From 0f9abe68fa3b9eb57bf782454f9f74c568ecc905 Mon Sep 17 00:00:00 2001
From: Akan Daniel <51209193+AkanimohOD19A@users.noreply.github.com>
Date: Sat, 28 Nov 2020 23:05:49 +0100
Subject: [PATCH 1/2] Update 19-11-2020 ML Course Nigeria Project 'AMAH Akan
 Daniel'.ipynb

Initial summit of first ML Task for Global Ai

Analysis of red-wine data set using Linear Discriminant Analysis by AMAH Akan Daniel
---
 ...020 ML Course Nigeria Project 'name'.ipynb | 2774 ++++++++++++++++-
 1 file changed, 2637 insertions(+), 137 deletions(-)

diff --git a/Project/19-11-2020 ML Course Nigeria Project 'name'.ipynb b/Project/19-11-2020 ML Course Nigeria Project 'name'.ipynb
index c73b70e..512ed04 100644
--- a/Project/19-11-2020 ML Course Nigeria Project 'name'.ipynb	
+++ b/Project/19-11-2020 ML Course Nigeria Project 'name'.ipynb	
@@ -4,55 +4,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Project\n",
-    "\n",
-    "In this project, our aim is to building a model for predicting wine qualities. Our label will be `quality` column. Do not forget, this is a Classification problem!\n",
-    "\n",
-    "## Steps\n",
-    "- Read the `winequality.csv` file and describe it.\n",
-    "- Make at least 4 different analysis on Exploratory Data Analysis section.\n",
-    "- Pre-process the dataset to get ready for ML application. (Check missing data and handle them, can we need to do scaling or feature extraction etc.)\n",
-    "- Define appropriate evaluation metric for our case (classification).\n",
-    "- Train and evaluate Decision Trees and at least 2 different appropriate algorithm which you can choose from scikit-learn library.\n",
-    "- Is there any overfitting and underfitting? Interpret your results and try to overcome if there is any problem in a new section.\n",
-    "- Create confusion metrics for each algorithm and display Accuracy, Recall, Precision and F1-Score values.\n",
-    "- Analyse and compare results of 3 algorithms.\n",
-    "- Select best performing model based on evaluation metric you chose on test dataset.\n",
-    "\n",
-    "\n",
-    "Good luck :)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h2>Your Name</h2>"
+    "# AMAH Akan Daniel"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Data"
+    "red_wine dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "import seaborn as sns\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -93,13 +75,13 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>7.4</td>\n",
-       "      <td>0.700</td>\n",
+       "      <td>0.70</td>\n",
        "      <td>0.00</td>\n",
        "      <td>1.9</td>\n",
        "      <td>0.076</td>\n",
        "      <td>11.0</td>\n",
        "      <td>34.0</td>\n",
-       "      <td>0.99780</td>\n",
+       "      <td>0.9978</td>\n",
        "      <td>3.51</td>\n",
        "      <td>0.56</td>\n",
        "      <td>9.4</td>\n",
@@ -108,13 +90,13 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>7.8</td>\n",
-       "      <td>0.880</td>\n",
+       "      <td>0.88</td>\n",
        "      <td>0.00</td>\n",
        "      <td>2.6</td>\n",
        "      <td>0.098</td>\n",
        "      <td>25.0</td>\n",
        "      <td>67.0</td>\n",
-       "      <td>0.99680</td>\n",
+       "      <td>0.9968</td>\n",
        "      <td>3.20</td>\n",
        "      <td>0.68</td>\n",
        "      <td>9.8</td>\n",
@@ -123,13 +105,13 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>7.8</td>\n",
-       "      <td>0.760</td>\n",
+       "      <td>0.76</td>\n",
        "      <td>0.04</td>\n",
        "      <td>2.3</td>\n",
        "      <td>0.092</td>\n",
        "      <td>15.0</td>\n",
        "      <td>54.0</td>\n",
-       "      <td>0.99700</td>\n",
+       "      <td>0.9970</td>\n",
        "      <td>3.26</td>\n",
        "      <td>0.65</td>\n",
        "      <td>9.8</td>\n",
@@ -138,13 +120,13 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>11.2</td>\n",
-       "      <td>0.280</td>\n",
+       "      <td>0.28</td>\n",
        "      <td>0.56</td>\n",
        "      <td>1.9</td>\n",
        "      <td>0.075</td>\n",
        "      <td>17.0</td>\n",
        "      <td>60.0</td>\n",
-       "      <td>0.99800</td>\n",
+       "      <td>0.9980</td>\n",
        "      <td>3.16</td>\n",
        "      <td>0.58</td>\n",
        "      <td>9.8</td>\n",
@@ -153,48 +135,96 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>7.4</td>\n",
-       "      <td>0.700</td>\n",
+       "      <td>0.70</td>\n",
        "      <td>0.00</td>\n",
        "      <td>1.9</td>\n",
        "      <td>0.076</td>\n",
        "      <td>11.0</td>\n",
        "      <td>34.0</td>\n",
-       "      <td>0.99780</td>\n",
+       "      <td>0.9978</td>\n",
        "      <td>3.51</td>\n",
        "      <td>0.56</td>\n",
        "      <td>9.4</td>\n",
        "      <td>5</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1594</th>\n",
-       "      <td>6.2</td>\n",
-       "      <td>0.600</td>\n",
-       "      <td>0.08</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.090</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>0.99490</td>\n",
-       "      <td>3.45</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>10.5</td>\n",
-       "      <td>5</td>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0            7.4              0.70         0.00             1.9      0.076   \n",
+       "1            7.8              0.88         0.00             2.6      0.098   \n",
+       "2            7.8              0.76         0.04             2.3      0.092   \n",
+       "3           11.2              0.28         0.56             1.9      0.075   \n",
+       "4            7.4              0.70         0.00             1.9      0.076   \n",
+       "\n",
+       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "\n",
+       "   alcohol  quality  \n",
+       "0      9.4        5  \n",
+       "1      9.8        5  \n",
+       "2      9.8        5  \n",
+       "3      9.8        6  \n",
