solution.ipynb

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   "metadata": {},
   "source": [
    "## Multivirate Linear Regression\n",
    "---"
   ]
  },
  {
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     "text": [
      "C:\\Users\\Temmie\\Anaconda03\\envs\\opencvdev\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "#import Libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn import preprocessing as prep\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VjVt7CPemd3s"
   },
   "source": [
    "### Data Preprocessing\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "63L_g6ramjgs"
   },
   "source": [
    "#### Preprocessing Steps\n",
    "- Read the data in the file\n",
    "- Delete the last column containing the car names\n",
    "- Fill empty values with the mean of the feature\n",
    "- Rename the column header names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3m0ajKfg8GzJ"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0  18.0          8         307.0       130.0  3504.0          12.0   \n",
       "1  15.0          8         350.0       165.0  3693.0          11.5   \n",
       "2  18.0          8         318.0       150.0  3436.0          11.0   \n",
       "3  16.0          8         304.0       150.0  3433.0          12.0   \n",
       "4  17.0          8         302.0       140.0  3449.0          10.5   \n",
       "\n",
       "   model year  origin  \n",
       "0          70       1  \n",
       "1          70       1  \n",
       "2          70       1  \n",
       "3          70       1  \n",
       "4          70       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"auto-mpg.data\"\n",
    "data = pd.read_csv(file_path, header = None, delimiter = '\\s+', low_memory = False, na_values = \"?\")\n",
    "data_frame = data.drop(columns = 8)\n",
    "data_frame = data_frame.fillna(data_frame.mean())\n",
    "data_frame = data_frame.rename(index = int, columns = {0: \"mpg\", 1:\"cylinders\", 2: \"displacement\",3: \"horsepower\", 4: \"weight\", 5:\"acceleration\",6:\"model year\",7:\"origin\"})\n",
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mpg field is the target variable<br>\n",
    "The Features in the dataset are explored to understand their relationship with the target variable\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8pL6Z8q9ysYM"
   },
   "source": [
    "**Summary Statistics on dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.514573</td>\n",
       "      <td>5.454774</td>\n",
       "      <td>193.425879</td>\n",
       "      <td>104.469388</td>\n",
       "      <td>2970.424623</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>76.010050</td>\n",
       "      <td>1.572864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.815984</td>\n",
       "      <td>1.701004</td>\n",
       "      <td>104.269838</td>\n",
       "      <td>38.199187</td>\n",
       "      <td>846.841774</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>3.697627</td>\n",
       "      <td>0.802055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>2223.750000</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>17.175000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement  horsepower       weight  \\\n",
       "count  398.000000  398.000000    398.000000  398.000000   398.000000   \n",
       "mean    23.514573    5.454774    193.425879  104.469388  2970.424623   \n",
       "std      7.815984    1.701004    104.269838   38.199187   846.841774   \n",
       "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
       "25%     17.500000    4.000000    104.250000   76.000000  2223.750000   \n",
       "50%     23.000000    4.000000    148.500000   95.000000  2803.500000   \n",
       "75%     29.000000    8.000000    262.000000  125.000000  3608.000000   \n",
       "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
       "\n",
       "       acceleration  model year      origin  \n",
       "count    398.000000  398.000000  398.000000  \n",
       "mean      15.568090   76.010050    1.572864  \n",
       "std        2.757689    3.697627    0.802055  \n",
       "min        8.000000   70.000000    1.000000  \n",
       "25%       13.825000   73.000000    1.000000  \n",
       "50%       15.500000   76.000000    1.000000  \n",
       "75%       17.175000   79.000000    2.000000  \n",
       "max       24.800000   82.000000    3.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data_frame.iloc[:,0] #target\n",
