                                         31.178794  \n",
       "1027                                         23.696601  \n",
       "1028                                         32.768036  \n",
       "1029                                         32.401235  \n",
       "\n",
       "[1030 rows x 9 columns]"
      ],
      "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>Cement (component 1)(kg in a m^3 mixture)</th>\n      <th>Blast Furnace Slag (component 2)(kg in a m^3 mixture)</th>\n      <th>Fly Ash (component 3)(kg in a m^3 mixture)</th>\n      <th>Water  (component 4)(kg in a m^3 mixture)</th>\n      <th>Superplasticizer (component 5)(kg in a m^3 mixture)</th>\n      <th>Coarse Aggregate  (component 6)(kg in a m^3 mixture)</th>\n      <th>Fine Aggregate (component 7)(kg in a m^3 mixture)</th>\n      <th>Age (day)</th>\n      <th>Concrete compressive strength(MPa, megapascals)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>540.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>162.0</td>\n      <td>2.5</td>\n      <td>1040.0</td>\n      <td>676.0</td>\n      <td>28</td>\n      <td>79.986111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>540.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>162.0</td>\n      <td>2.5</td>\n      <td>1055.0</td>\n      <td>676.0</td>\n      <td>28</td>\n      <td>61.887366</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>332.5</td>\n      <td>142.5</td>\n      <td>0.0</td>\n      <td>228.0</td>\n      <td>0.0</td>\n      <td>932.0</td>\n      <td>594.0</td>\n      <td>270</td>\n      <td>40.269535</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>332.5</td>\n      <td>142.5</td>\n      <td>0.0</td>\n      <td>228.0</td>\n      <td>0.0</td>\n      <td>932.0</td>\n      <td>594.0</td>\n      <td>365</td>\n      <td>41.052780</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>198.6</td>\n      <td>132.4</td>\n      <td>0.0</td>\n      <td>192.0</td>\n      <td>0.0</td>\n      <td>978.4</td>\n      <td>825.5</td>\n      <td>360</td>\n      <td>44.296075</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1025</th>\n      <td>276.4</td>\n      <td>116.0</td>\n      <td>90.3</td>\n      <td>179.6</td>\n      <td>8.9</td>\n      <td>870.1</td>\n      <td>768.3</td>\n      <td>28</td>\n      <td>44.284354</td>\n    </tr>\n    <tr>\n      <th>1026</th>\n      <td>322.2</td>\n      <td>0.0</td>\n      <td>115.6</td>\n      <td>196.0</td>\n      <td>10.4</td>\n      <td>817.9</td>\n      <td>813.4</td>\n      <td>28</td>\n      <td>31.178794</td>\n    </tr>\n    <tr>\n      <th>1027</th>\n      <td>148.5</td>\n      <td>139.4</td>\n      <td>108.6</td>\n      <td>192.7</td>\n      <td>6.1</td>\n      <td>892.4</td>\n      <td>780.0</td>\n      <td>28</td>\n      <td>23.696601</td>\n    </tr>\n    <tr>\n      <th>1028</th>\n      <td>159.1</td>\n      <td>186.7</td>\n      <td>0.0</td>\n      <td>175.6</td>\n      <td>11.3</td>\n      <td>989.6</td>\n      <td>788.9</td>\n      <td>28</td>\n      <td>32.768036</td>\n    </tr>\n    <tr>\n      <th>1029</th>\n      <td>260.9</td>\n      <td>100.5</td>\n      <td>78.3</td>\n      <td>200.6</td>\n      <td>8.6</td>\n      <td>864.5</td>\n      <td>761.5</td>\n      <td>28</td>\n      <td>32.401235</td>\n    </tr>\n  </tbody>\n</table>\n<p>1030 rows × 9 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "#importing data\n",
    "dataset = pd.read_excel('Concrete_Data.xls')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We are going to use the Cement and Concrete compressive Strength for this\n",
    "X = dataset.iloc[:,:-1].values\n",
    "y = dataset.iloc[:,-1].values\n",