+       "4      9.4        5  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_Df = pd.read_csv(\"C:/Users/HP/PycharmProjects/GlobalAi/red-white-wine-dataset/winequality-red.csv\", sep=';')\n",
+    "wine_Df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
        "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
        "    <tr>\n",
        "      <th>1595</th>\n",
        "      <td>5.9</td>\n",
@@ -257,50 +287,26 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1599 rows × 12 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
-       "0               7.4             0.700         0.00             1.9      0.076   \n",
-       "1               7.8             0.880         0.00             2.6      0.098   \n",
-       "2               7.8             0.760         0.04             2.3      0.092   \n",
-       "3              11.2             0.280         0.56             1.9      0.075   \n",
-       "4               7.4             0.700         0.00             1.9      0.076   \n",
-       "...             ...               ...          ...             ...        ...   \n",
-       "1594            6.2             0.600         0.08             2.0      0.090   \n",
        "1595            5.9             0.550         0.10             2.2      0.062   \n",
        "1596            6.3             0.510         0.13             2.3      0.076   \n",
        "1597            5.9             0.645         0.12             2.0      0.075   \n",
        "1598            6.0             0.310         0.47             3.6      0.067   \n",
        "\n",
        "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
-       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
-       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
-       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
-       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
-       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
-       "...                   ...                   ...      ...   ...        ...   \n",
-       "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
        "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
        "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
        "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
        "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
        "\n",
        "      alcohol  quality  \n",
-       "0         9.4        5  \n",
-       "1         9.8        5  \n",
-       "2         9.8        5  \n",
-       "3         9.8        6  \n",
-       "4         9.4        5  \n",
-       "...       ...      ...  \n",
-       "1594     10.5        5  \n",
        "1595     11.2        6  \n",
        "1596     11.0        6  \n",
        "1597     10.2        5  \n",
-       "1598     11.0        6  \n",
-       "\n",
-       "[1599 rows x 12 columns]"
+       "1598     11.0        6  "
       ]
      },
      "execution_count": 3,
@@ -309,46 +315,301 @@
     }
    ],
    "source": [
-    "# Read csv\n"
+    "wine_Df.iloc[1595:1600]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1599 entries, 0 to 1598\n",
+      "Data columns (total 12 columns):\n",
+      "fixed acidity           1599 non-null float64\n",
+      "volatile acidity        1599 non-null float64\n",
+      "citric acid             1599 non-null float64\n",
+      "residual sugar          1599 non-null float64\n",
+      "chlorides               1599 non-null float64\n",
+      "free sulfur dioxide     1599 non-null float64\n",
+      "total sulfur dioxide    1599 non-null float64\n",
+      "density                 1599 non-null float64\n",
+      "pH                      1599 non-null float64\n",
+      "sulphates               1599 non-null float64\n",
+      "alcohol                 1599 non-null float64\n",
+      "quality                 1599 non-null int64\n",
+      "dtypes: float64(11), int64(1)\n",
+      "memory usage: 150.0 KB\n"
+     ]
+    }
+   ],
    "source": [
-    "# Describe our data for each feature and use .info() for get information about our dataset\n",
-    "# Analyse missing values"
+    "wine_Df.info()"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 5,
    "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>8.319637</td>\n",
+       "      <td>0.527821</td>\n",
+       "      <td>0.270976</td>\n",
+       "      <td>2.538806</td>\n",
+       "      <td>0.087467</td>\n",
+       "      <td>15.874922</td>\n",
+       "      <td>46.467792</td>\n",
+       "      <td>0.996747</td>\n",
+       "      <td>3.311113</td>\n",
+       "      <td>0.658149</td>\n",
+       "      <td>10.422983</td>\n",
+       "      <td>5.636023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.741096</td>\n",
+       "      <td>0.179060</td>\n",
+       "      <td>0.194801</td>\n",
+       "      <td>1.409928</td>\n",
+       "      <td>0.047065</td>\n",
+       "      <td>10.460157</td>\n",
+       "      <td>32.895324</td>\n",
+       "      <td>0.001887</td>\n",
+       "      <td>0.154386</td>\n",
+       "      <td>0.169507</td>\n",
+       "      <td>1.065668</td>\n",
+       "      <td>0.807569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.600000</td>\n",
+       "      <td>0.120000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.012000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.990070</td>\n",
+       "      <td>2.740000</td>\n",
+       "      <td>0.330000</td>\n",
+       "      <td>8.400000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>7.100000</td>\n",
+       "      <td>0.390000</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>0.070000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>0.995600</td>\n",
+       "      <td>3.210000</td>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>9.500000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>0.520000</td>\n",
+       "      <td>0.260000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>0.079000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>0.996750</td>\n",