    "x = data_frame.iloc[:,1:8] #features\n",
    "data_frame.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kzoRyF_zy4__"
   },
   "source": [
    "**Skewness and kurtosis of dependent variable y**"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2f9993e10>"
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     "execution_count": 4,
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2f9993860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 808,
     "status": "ok",
     "timestamp": 1559379563893,
     "user": {
      "displayName": "Roy Jacuzzi",
      "photoUrl": "",
      "userId": "04564756668878318833"
     },
     "user_tz": -120
    },
    "id": "Q4r0WmYAoIal",
    "outputId": "9e002319-441e-46f9-81b3-f0d7da8526c6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness  0.45706634399491913\n",
      "Kurtosis  -0.5107812652123154\n"
     ]
    }
   ],
   "source": [
    "print(\"Skewness \", y.skew())\n",
    "print(\"Kurtosis \", y.kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Dqx9wtfrzB6a"
   },
   "source": [
    "The depent variable has a slight positive skewness and a slightly flat peak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_iEZzaND-znq"
   },
   "source": [
    "**Relationship between target variable and cylinders**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 883,
     "status": "ok",
     "timestamp": 1559379564682,
     "user": {
      "displayName": "Roy Jacuzzi",
      "photoUrl": "",
      "userId": "04564756668878318833"
     },
     "user_tz": -120
    },
    "id": "lShgvA-U-zns",
    "outputId": "2cc343bc-3089-48c9-c05e-b79b7210076d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting relationship between mpg and cylinders\n",
      "     mpg  cylinders\n",
      "0  18.0          8\n",
      "1  15.0          8\n",
      "2  18.0          8\n",
      "3  16.0          8\n",
      "4  17.0          8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2f9a4bb38>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2f9993940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([data_frame[\"mpg\"], data_frame[\"cylinders\"]], axis = 1)\n",
    "print(\"Plotting relationship between mpg and cylinders\\n\", data.head())\n",
    "data.plot.scatter(x = \"cylinders\", y = \"mpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NPukB9aK-zny"
   },
   "source": [
    "-  an inverse relationship can be observed between the two variables "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LUKiyWkt-ZGC"
   },
   "source": [
    "**Relationship between target variable and displacement**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 756,
     "status": "ok",
     "timestamp": 1559379565307,
     "user": {
      "displayName": "Roy Jacuzzi",
      "photoUrl": "",
      "userId": "04564756668878318833"
     },
     "user_tz": -120
    },
    "id": "NHGMLQXy-ZGE",
    "outputId": "dd9223d6-33ea-40df-e015-fb374b4d16e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting relationship between mpg and displacement\n",
      "     mpg  displacement\n",
      "0  18.0         307.0\n",
      "1  15.0         350.0\n",
      "2  18.0         318.0\n",
      "3  16.0         304.0\n",
      "4  17.0         302.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2f9a4ba20>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2f9da3f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([data_frame[\"mpg\"], data_frame[\"displacement\"]], axis = 1)\n",
    "print(\"Plotting relationship between mpg and displacement\\n\", data.head())\n",
    "data.plot.scatter(x = \"displacement\", y = \"mpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "724ZOix5-ZGL"
   },
   "source": [
    "-  approximate exponential decaying relationship exists between the two variables "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9nLHs3Bz0V3Z"
   },
   "source": [
    "**Relationship between target variable and horsepower**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 751,
     "status": "ok",
     "timestamp": 1559379566501,
     "user": {
      "displayName": "Roy Jacuzzi",
      "photoUrl": "",
      "userId": "04564756668878318833"
     },
     "user_tz": -120
    },
    "id": "zoH6hMJP0V3a",
    "outputId": "2d937fa2-afc5-4daf-d0ee-a358a33ea2e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting relationship between mpg and horsepower\n",
      "     mpg  horsepower\n",
      "0  18.0       130.0\n",