    "#X = X.reshape(-1, 1)\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[ 540.     0.     0.  ... 1040.   676.    28. ]\n [ 540.     0.     0.  ... 1055.   676.    28. ]\n [ 332.5  142.5    0.  ...  932.   594.   270. ]\n ...\n [ 148.5  139.4  108.6 ...  892.4  780.    28. ]\n [ 159.1  186.7    0.  ...  989.6  788.9   28. ]\n [ 260.9  100.5   78.3 ...  864.5  761.5   28. ]]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[79.98611076]\n [61.88736576]\n [40.26953526]\n ...\n [23.69660064]\n [32.76803638]\n [32.40123514]]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the data\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#print(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#print(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "#Training the simple Linear Regression model on training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "71]\n [39.84 57.22]\n [23.21 22.53]\n [51.02 35.3 ]\n [36.47 31.38]\n [49.5  28.8 ]\n [37.59 37.72]\n [33.65 41.2 ]\n [42.53 39.38]\n [30.13 31.12]\n [33.94 28.99]\n [27.73 32.72]\n [31.15 26.23]\n [23.08 17.44]\n [19.78  8.  ]\n [16.7  18.75]\n [50.87 60.28]\n [24.26 37.27]\n [30.66 14.94]\n [24.31 33.06]\n [56.77 60.29]\n [16.5  17.34]\n [27.   26.77]\n [25.45 13.09]\n [26.25 32.25]\n [28.14 19.11]\n [53.97 56.34]\n [51.4  76.24]\n [35.83 42.14]\n [20.26 17.17]\n [48.87 49.9 ]\n [29.91 27.92]\n [42.27 55.02]\n [49.   29.55]\n [49.1  56.83]\n [53.32 33.69]\n [30.66 30.12]\n [31.79 33.42]\n [32.07 35.17]\n [46.36 35.1 ]\n [49.97 41.84]\n [35.28 38.77]\n [53.78 33.4 ]\n [53.   69.84]\n [29.07 23.22]\n [24.87 27.22]\n [20.63 24.45]\n [33.61 19.54]\n [20.85 13.36]\n [25.64 13.54]\n [64.19 73.3 ]\n [13.99 11.17]\n [41.16 33.76]\n [48.7  41.64]\n [26.03 31.97]\n [40.3  56.85]\n [60.89 60.2 ]\n [31.89 39.05]\n [60.92 59.09]\n [35.87 47.4 ]\n [21.73 21.65]\n [44.67 50.53]\n [19.32 11.58]\n [30.22 31.72]\n [26.53 23.7 ]\n [21.88  8.49]\n [24.68 30.23]\n [49.16 40.76]\n [32.41 12.37]\n [40.34 41.94]\n [50.74 54.1 ]\n [18.4  19.69]\n [39.87 55.83]\n [28.41 19.2 ]\n [33.3  34.67]\n [43.   50.73]\n [32.89 35.85]\n [28.68 39.66]\n [26.94  9.62]\n [52.09 55.55]\n [60.73 43.01]\n [40.11 29.59]\n [24.38  6.9 ]\n [37.55 24.4 ]\n [24.19 15.34]\n [21.54 27.87]\n [59.04 33.7 ]\n [33.6  37.43]\n [38.32 45.08]\n [34.7  44.61]\n [24.79 18.29]\n [22.59 24.28]\n [54.58 67.57]\n [21.94 25.18]\n [22.54 25.73]\n [30.41 34.24]\n [47.21 81.75]\n [34.47  7.75]\n [40.75 41.05]\n [35.43 36.8 ]\n [28.5  44.87]\n [16.96  8.54]\n [31.77 37.96]\n [40.41 46.23]\n [20.34 18.91]\n [27.36 33.95]\n [38.31 46.64]\n [47.08 28.1 ]\n [19.79 11.41]\n [38.02 48.72]\n [25.68 15.44]\n [37.23 42.92]\n [25.69 29.93]\n [56.77 60.29]\n [35.85 42.13]\n [59.58 53.69]\n [35.43 48.59]\n [29.07 37.4 ]\n [17.35 11.65]\n [25.71 26.92]\n [72.89 41.93]\n [24.51 18.  ]\n [48.07 46.23]\n [40.8  41.05]\n [33.19 46.25]\n [33.34 46.24]\n [22.62 16.26]\n [44.47 56.74]\n [53.96 51.02]\n [28.41 31.35]\n [35.78 17.82]\n [37.53 32.33]\n [55.89 29.79]\n [29.42 32.92]]\n"
     ]
    }
   ],
   "source": [
    "#Predicting the test set results\n",
    "y_pred = regressor.predict(X_test)\n",
    "np.set_printoptions(precision=2)  #two decimals\n",
    "print(np.concatenate((y_pred.reshape(len(y_pred),1),y_test.reshape(len(y_test),1)),1))  #reshape vertically, and join prediction and real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Median error = [7.92]\n"
     ]
    }
   ],
   "source": [
    "median_error = sum(abs(y_pred - y_test))/len(y_pred)\n",
    "print(f\"Median error = {median_error}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#Visualising the Trainin set results\n",
    "plt.scatter(range(0,len(y_pred)),y_pred, color = 'red', s=10)  #predictions result\n",
    "plt.scatter(range(0,len(y_pred)),y_test, color = 'blue', s=10)  #real result\n",
    "\n",
    "plt.title('Concrete compressive strength (Mpa) [red = predictions, blue = real] vs. Count')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Concrete compressive strength (Mpa)')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ]
}