+       "      <td>3.310000</td>\n",
+       "      <td>0.620000</td>\n",
+       "      <td>10.200000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>9.200000</td>\n",
+       "      <td>0.640000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.997835</td>\n",
+       "      <td>3.400000</td>\n",
+       "      <td>0.730000</td>\n",
+       "      <td>11.100000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>15.900000</td>\n",
+       "      <td>1.580000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>15.500000</td>\n",
+       "      <td>0.611000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>289.000000</td>\n",
+       "      <td>1.003690</td>\n",
+       "      <td>4.010000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>14.900000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
+       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
+       "mean        8.319637          0.527821     0.270976        2.538806   \n",
+       "std         1.741096          0.179060     0.194801        1.409928   \n",
+       "min         4.600000          0.120000     0.000000        0.900000   \n",
+       "25%         7.100000          0.390000     0.090000        1.900000   \n",
+       "50%         7.900000          0.520000     0.260000        2.200000   \n",
+       "75%         9.200000          0.640000     0.420000        2.600000   \n",
+       "max        15.900000          1.580000     1.000000       15.500000   \n",
+       "\n",
+       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
+       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
+       "mean      0.087467            15.874922             46.467792     0.996747   \n",
+       "std       0.047065            10.460157             32.895324     0.001887   \n",
+       "min       0.012000             1.000000              6.000000     0.990070   \n",
+       "25%       0.070000             7.000000             22.000000     0.995600   \n",
+       "50%       0.079000            14.000000             38.000000     0.996750   \n",
+       "75%       0.090000            21.000000             62.000000     0.997835   \n",
+       "max       0.611000            72.000000            289.000000     1.003690   \n",
+       "\n",
+       "                pH    sulphates      alcohol      quality  \n",
+       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
+       "mean      3.311113     0.658149    10.422983     5.636023  \n",
+       "std       0.154386     0.169507     1.065668     0.807569  \n",
+       "min       2.740000     0.330000     8.400000     3.000000  \n",
+       "25%       3.210000     0.550000     9.500000     5.000000  \n",
+       "50%       3.310000     0.620000    10.200000     6.000000  \n",
+       "75%       3.400000     0.730000    11.100000     6.000000  \n",
+       "max       4.010000     2.000000    14.900000     8.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Exploratory Data Analysis"
+    "wine_Df.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1f242869358>"
+       "fixed acidity           0\n",
+       "volatile acidity        0\n",
+       "citric acid             0\n",
+       "residual sugar          0\n",
+       "chlorides               0\n",
+       "free sulfur dioxide     0\n",
+       "total sulfur dioxide    0\n",
+       "density                 0\n",
+       "pH                      0\n",
+       "sulphates               0\n",
+       "alcohol                 0\n",
+       "quality                 0\n",
+       "dtype: int64"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "wine_Df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Clean Data, no missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1440x720 with 12 Axes>"
       ]
      },
      "metadata": {
@@ -358,27 +619,55 @@
     }
    ],
    "source": [
-    "# Our label Distribution (countplot)\n"
+    "hist = wine_Df.hist(bins = 10, figsize = (20, 10))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5    681\n",
+       "6    638\n",
+       "7    199\n",
+       "4     53\n",
+       "8     18\n",
+       "3     10\n",
+       "Name: quality, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_Df['quality'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1f242b87048>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d4d27b7d30>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFCZJREFUeJzt3X+w5XV93/HnSxaCUHFBLhR3oavJlsRJIuAdSsL4o25MhShLU3FwqmwonbUdwhibNiXNTJq0yYyZmqiQlA6B4K4xKsEQVoex0lXU6IAuPwIIOqwE2ZvF3VUBRWos+u4f53OHy+6H3bNwv/fcvft8zJz5fr+f7+d7zvsMw33t5/vjc1JVSJK0u+dNugBJ0uJkQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUtWzSBTwXxx57bK1atWrSZUjSAeW22277ZlVN7avfYAGR5GTgI3OaXgr8NrCxta8CHgTeXFWPJAnwPuBs4AngV6rq9r19xqpVq9iyZcv8Fy9JS1iSr4/Tb7BTTFX11ao6papOAV7B6I/+9cClwOaqWg1sbtsAZwGr22s9cMVQtUmS9m2hrkGsAb5WVV8H1gIbWvsG4Ny2vhbYWCO3AMuTnLBA9UmSdrNQAXE+8KG2fnxVPQzQlse19hXAtjnHzLQ2SdIEDB4QSQ4DzgH+cl9dO217zEWeZH2SLUm27Nq1az5KlCR1LMQI4izg9qra0bZ3zJ46asudrX0GOHHOcSuB7bu/WVVdWVXTVTU9NbXPi/CSpGdpIQLiLTx1eglgE7Cura8DbpjTfkFGzgAemz0VJUlaeIM+B5HkCOB1wNvnNL8LuDbJRcBDwHmt/UZGt7huZXTH04VD1iZJ2rtBA6KqngBetFvbtxjd1bR73wIuHrIeSdL4nGpDktR1QE+1oaXnzMvPnHQJ++3zl3x+0iVIg3AEIUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1OVmftIA+86pXT7qE/fbqz35m0iVoQhxBSJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkrkEDIsnyJNcl+UqS+5L8XJJjktyU5P62PLr1TZLLkmxNcleS04asTZK0d0OPIN4HfKKqfhJ4OXAfcCmwuapWA5vbNsBZwOr2Wg9cMXBtkqS9GCwgkhwFvAq4GqCqflBVjwJrgQ2t2wbg3La+FthYI7cAy5OcMFR9kqS9G3IE8VJgF3BNkjuSXJXkSOD4qnoYoC2Pa/1XANvmHD/T2p4myfokW5Js2bVr14DlS9LBbciAWAacBlxRVacC3+Op00k96bTVHg1VV1bVdFVNT01NzU+lkqQ9DBkQM8BMVd3atq9jFBg7Zk8dteXOOf1PnHP8SmD7gPVJkvZisICoqm8A25Kc3JrWAPcCm4B1rW0dcENb3wRc0O5mOgN4bPZUlCRp4Q093fclwAeTHAY8AFzIKJSuTXIR8BBwXut7I3A2sBV4ovWVJE3IoAFRVXcC051dazp9C7h4yHokSePzSWpJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdQ0aEEkeTHJ3kjuTbGltxyS5Kcn9bXl0a0+Sy5JsTXJXktOGrE2StHcLMYL451V1SlVNt+1Lgc1VtRrY3LYBzgJWt9d64IoFqE2S9AwmcYppLbChrW8Azp3TvrFGbgGWJzlhAvVJkhg+IAr4ZJLbkqxvbcdX1cMAbXlca18BbJtz7ExrkyRNwLKB3//Mqtqe5DjgpiRf2UvfdNpqj06joFkPcNJJJ81PlZKkPQw6gqiq7W25E7geOB3YMXvqqC13tu4zwIlzDl8JbO+855VVNV1V01NTU0OWL0kHtcECIsmRSV4wuw78InAPsAlY17qtA25o65uAC9rdTGcAj82eipIkLbwhTzEdD1yfZPZz/qKqPpHkS8C1SS4CHgLOa/1vBM4GtgJPABcOWJskaR8GC4iqegB4eaf9W8CaTnsBFw9VjyRp//gktSSpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUtfgAZHkkCR3JPl4235JkluT3J/kI0kOa+0/1ra3tv2rhq5NkvTMFmIE8Q7gvjnbfwC8p6pWA48AF7X2i4BHquongPe0fpKkCRk0IJKsBH4JuKptB3gtcF3rsgE4t62vbdu0/Wtaf0nSBAw9gngv8BvAj9r2i4BHq+rJtj0DrGjrK4BtAG3/Y63/0yRZn2RLki27du0asnZJOqiNFRBJNo/Tttv+NwA7q+q2uc2drjXGvqcaqq6squmqmp6amtpbCZKk52DZ3nYmORw4Ajg2ydE89Uf8KODF+3jvM4FzkpwNHN6OeS+wPMmyNkpYCWxv/WeAE4GZJMuAFwLf3v+vJEmaD/saQbwduA34ybacfd0A/MneDqyq36yqlVW1Cjgf+FRV/Wvg08CbWrd17b0ANrVt2v5PVdUeIwhJ0sLY6wiiqt4HvC/JJVV1+Tx95n8GPpzk94A7gKtb+9XAB5JsZTRyOH+ePk+S9CzsNSBmVdXlSX4eWDX3mKraOObxNwM3t/UHgNM7fb4PnDfO+0mShjdWQCT5APDjwJ3AD1tzAWMFhCTpwDNWQADTwMu8JiBJB49xn4O4B/jHQxYiSVpcxh1BHAvcm+SLwD/MNlbVOYNUJUmauHED4neGLEKStPiMexfTZ4YuRJK0uIx7F9N3eWrai8OAQ4HvVdVRQxUmSZqscUcQL5i7neRcOs8ySJKWjmc1m2tV/TWjabslSUvUuKeYfnnO5vMYPRfhMxGStISNexfTG+esPwk8yOgHfiRJS9S41yAuHLoQSdLiMu4PBq1Mcn2SnUl2JPlo+zlRSdISNe5F6msY/V7Dixn9NOjHWpskaYkaNyCmquqaqnqyvd4P+HufkrSEjRsQ30zy1iSHtNdbgW8NWZgkabLGDYh/A7wZ+AbwMKOfBPXCtSQtYePe5vrfgXVV9QhAkmOAdzMKDknSEjTuCOJnZ8MBoKq+DZw6TEmSpMVg3IB4XpKjZzfaCGLc0Yck6QA07h/5PwS+kOQ6RlNsvBn4/cGqkiRN3FgjiKraCPwrYAewC/jlqvrA3o5JcniSLyb52yRfTvK7rf0lSW5Ncn+SjyQ5rLX/WNve2vavei5fTJL03Iw9m2tV3VtVf1xVl1fVvWMc8g/Aa6vq5cApwOuTnAH8AfCeqloNPAJc1PpfBDxSVT8BvKf1kyRNyLOa7nscNfJ42zy0vYrRNOHXtfYNwLltfW3bpu1fkyRD1SdJ2rvBAgKgPVR3J7ATuAn4GvBoVT3ZuswwmrqDttwG0PY/BrxoyPokSc9s0ICoqh9W1SnASka/QPdTvW5t2Rst7PGbE0nWJ9mSZMuuXbvmr1hJ0tMMGhCzqupR4GbgDGB5ktm7p1YC29v6DHAiQNv/QuDbnfe6sqqmq2p6asrpoCRpKIMFRJKpJMvb+vOBXwDuAz7NaKoOgHXADW19U9um7f9UVfmrdZI0IUM+7HYCsCHJIYyC6Nqq+niSe4EPJ/k94A7g6tb/auADSbYyGjmcP2BtkqR9GCwgquouOtNxVNUDjK5H7N7+feC8oeqRJO2fBbkGIUk68BgQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElS12ABkeTEJJ9Ocl+SLyd5R2s/JslNSe5vy6Nbe5JclmRrkruSnDZUbZKkfRtyBPEk8OtV9VPAGcDFSV4GXApsrqrVwOa2DXAWsLq91gNXDFibJGkfBguIqnq4qm5v698F7gNWAGuBDa3bBuDctr4W2FgjtwDLk5wwVH2SpL1bkGsQSVYBpwK3AsdX1cMwChHguNZtBbBtzmEzrU2SNAHLhv6AJP8I+Cjwa1X1nSTP2LXTVp33W8/oFBQnnXTSfJUpaR788a9/bNIl7Jdf/cM3TrqERW3QEUSSQxmFwwer6q9a847ZU0dtubO1zwAnzjl8JbB99/esqiurarqqpqempoYrXpIOckPexRTgauC+qvqjObs2Aeva+jrghjntF7S7mc4AHps9FSVJWnhDnmI6E3gbcHeSO1vbfwHeBVyb5CLgIeC8tu9G4GxgK/AEcOGAtUmS9mGwgKiqv6F/XQFgTad/ARcPVY8kaf/4JLUkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSugwISVLXYAGR5M+S7Exyz5y2Y5LclOT+tjy6tSfJZUm2JrkryWlD1SVJGs+QI4j3A6/fre1SYHNVrQY2t22As4DV7bUeuGLAuiRJYxgsIKrqs8C3d2teC2xo6xuAc+e0b6yRW4DlSU4YqjZJ0r4t9DWI46vqYYC2PK61rwC2zek309r2kGR9ki1JtuzatWvQYiXpYLZYLlKn01a9jlV1ZVVNV9X01NTUwGVJ0sFroQNix+ypo7bc2dpngBPn9FsJbF/g2iRJcyx0QGwC1rX1dcANc9ovaHcznQE8NnsqSpI0GcuGeuMkHwJeAxybZAb4r8C7gGuTXAQ8BJzXut8InA1sBZ4ALhyqLknSeAYLiKp6yzPsWtPpW8DFQ9UiSdp/i+UitSRpkTEgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoa7ElqDeOh//Yzky5hv53023dPugRJz4IjCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpdPUkvSmH7/rW+adAn75bf+/LrndLwjCElS16IKiCSvT/LVJFuTXDrpeiTpYLZoTjElOQT4E+B1wAzwpSSbqure/X2vV/ynjfNd3qBu+x8XTLoESdrDYhpBnA5sraoHquoHwIeBtROuSZIOWospIFYA2+Zsz7Q2SdIEpKomXQMASc4D/kVV/du2/Tbg9Kq6ZLd+64H1bfNk4KsLWOaxwDcX8PMWmt/vwLWUvxv4/ebbP6mqqX11WjTXIBiNGE6cs70S2L57p6q6ErhyoYqaK8mWqpqexGcvBL/fgWspfzfw+03KYjrF9CVgdZKXJDkMOB/YNOGaJOmgtWhGEFX1ZJJfBf43cAjwZ1X15QmXJUkHrUUTEABVdSNw46Tr2IuJnNpaQH6/A9dS/m7g95uIRXORWpK0uCymaxCSpEXEgBhDksOTfDHJ3yb5cpLfnXRN8y3JIUnuSPLxSdcy35I8mOTuJHcm2TLpeuZbkuVJrkvylST3Jfm5Sdc0X5Kc3P67zb6+k+TXJl3XfEnyzvY35Z4kH0py+KRrmstTTGNIEuDIqno8yaHA3wDvqKpbJlzavEnyH4Bp4KiqesOk65lPSR4EpqtqSd5Hn2QD8LmquqrdAXhEVT066brmW5uO5++Bf1ZVX590Pc9VkhWM/pa8rKr+b5JrgRur6v2TrewpjiDGUCOPt81D22vJJGuSlcAvAVdNuhbtnyRHAa8Crgaoqh8sxXBo1gBfWwrhMMcy4PlJlgFH0Hn2a5IMiDG1UzB3AjuBm6rq1knXNI/eC/wG8KNJFzKQAj6Z5Lb2JP5S8lJgF3BNO0V4VZIjJ13UQM4HPjTpIuZLVf098G7gIeBh4LGq+uRkq3o6A2JMVfXDqjqF0RPepyf56UnXNB+SvAHYWVW3TbqWAZ1ZVacBZwEXJ3nVpAuaR8uA04ArqupU4HvAkpsqv506Owf4y0nXMl+SHM1oQtKXAC8Gjkzy1slW9XQGxH5qw/ebgddPuJT5ciZwTjtP/2HgtUn+fLIlza+q2t6WO4HrGc0cvFTMADNzRrTXMQqMpeYs4Paq2jHpQubRLwB/V1W7qur/AX8F/PyEa3oaA2IMSaaSLG/rz2f0H/Yrk61qflTVb1bVyqpaxWgI/6mqWlT/inkukhyZ5AWz68AvAvdMtqr5U1XfALYlObk1rQH2+zdUDgBvYQmdXmoeAs5IckS7EWYNcN+Ea3qaRfUk9SJ2ArCh3UXxPODaqlpyt4MuUccD14/+/2MZ8BdV9YnJljTvLgE+2E7DPABcOOF65lWSIxj9kNjbJ13LfKqqW5NcB9wOPAncwSJ7otrbXCVJXZ5ikiR1GRCSpC4DQpLUZUBIkroMCElSlwEhDSjJqiT3tPXpJJe19dckWVQPRUm78zkIaYFU1RZgdrrx1wCPA1+YWEHSPjiCkJ5Bkt9K8tUk/6fN1f8fk9ycZLrtP7ZNUTI7Uvhcktvba4/RQRs1fDzJKuDfAe9sv3HwyiR/16aSJ8lR7TcsDl2wLyt1OIKQOpK8gtHUI6cy+v/kdmBvExruBF5XVd9PsprRtBDTvY5V9WCS/wU8XlXvbp93M6Mp1/+6fe5H2/w80sQ4gpD6XglcX1VPVNV3gE376H8o8KdJ7mY04+jL9vPzruKpKTIuBK7Zz+OleecIQnpmvXlonuSpf1jN/XnIdwI7gJe3/d/frw+q+nw7TfVq4JCqWjITCurA5QhC6vss8C+TPL/NBvvG1v4g8Iq2/qY5/V8IPFxVPwLeBhyyj/f/LvCC3do2Mjo15ehBi4IBIXVU1e3AR4A7gY8Cn2u73g38+yRfAI6dc8j/BNYluQX4p4x+uGdvPsYogO5M8srW9kHgaJbetNY6QDmbqzSGJL/DnIvKA33Gm4C1VfW2oT5D2h9eg5AWgSSXM/rVtLMnXYs0yxGEJKnLaxCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXf8fTBZcxWvQ7foAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -390,42 +679,2253 @@
     }
    ],
    "source": [
-    "# Example EDA (distplot)\n"
+    "sns.countplot(x = \"quality\", data = wine_Df)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d4d2239588>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Preprocessing\n",
-    "\n",
-    "- Are there any duplicated values?\n",
-    "- Do we need to do feature scaling?\n",
-    "- Do we need to generate new features?\n",
-    "- Split Train and Test dataset. (0.7/0.3)"
+    "sns.distplot(wine_Df['citric acid'])"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ML Application\n",
-    "\n",
-    "- Define models.\n",
-    "- Fit models.\n",
-    "- Evaluate models for both train and test dataset.\n",
-    "- Generate Confusion Matrix and scores of Accuracy, Recall, Precision and F1-Score.\n",
-    "- Analyse occurrence of overfitting and underfitting. If there is any of them, try to overcome it within a different section."