      "1  15.0       165.0\n",
      "2  18.0       150.0\n",
      "3  16.0       150.0\n",
      "4  17.0       140.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2f9dd9128>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2f9ddb7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([data_frame[\"mpg\"], data_frame[\"horsepower\"]], axis = 1)\n",
    "print(\"Plotting relationship between mpg and horsepower\\n\", data.head())\n",
    "data.plot.scatter(x = \"horsepower\", y = \"mpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2ddtlawz0V3e"
   },
   "source": [
    "-  A similar relationship as observed with displacement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kNWAh1Wl01rn"
   },
   "source": [
    "**Relationship between target variable and weight**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 637,
     "status": "ok",
     "timestamp": 1559379567086,
     "user": {
      "displayName": "Roy Jacuzzi",
      "photoUrl": "",
      "userId": "04564756668878318833"
     },
     "user_tz": -120
    },
    "id": "5H__FVWB01ro",
    "outputId": "fd1cff2f-6e11-4466-f5c9-834866903ff9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting relationship between mpg and weight\n",
      "     mpg  weight\n",
      "0  18.0  3504.0\n",
      "1  15.0  3693.0\n",
      "2  18.0  3436.0\n",
      "3  16.0  3433.0\n",
      "4  17.0  3449.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2f9e33d30>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2f9df8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([data_frame[\"mpg\"], data_frame[\"weight\"]], axis = 1)\n",
    "print(\"Plotting relationship between mpg and weight\\n\", data.head())\n",
    "data.plot.scatter(x = \"weight\", y = \"mpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LKuGZFpS01rt"
   },
   "source": [
    "- an inverse relationship exists between the target variable and weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "34HQGKPd1eII"
   },
   "source": [
    "**Relationship between target variable and acceleration**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 557,
     "status": "ok",
     "timestamp": 1559379568693,
     "user": {
      "displayName": "Roy Jacuzzi",
      "photoUrl": "",
      "userId": "04564756668878318833"
     },
     "user_tz": -120
    },
    "id": "1uCs1Gx21eIK",
    "outputId": "06b7b884-b4fd-4c85-83cd-f709fe5720d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting relationship between mpg and acceleration\n",
      "     mpg  acceleration\n",
      "0  18.0          12.0\n",
      "1  15.0          11.5\n",
      "2  18.0          11.0\n",
      "3  16.0          12.0\n",
      "4  17.0          10.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2f9e1d518>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2f9e69668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([data_frame[\"mpg\"], data_frame[\"acceleration\"]], axis = 1)\n",
    "print(\"Plotting relationship between mpg and acceleration\\n\", data.head())\n",
    "data.plot.scatter(x = \"acceleration\", y = \"mpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LUGaV4T91eIO"
   },
   "source": [
    "- a clear relationship cannot be established between this two variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hq1e5uaz1_rq"
   },
   "source": [
    "**Relationship between target variable and Age of car by 1982**\n",
    "- The fied model year is converted to a numerical variable. \n",
    "- This is done by getting the age of the car by 1982.\n",
    "- This is computed as 82 - model year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 663,
     "status": "ok",
     "timestamp": 1559379570617,
     "user": {
      "displayName": "Roy Jacuzzi",
      "photoUrl": "",
      "userId": "04564756668878318833"
     },
     "user_tz": -120
    },
    "id": "04B0hVsf1_rt",
    "outputId": "a4a2f8a3-8c28-466c-dc35-b3e7d21c4fea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting relationship between mpg and age by 1982\n",
      "     mpg  model year\n",
      "0  18.0          12\n",
      "1  15.0          12\n",
      "2  18.0          12\n",
      "3  16.0          12\n",
      "4  17.0          12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2f9f124e0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2f9ea8cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_year = data_frame[\"model year\"]\n",
    "model_year = 82 - model_year\n",