-   ]
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>6.10</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.35</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.430</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>0.106</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>0.99660</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.450</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>5.90</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.33</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.725</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>4.65</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.99620</td>\n",
+       "      <td>3.41</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>8.8</td>\n",
+       "      <td>0.410</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0.99860</td>\n",
+       "      <td>3.54</td>\n",
+       "      <td>0.66</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>8.6</td>\n",
+       "      <td>0.490</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.110</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>136.0</td>\n",
+       "      <td>0.99720</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.490</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>0.99660</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>8.1</td>\n",
+       "      <td>0.545</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0.99720</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>8.1</td>\n",
+       "      <td>0.575</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>2.10</td>\n",
+       "      <td>0.077</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>0.99670</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0.51</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.560</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>0.99640</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>8.8</td>\n",
+       "      <td>0.550</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0.119</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0.99620</td>\n",
+       "      <td>3.21</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>0.99370</td>\n",
+       "      <td>3.63</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.745</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.090</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.99650</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.715</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.089</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>0.99680</td>\n",
+       "      <td>3.23</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>5.2</td>\n",
+       "      <td>0.340</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.99160</td>\n",
+       "      <td>3.68</td>\n",
+       "      <td>0.79</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.095</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>0.99500</td>\n",
+       "      <td>3.35</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.430</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>5.50</td>\n",
+       "      <td>0.070</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>0.99730</td>\n",
+       "      <td>3.42</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.430</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>5.50</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>0.99730</td>\n",
+       "      <td>3.42</td>\n",
+       "      <td>0.71</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.99720</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.380</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0.99610</td>\n",
+       "      <td>3.33</td>\n",
+       "      <td>0.47</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>8.8</td>\n",
+       "      <td>0.610</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>0.067</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0.99690</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>7.6</td>\n",
+       "      <td>0.550</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.99640</td>\n",
+       "      <td>3.28</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>12.8</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.74</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.095</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.99940</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.430</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.99615</td>\n",
+       "      <td>3.37</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>6.9</td>\n",
+       "      <td>0.520</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.081</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>0.99685</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.630</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.097</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.99675</td>\n",
+       "      <td>3.37</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.630</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.097</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.99675</td>\n",
+       "      <td>3.37</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>8.2</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>0.99685</td>\n",
+       "      <td>3.32</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.210</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1.00005</td>\n",
+       "      <td>3.07</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>7</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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1383</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>2.10</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>0.99613</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1387</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.640</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.100</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.99610</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1401</th>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.690</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>2.10</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>0.99620</td>\n",
+       "      <td>3.25</td>\n",
+       "      <td>0.51</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1409</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.510</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.10</td>\n",
+       "      <td>0.064</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.99500</td>\n",
+       "      <td>3.54</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1412</th>\n",
+       "      <td>8.2</td>\n",
+       "      <td>0.240</td>\n",
+       "      <td>0.34</td>\n",
+       "      <td>5.10</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0.99740</td>\n",
+       "      <td>3.22</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1416</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.320</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0.077</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.99940</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1420</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.530</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>0.077</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.99500</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1425</th>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.260</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.99740</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1435</th>\n",
+       "      <td>10.2</td>\n",
+       "      <td>0.540</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>15.40</td>\n",
+       "      <td>0.214</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.00369</td>\n",
+       "      <td>3.18</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1446</th>\n",
+       "      <td>6.9</td>\n",
+       "      <td>0.630</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.99712</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1450</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.370</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.99470</td>\n",
+       "      <td>3.23</td>\n",
+       "      <td>0.73</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1457</th>\n",
+       "      <td>7.6</td>\n",
+       "      <td>0.490</td>\n",
+       "      <td>0.33</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.99706</td>\n",
+       "      <td>3.41</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1465</th>\n",
+       "      <td>6.8</td>\n",
+       "      <td>0.590</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0.063</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>0.99580</td>\n",
+       "      <td>3.41</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1468</th>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.480</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>2.10</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.99728</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1476</th>\n",
+       "      <td>9.9</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>13.80</td>\n",
+       "      <td>0.205</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>1.00242</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1481</th>\n",
+       "      <td>8.2</td>\n",
+       "      <td>0.280</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0.99828</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1491</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.540</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.99420</td>\n",
+       "      <td>3.72</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1496</th>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.540</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.089</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>0.99636</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1499</th>\n",
+       "      <td>6.9</td>\n",
+       "      <td>0.740</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>0.054</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.99508</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1507</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.380</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>0.106</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.99605</td>\n",
+       "      <td>3.36</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1520</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0.530</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.063</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>0.99489</td>\n",
+       "      <td>3.38</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1522</th>\n",
+       "      <td>6.1</td>\n",
+       "      <td>0.320</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0.99633</td>\n",
+       "      <td>3.42</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1557</th>\n",
+       "      <td>6.6</td>\n",
+       "      <td>0.855</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.99627</td>\n",
+       "      <td>3.54</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1560</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>0.99622</td>\n",
+       "      <td>3.21</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1561</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>0.99622</td>\n",
+       "      <td>3.21</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1563</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.695</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.99546</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1564</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.695</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.99546</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1567</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.695</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.99546</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1581</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.560</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0.99402</td>\n",
+       "      <td>3.54</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1596</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>0.510</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.99574</td>\n",
+       "      <td>3.42</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>240 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "4               7.4             0.700         0.00            1.90      0.076   \n",
+       "11              7.5             0.500         0.36            6.10      0.071   \n",
+       "27              7.9             0.430         0.21            1.60      0.106   \n",
+       "40              7.3             0.450         0.36            5.90      0.074   \n",
+       "65              7.2             0.725         0.05            4.65      0.086   \n",
+       "76              8.8             0.410         0.64            2.20      0.093   \n",
+       "91              8.6             0.490         0.28            1.90      0.110   \n",
+       "93              7.7             0.490         0.26            1.90      0.062   \n",
+       "102             8.1             0.545         0.18            1.90      0.080   \n",
+       "105             8.1             0.575         0.22            2.10      0.077   \n",
+       "114             7.8             0.560         0.19            1.80      0.104   \n",
+       "121             8.8             0.550         0.04            2.20      0.119   \n",
+       "132             5.6             0.500         0.09            2.30      0.049   \n",
+       "140             8.4             0.745         0.11            1.90      0.090   \n",
+       "141             8.3             0.715         0.15            1.80      0.089   \n",
+       "144             5.2             0.340         0.00            1.80      0.050   \n",
+       "153             7.5             0.600         0.03            1.80      0.095   \n",
+       "156             7.1             0.430         0.42            5.50      0.070   \n",
+       "157             7.1             0.430         0.42            5.50      0.071   \n",
+       "172             8.0             0.420         0.17            2.00      0.073   \n",
+       "176             7.3             0.380         0.21            2.00      0.080   \n",
+       "180             8.8             0.610         0.14            2.40      0.067   \n",
+       "194             7.6             0.550         0.21            2.20      0.071   \n",
+       "206            12.8             0.300         0.74            2.60      0.095   \n",
+       "228             7.7             0.430         0.25            2.60      0.073   \n",
+       "233             6.9             0.520         0.25            2.60      0.081   \n",
+       "236             7.2             0.630         0.00            1.90      0.097   \n",
+       "238             7.2             0.630         0.00            1.90      0.097   \n",
+       "239             8.2             1.000         0.09            2.30      0.065   \n",
+       "244            15.0             0.210         0.44            2.20      0.075   \n",
+       "...             ...               ...          ...             ...        ...   \n",
+       "1383            8.0             0.600         0.22            2.10      0.080   \n",
+       "1387            7.4             0.640         0.07            1.80      0.100   \n",
+       "1401            7.9             0.690         0.21            2.10      0.080   \n",
+       "1409            6.0             0.510         0.00            2.10      0.064   \n",
+       "1412            8.2             0.240         0.34            5.10      0.062   \n",
+       "1416           10.0             0.320         0.59            2.20      0.077   \n",
+       "1420            7.8             0.530         0.01            1.60      0.077   \n",
+       "1425            8.3             0.260         0.37            1.40      0.076   \n",
+       "1435           10.2             0.540         0.37           15.40      0.214   \n",
+       "1446            6.9             0.630         0.02            1.90      0.078   \n",
+       "1450            7.2             0.370         0.32            2.00      0.062   \n",
+       "1457            7.6             0.490         0.33            1.90      0.074   \n",
+       "1465            6.8             0.590         0.10            1.70      0.063   \n",
+       "1468            7.3             0.480         0.32            2.10      0.062   \n",
+       "1476            9.9             0.500         0.50           13.80      0.205   \n",
+       "1481            8.2             0.280         0.60            3.00      0.104   \n",
+       "1491            5.6             0.540         0.04            1.70      0.049   \n",
+       "1496            7.7             0.540         0.26            1.90      0.089   \n",
+       "1499            6.9             0.740         0.03            2.30      0.054   \n",
+       "1507            7.5             0.380         0.57            2.30      0.106   \n",
+       "1520            6.5             0.530         0.06            2.00      0.063   \n",
+       "1522            6.1             0.320         0.25            2.30      0.071   \n",
+       "1557            6.6             0.855         0.02            2.40      0.062   \n",
+       "1560            7.8             0.600         0.26            2.00      0.080   \n",
+       "1561            7.8             0.600         0.26            2.00      0.080   \n",