    "data = pd.concat([data_frame[\"mpg\"], model_year ], axis = 1)\n",
    "print(\"Plotting relationship between mpg and age by 1982\\n\", data.head())\n",
    "data.plot.scatter(x = \"model year\", y = \"mpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RB4j1fGX1_rz"
   },
   "source": [
    "- an inverse relationship exists between the target variable and the age of the car by 1982\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c8RaWTrN623T"
   },
   "source": [
    "**Correlation  Matrix of the features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 613
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 844,
     "status": "ok",
     "timestamp": 1559379576836,
     "user": {
      "displayName": "Roy Jacuzzi",
      "photoUrl": "",
      "userId": "04564756668878318833"
     },
     "user_tz": -120
    },
    "id": "HfZ9kohD6x9G",
    "outputId": "ce6667a0-8dcf-4637-8ac5-0b89e1fdab82"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2f9f624a8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2f9ec4518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor_mat = data_frame.corr()\n",
    "plt.subplots(figsize = (13,10))\n",
    "sns.heatmap(cor_mat, vmax = 0.8, square = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the heat maps the following can be observed.\n",
    "- cylinders, displacement, horsepower and weight are strongly correlated with one another\n",
    "- Let the following fields : cylinders, displacement, horsepower and weight be referred to as inverse fields\n",
    "- There is an strong inverse correlation between mpg and the fields I refer to as inverse fields\n",
    "- acceleration shows the weakest correlation to mpg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TZLEzbuOBrKE"
   },
   "source": [
    " ### Feature Selection and Preprocessing \n",
    " ---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- All the features except acceleration ,which shows the weakest correlation, is selected for this model\n",
    "- Feature cylinder is one hot encoded\n",
    "- Features displacement, horsepower and weight are min-max normalized\n",
    "- Feature Model year is converted to Age of Car by 1982\n",
    "- Feature Origin is one hot encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xWSz89rpAZMt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_data shape  (398, 12)\n",
      "y_data shape  (398, 1)\n",
      "Number of features is  12\n"
     ]
    }
   ],
   "source": [
    "#select target variable\n",
    "y = data_frame.iloc[:,0]\n",
    "y_data = y.values.astype(\"float\").reshape(398,-1)\n",
    "\n",
    "#one hot encoding cylinders feature\n",
    "cylinders = pd.get_dummies(data_frame.iloc[:,1])\n",
    "cylinders = cylinders.values.astype(\"float\")\n",
    "\n",
    "#select the columns displacement, horsepower and weight and perform min-max normalization\n",
    "disp_horsp_weight = data_frame.iloc[:,2:5].values\n",
    "min_max = prep.MinMaxScaler()\n",
    "disp_horsp_weight = min_max.fit_transform(disp_horsp_weight)\n",
    "\n",
    "#converting model year to age of car by 1982\n",
    "age_of_car_by_82 = 82 - data_frame.iloc[:,6]\n",
    "age_of_car_by_82 = age_of_car_by_82.values.astype(\"float\").reshape(398,-1)\n",
    "\n",
    "#one hot encoding of origin\n",
    "origin = pd.get_dummies(data_frame.iloc[:,7])\n",
    "origin = origin.values.astype(\"float\")\n",
    "\n",
    "#concatenating cylinders, numerical features, age of car by 1982 and origin \n",
    "x_data = np.concatenate((cylinders,disp_horsp_weight,age_of_car_by_82,origin), axis = 1)\n",
    "\n",
    "#number of features in the dataset\n",
    "n = x_data.shape[1]\n",
    "\n",
    "print(\"x_data shape \",x_data.shape)\n",
    "print(\"y_data shape \",y_data.shape)\n",
    "print(\"Number of features is \",n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "odyIChXBB7xl"
   },
   "source": [
    "**Data set is shuffled, train and test sets are selected**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0epMRAYnCqLi"
   },
   "outputs": [],
   "source": [
    "x_shuffled,y_shuffled = shuffle(x_data,y_data,random_state=0)\n",
    "X_train = x_shuffled[:358].reshape(-1,n)\n",
    "X_test = x_shuffled[358:].reshape(-1,n)\n",
    "y_train = y_shuffled[:358].reshape(-1,1)\n",
    "y_test = y_shuffled[358:].reshape(-1,1)\n",
    "\n",
    "m = 398 #number of training features\n",
    "lr = 0.00001# learning rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- cost function is 0.5 * Mean Squared Error\n",
    "- Mean Absolute error is also computed\n",
    "- Model is trained using GradientDescentOptimizer\n",
    "- Learning rate of 0.002 is selected\n",
    "- 180 epochs is selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LGIDA27NCtxO"
   },
   "outputs": [],
   "source": [
    "X = tf.placeholder(\"float32\", [None,n])\n",
    "Y = tf.placeholder(\"float32\", [None,1])\n",
    "W = tf.Variable(np.random.rand(n,1).astype(\"float32\"))\n",
    "b = tf.Variable((np.random.rand()))\n",
    "\n",
    "y_hat = tf.matmul(X,W) + b\n",
    "error = Y - y_hat\n",
    "\n",
    "cost = tf.reduce_mean(tf.pow(error,2))/2.0\n",
    "mae_loss = tf.reduce_mean(tf.abs(error))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(lr).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F8NdCMNLEVNw"
   },
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "epochs = 180\n",
    "mse_losses = []\n",
    "mae_losses = []\n",
    "rmse_losses = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1615
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 37251,
     "status": "ok",
     "timestamp": 1559379625389,
     "user": {
      "displayName": "Roy Jacuzzi",
      "photoUrl": "",
      "userId": "04564756668878318833"
     },
     "user_tz": -120
    },
    "id": "PCQFbhPCEWV0",
    "outputId": "522f07b4-07bd-483f-b3aa-5efba9f7cf65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Weight  [[0.33905014]\n",
      " [0.82588875]\n",
      " [0.9529132 ]\n",
      " [0.75860846]\n",
      " [0.20069438]\n",
      " [0.5181065 ]\n",
      " [0.3181752 ]\n",
      " [0.7031582 ]\n",
      " [0.5507825 ]\n",
      " [0.31898013]\n",
      " [0.0916599 ]\n",
      " [0.6175803 ]] \n",
      " Initial bias : 0.34582812 \n",
      "\n",
      "For epoch  1 mse loss is  377.46704\n",
      "For epoch  1 mae loss is  16.644308\n",
      "For epoch  1 rmse loss is  19.42851 \n",
      "\n",
      "For epoch  21 mse loss is  248.14494\n",
      "For epoch  21 mae loss is  12.380547\n",
      "For epoch  21 rmse loss is  15.752617 \n",
      "\n",
      "For epoch  41 mse loss is  228.73071\n",
      "For epoch  41 mae loss is  11.89013\n",
      "For epoch  41 rmse loss is  15.123846 \n",
      "\n",
      "For epoch  61 mse loss is  211.01927\n",
      "For epoch  61 mae loss is  11.4026165\n",
      "For epoch  61 rmse loss is  14.526503 \n",
      "\n",
      "For epoch  81 mse loss is  194.81175\n",
      "For epoch  81 mae loss is  10.934438\n",
      "For epoch  81 rmse loss is  13.957498 \n",
      "\n",
      "For epoch  101 mse loss is  179.9792\n",
      "For epoch  101 mae loss is  10.485959\n",
      "For epoch  101 rmse loss is  13.415633 \n",
      "\n",
      "For epoch  121 mse loss is  166.40347\n",
      "For epoch  121 mae loss is  10.061172\n",
      "For epoch  121 rmse loss is  12.899747 \n",
      "\n",
      "For epoch  141 mse loss is  153.97818\n",
      "For epoch  141 mae loss is  9.654467\n",
      "For epoch  141 rmse loss is  12.408794 \n",
      "\n",
      "For epoch  161 mse loss is  142.6053\n",
      "For epoch  161 mae loss is  9.266918\n",
      "For epoch  161 rmse loss is  11.941746 \n",
      "\n",
      " Weight  [[ 0.35529727]\n",
      " [ 5.2765417 ]\n",
      " [ 1.0452636 ]\n",
      " [ 1.2298527 ]\n",
      " [-0.5080969 ]\n",
      " [ 0.5644293 ]\n",
      " [ 0.619423  ]\n",
      " [ 1.1587123 ]\n",
      " [ 1.3683883 ]\n",
      " [ 1.6007365 ]\n",
      " [ 1.1964394 ]\n",
      " [ 2.5527523 ]] \n",
      "Final bias : 4.6675014 \n",
      "\n",
      "Final training rmse loss  11.519247 \n",
      "\n",
      "Final test rmse loss  10.627703\n",
      "\n",
      "*************************************\n",
      "     Ground truth  Predictions on Test set\n",
      "0           18.2                11.257879\n",
      "1           30.7                 9.183707\n",
      "2           24.0                22.379923\n",
      "3           17.0                11.225372\n",
      "4           27.0                12.087739\n",
      "5           23.0                21.307840\n",
      "6           18.0                20.672159\n",
      "7           27.2                13.426928\n",
      "8           35.0                14.388810\n",
      "9           29.0                19.570137\n",
      "10          21.5                15.014124\n",
      "11          15.0                19.631332\n",