+       "1563            7.2             0.695         0.13            2.00      0.076   \n",
+       "1564            7.2             0.695         0.13            2.00      0.076   \n",
+       "1567            7.2             0.695         0.13            2.00      0.076   \n",
+       "1581            6.2             0.560         0.09            1.70      0.053   \n",
+       "1596            6.3             0.510         0.13            2.30      0.076   \n",
+       "\n",
+       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
+       "11                   17.0                 102.0  0.99780  3.35       0.80   \n",
+       "27                   10.0                  37.0  0.99660  3.17       0.91   \n",
+       "40                   12.0                  87.0  0.99780  3.33       0.83   \n",
+       "65                    4.0                  11.0  0.99620  3.41       0.39   \n",
+       "76                    9.0                  42.0  0.99860  3.54       0.66   \n",
+       "91                   20.0                 136.0  0.99720  2.93       1.95   \n",
+       "93                    9.0                  31.0  0.99660  3.39       0.64   \n",
+       "102                  13.0                  35.0  0.99720  3.30       0.59   \n",
+       "105                  12.0                  65.0  0.99670  3.29       0.51   \n",
+       "114                  12.0                  47.0  0.99640  3.19       0.93   \n",
+       "121                  14.0                  56.0  0.99620  3.21       0.60   \n",
+       "132                  17.0                  99.0  0.99370  3.63       0.63   \n",
+       "140                  16.0                  63.0  0.99650  3.19       0.82   \n",
+       "141                  10.0                  52.0  0.99680  3.23       0.77   \n",
+       "144                  27.0                  63.0  0.99160  3.68       0.79   \n",
+       "153                  25.0                  99.0  0.99500  3.35       0.54   \n",
+       "156                  29.0                 129.0  0.99730  3.42       0.72   \n",
+       "157                  28.0                 128.0  0.99730  3.42       0.71   \n",
+       "172                   6.0                  18.0  0.99720  3.29       0.61   \n",
+       "176                   7.0                  35.0  0.99610  3.33       0.47   \n",
+       "180                  10.0                  42.0  0.99690  3.19       0.59   \n",
+       "194                   7.0                  28.0  0.99640  3.28       0.55   \n",
+       "206                   9.0                  28.0  0.99940  3.20       0.77   \n",
+       "228                  29.0                  63.0  0.99615  3.37       0.58   \n",
+       "233                  10.0                  37.0  0.99685  3.46       0.50   \n",
+       "236                  14.0                  38.0  0.99675  3.37       0.58   \n",
+       "238                  14.0                  38.0  0.99675  3.37       0.58   \n",
+       "239                   7.0                  37.0  0.99685  3.32       0.55   \n",
+       "244                  10.0                  24.0  1.00005  3.07       0.84   \n",
+       "...                   ...                   ...      ...   ...        ...   \n",
+       "1383                 25.0                 105.0  0.99613  3.30       0.49   \n",
+       "1387                  8.0                  23.0  0.99610  3.30       0.58   \n",
+       "1401                 33.0                 141.0  0.99620  3.25       0.51   \n",
+       "1409                 40.0                  54.0  0.99500  3.54       0.93   \n",
+       "1412                  8.0                  22.0  0.99740  3.22       0.94   \n",
+       "1416                  3.0                  15.0  0.99940  3.20       0.78   \n",
+       "1420                  3.0                  19.0  0.99500  3.16       0.46   \n",
+       "1425                  8.0                  23.0  0.99740  3.26       0.70   \n",
+       "1435                 55.0                  95.0  1.00369  3.18       0.77   \n",
+       "1446                 18.0                  30.0  0.99712  3.40       0.75   \n",
+       "1450                 15.0                  28.0  0.99470  3.23       0.73   \n",
+       "1457                 27.0                  85.0  0.99706  3.41       0.58   \n",
+       "1465                 34.0                  53.0  0.99580  3.41       0.67   \n",
+       "1468                 31.0                  54.0  0.99728  3.30       0.65   \n",
+       "1476                 48.0                  82.0  1.00242  3.16       0.75   \n",
+       "1481                 10.0                  22.0  0.99828  3.39       0.68   \n",
+       "1491                  5.0                  13.0  0.99420  3.72       0.58   \n",
+       "1496                 23.0                 147.0  0.99636  3.26       0.59   \n",
+       "1499                  7.0                  16.0  0.99508  3.45       0.63   \n",
+       "1507                  5.0                  12.0  0.99605  3.36       0.55   \n",
+       "1520                 29.0                  44.0  0.99489  3.38       0.83   \n",
+       "1522                 23.0                  58.0  0.99633  3.42       0.97   \n",
+       "1557                 15.0                  23.0  0.99627  3.54       0.60   \n",
+       "1560                 31.0                 131.0  0.99622  3.21       0.52   \n",
+       "1561                 31.0                 131.0  0.99622  3.21       0.52   \n",
+       "1563                 12.0                  20.0  0.99546  3.29       0.54   \n",
+       "1564                 12.0                  20.0  0.99546  3.29       0.54   \n",
+       "1567                 12.0                  20.0  0.99546  3.29       0.54   \n",
+       "1581                 24.0                  32.0  0.99402  3.54       0.60   \n",
+       "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
+       "\n",
+       "      alcohol  quality  \n",
+       "4         9.4        5  \n",
+       "11       10.5        5  \n",
+       "27        9.5        5  \n",
+       "40       10.5        5  \n",
+       "65       10.9        5  \n",
+       "76       10.5        5  \n",
+       "91        9.9        6  \n",
+       "93        9.6        5  \n",
+       "102       9.0        6  \n",
+       "105       9.2        5  \n",
+       "114       9.5        5  \n",
+       "121      10.9        6  \n",
+       "132      13.0        5  \n",
+       "140       9.6        5  \n",
+       "141       9.5        5  \n",
+       "144      14.0        6  \n",
+       "153      10.1        5  \n",
+       "156      10.5        5  \n",
+       "157      10.5        5  \n",
+       "172       9.2        6  \n",
+       "176       9.5        5  \n",
+       "180       9.5        5  \n",
+       "194       9.7        5  \n",
+       "206      10.8        7  \n",
+       "228      10.5        6  \n",
+       "233      11.0        5  \n",
+       "236       9.0        6  \n",
+       "238       9.0        6  \n",
+       "239       9.0        6  \n",
+       "244       9.2        7  \n",
+       "...       ...      ...  \n",
+       "1383      9.9        5  \n",
+       "1387      9.6        5  \n",
+       "1401      9.9        5  \n",
+       "1409     10.7        6  \n",
+       "1412     10.9        6  \n",
+       "1416      9.6        5  \n",
+       "1420      9.8        5  \n",
+       "1425      9.6        6  \n",
+       "1435      9.0        6  \n",
+       "1446      9.8        5  \n",
+       "1450     11.3        7  \n",
+       "1457      9.0        5  \n",
+       "1465      9.7        5  \n",
+       "1468     10.0        7  \n",
+       "1476      8.8        5  \n",
+       "1481     10.6        5  \n",
+       "1491     11.4        5  \n",
+       "1496      9.7        5  \n",
+       "1499     11.5        6  \n",
+       "1507     11.4        6  \n",
+       "1520     10.3        6  \n",
+       "1522     10.6        5  \n",
+       "1557     11.0        6  \n",
+       "1560      9.9        5  \n",
+       "1561      9.9        5  \n",
+       "1563     10.1        5  \n",
+       "1564     10.1        5  \n",
+       "1567     10.1        5  \n",
+       "1581     11.3        5  \n",
+       "1596     11.0        6  \n",
+       "\n",
+       "[240 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Are there any duplicates\n",
+    "wine_Df[wine_Df.duplicated() == True]\n",
+    "#240 rows have instances of repetition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "fixed acidity           240\n",
+       "volatile acidity        240\n",
+       "citric acid             240\n",
+       "residual sugar          240\n",
+       "chlorides               240\n",
+       "free sulfur dioxide     240\n",
+       "total sulfur dioxide    240\n",
+       "density                 240\n",
+       "pH                      240\n",
+       "sulphates               240\n",