      "12          17.5                12.725231\n",
      "13          15.0                20.887043\n",
      "14          32.7                12.048342\n",
      "15          10.0                24.161839\n",
      "16          20.0                16.365524\n",
      "17          32.4                15.600392\n",
      "18          34.0                12.843557\n",
      "19          18.0                17.849129\n",
      "20          32.0                12.716677\n",
      "21          14.0                22.667522\n",
      "22          24.0                16.478373\n",
      "23          26.4                14.940914\n",
      "24          14.0                19.521254\n",
      "25          13.0                21.335833\n",
      "26          13.0                19.600452\n",
      "27          18.5                11.405769\n",
      "28          21.5                18.599430\n",
      "29          16.2                13.589143\n",
      "30          16.5                16.424145\n",
      "31          15.0                23.870110\n",
      "32          28.1                13.261025\n",
      "33          29.0                19.938740\n",
      "34          20.2                12.530981\n",
      "35          27.9                14.998482\n",
      "36          22.0                16.744118\n",
      "37          29.0                23.549522\n",
      "38          19.0                23.545902\n",
      "39          25.0                21.035849\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as s:\n",
    "  s.run(init)\n",
    "  print(\"Initial Weight \", s.run(W), \"\\n\",\"Initial bias :\", s.run(b), \"\\n\")\n",
    "  for epoch in range(epochs):\n",
    "    X_train,y_train = shuffle(X_train,y_train,random_state=0)\n",
    "    for (x,y) in zip(X_train, y_train):\n",
    "      x = x.reshape(1,n)\n",
    "      y = y.reshape(1,1)\n",
    "      s.run(optimizer, feed_dict = {X:x, Y:y})\n",
    "    \n",
    "    epoch_mse_loss, epoch_mae_loss =  s.run([cost*2.0, mae_loss], feed_dict = {X:X_train, Y:y_train})\n",
    "    epoch_rmse_loss = np.sqrt(epoch_mse_loss)\n",
    "    \n",
    "    if epoch % 20 == 0:\n",
    "      print(\"For epoch \", epoch+1, \"mse loss is \", epoch_mse_loss)\n",
    "      print(\"For epoch \", epoch+1, \"mae loss is \", epoch_mae_loss)\n",
    "      print(\"For epoch \", epoch+1, \"rmse loss is \", epoch_rmse_loss,\"\\n\")\n",
    "    \n",
    "    mse_losses.append(epoch_mse_loss)\n",
    "    mae_losses.append(epoch_mae_loss)\n",
    "    rmse_losses.append(epoch_rmse_loss)\n",
    "    \n",
    "  print(\" Weight \", s.run(W), \"\\nFinal bias :\", s.run(b),\"\\n\")\n",
    "  print(\"Final training rmse loss \", epoch_rmse_loss, \"\\n\")\n",
    "  print(\"Final test rmse loss \", s.run(tf.sqrt(2.0*cost), feed_dict = {X:X_test, Y:y_test}))\n",
    "  \n",
    "  pred_test = s.run(y_hat, feed_dict = {X:X_test, Y:y_test})\n",
    "  _ = np.concatenate((y_test,pred_test), axis = 1)\n",
    "  test_set = pd.DataFrame(_, columns = [\"Ground truth\",\"Predictions on Test set\"])\n",
    "  print(\"\\n*************************************\\n\",test_set)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 239120,
     "status": "ok",
     "timestamp": 1559378454842,
     "user": {
      "displayName": "Roy Jacuzzi",
      "photoUrl": "",
      "userId": "04564756668878318833"
     },
     "user_tz": -120
    },
    "id": "xBbhHaJEcJsK",
    "outputId": "8b908f1d-cf37-4af9-b27b-45c247cb9d99"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Loss')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2fcba7e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize = (20,6))\n",
    "ax1.plot(mse_losses)\n",
    "ax2.plot(mae_losses)\n",
    "ax3.plot(rmse_losses)\n",
    "ax1.set_title('MSE Loss', fontsize = 15)\n",
    "ax2.set_title('MAE Loss', fontsize = 15)\n",
    "ax3.set_title('RMSE Loss', fontsize = 15)\n",
    "ax1.set_xlabel('Epochs', fontsize = 15)\n",
    "ax2.set_xlabel('Epochs', fontsize = 15)\n",
    "ax3.set_xlabel('Epochs', fontsize = 15)\n",
    "ax1.set_ylabel('Loss', fontsize = 15)\n",
    "ax2.set_ylabel('Loss', fontsize = 15)\n",
    "ax3.set_ylabel('Loss', fontsize = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "DDA_Ex_5_1b.ipynb",
   "provenance": [
    {
     "file_id": "1M6oQLIaFBx8kEyt2U7m3Kv7oVbudJYGw",
     "timestamp": 1559056094794
    }
   ],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}