+       "alcohol                 240\n",
+       "quality                 240\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_Df[wine_Df.duplicated() == True].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "fixed acidity            2009.000000\n",
+       "volatile acidity          124.425000\n",
+       "citric acid                63.190000\n",
+       "residual sugar            630.250000\n",
+       "chlorides                  20.099000\n",
+       "free sulfur dioxide      3785.000000\n",
+       "total sulfur dioxide    10665.500000\n",
+       "density                   239.270480\n",
+       "pH                        796.470000\n",
+       "sulphates                 157.200000\n",
+       "alcohol                  2488.833333\n",
+       "quality                  1370.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_Df[wine_Df.duplicated() == True].sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CORRELATION AND HEATMAP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d4d22f3630>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wine_corr = wine_Df.corr()\n",
+    "\n",
+    "mask = np.zeros_like(wine_corr, dtype = np.bool)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "\n",
+    "plt.figure(figsize= (20, 10))\n",
+    "sns.heatmap(wine_corr, mask = mask, vmin = -1.2, vmax = 1.2, center = 0, linewidths=.1,\n",
+    "            cbar_kws = {\"shrink\":.5}, cmap = 'coolwarm', square = False, annot = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>fixed acidity</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.256131</td>\n",
+       "      <td>0.671703</td>\n",
+       "      <td>0.114777</td>\n",
+       "      <td>0.093705</td>\n",
+       "      <td>0.153794</td>\n",
+       "      <td>0.113181</td>\n",
+       "      <td>0.668047</td>\n",
+       "      <td>0.682978</td>\n",
+       "      <td>0.183006</td>\n",
+       "      <td>0.061668</td>\n",
+       "      <td>0.124052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <td>0.256131</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.552496</td>\n",
+       "      <td>0.001918</td>\n",
+       "      <td>0.061298</td>\n",
+       "      <td>0.010504</td>\n",
+       "      <td>0.076470</td>\n",
+       "      <td>0.022026</td>\n",
+       "      <td>0.234937</td>\n",
+       "      <td>0.260987</td>\n",
+       "      <td>0.202288</td>\n",
+       "      <td>0.390558</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>citric acid</th>\n",
+       "      <td>0.671703</td>\n",
+       "      <td>0.552496</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.143577</td>\n",
+       "      <td>0.203823</td>\n",
+       "      <td>0.060978</td>\n",
+       "      <td>0.035533</td>\n",
+       "      <td>0.364947</td>\n",
+       "      <td>0.541904</td>\n",
+       "      <td>0.312770</td>\n",
+       "      <td>0.109903</td>\n",
+       "      <td>0.226373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>residual sugar</th>\n",
+       "      <td>0.114777</td>\n",
+       "      <td>0.001918</td>\n",
+       "      <td>0.143577</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.055610</td>\n",
+       "      <td>0.187049</td>\n",
+       "      <td>0.203028</td>\n",
+       "      <td>0.355283</td>\n",
+       "      <td>0.085652</td>\n",
+       "      <td>0.005527</td>\n",
+       "      <td>0.042075</td>\n",
+       "      <td>0.013732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chlorides</th>\n",
+       "      <td>0.093705</td>\n",
+       "      <td>0.061298</td>\n",
+       "      <td>0.203823</td>\n",
+       "      <td>0.055610</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.005562</td>\n",
+       "      <td>0.047400</td>\n",
+       "      <td>0.200632</td>\n",
+       "      <td>0.265026</td>\n",
+       "      <td>0.371260</td>\n",
+       "      <td>0.221141</td>\n",
+       "      <td>0.128907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <td>0.153794</td>\n",
+       "      <td>0.010504</td>\n",
+       "      <td>0.060978</td>\n",
+       "      <td>0.187049</td>\n",
+       "      <td>0.005562</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.667666</td>\n",
+       "      <td>0.021946</td>\n",
+       "      <td>0.070377</td>\n",
+       "      <td>0.051658</td>\n",
+       "      <td>0.069408</td>\n",
+       "      <td>0.050656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <td>0.113181</td>\n",
+       "      <td>0.076470</td>\n",
+       "      <td>0.035533</td>\n",
+       "      <td>0.203028</td>\n",
+       "      <td>0.047400</td>\n",
+       "      <td>0.667666</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.071269</td>\n",
+       "      <td>0.066495</td>\n",
+       "      <td>0.042947</td>\n",
+       "      <td>0.205654</td>\n",
+       "      <td>0.185100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>density</th>\n",
+       "      <td>0.668047</td>\n",
+       "      <td>0.022026</td>\n",
+       "      <td>0.364947</td>\n",
+       "      <td>0.355283</td>\n",
+       "      <td>0.200632</td>\n",
+       "      <td>0.021946</td>\n",
+       "      <td>0.071269</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.341699</td>\n",
+       "      <td>0.148506</td>\n",
+       "      <td>0.496180</td>\n",
+       "      <td>0.174919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pH</th>\n",
+       "      <td>0.682978</td>\n",
+       "      <td>0.234937</td>\n",
+       "      <td>0.541904</td>\n",
+       "      <td>0.085652</td>\n",
+       "      <td>0.265026</td>\n",
+       "      <td>0.070377</td>\n",
+       "      <td>0.066495</td>\n",
+       "      <td>0.341699</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.196648</td>\n",
+       "      <td>0.205633</td>\n",
+       "      <td>0.057731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sulphates</th>\n",
+       "      <td>0.183006</td>\n",
+       "      <td>0.260987</td>\n",
+       "      <td>0.312770</td>\n",
+       "      <td>0.005527</td>\n",
+       "      <td>0.371260</td>\n",
+       "      <td>0.051658</td>\n",
+       "      <td>0.042947</td>\n",
+       "      <td>0.148506</td>\n",
+       "      <td>0.196648</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.093595</td>\n",
+       "      <td>0.251397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alcohol</th>\n",
+       "      <td>0.061668</td>\n",
+       "      <td>0.202288</td>\n",
+       "      <td>0.109903</td>\n",
+       "      <td>0.042075</td>\n",
+       "      <td>0.221141</td>\n",
+       "      <td>0.069408</td>\n",
+       "      <td>0.205654</td>\n",
+       "      <td>0.496180</td>\n",
+       "      <td>0.205633</td>\n",
+       "      <td>0.093595</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.476166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quality</th>\n",
+       "      <td>0.124052</td>\n",
+       "      <td>0.390558</td>\n",
+       "      <td>0.226373</td>\n",
+       "      <td>0.013732</td>\n",
+       "      <td>0.128907</td>\n",
+       "      <td>0.050656</td>\n",
+       "      <td>0.185100</td>\n",
+       "      <td>0.174919</td>\n",
+       "      <td>0.057731</td>\n",
+       "      <td>0.251397</td>\n",
+       "      <td>0.476166</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      fixed acidity  volatile acidity  citric acid  \\\n",
+       "fixed acidity              1.000000          0.256131     0.671703   \n",
+       "volatile acidity           0.256131          1.000000     0.552496   \n",
+       "citric acid                0.671703          0.552496     1.000000   \n",
+       "residual sugar             0.114777          0.001918     0.143577   \n",
+       "chlorides                  0.093705          0.061298     0.203823   \n",
+       "free sulfur dioxide        0.153794          0.010504     0.060978   \n",
+       "total sulfur dioxide       0.113181          0.076470     0.035533   \n",
+       "density                    0.668047          0.022026     0.364947   \n",
+       "pH                         0.682978          0.234937     0.541904   \n",
+       "sulphates                  0.183006          0.260987     0.312770   \n",
+       "alcohol                    0.061668          0.202288     0.109903   \n",
+       "quality                    0.124052          0.390558     0.226373   \n",
+       "\n",
+       "                      residual sugar  chlorides  free sulfur dioxide  \\\n",
+       "fixed acidity               0.114777   0.093705             0.153794   \n",
+       "volatile acidity            0.001918   0.061298             0.010504   \n",
+       "citric acid                 0.143577   0.203823             0.060978   \n",
+       "residual sugar              1.000000   0.055610             0.187049   \n",
+       "chlorides                   0.055610   1.000000             0.005562   \n",
+       "free sulfur dioxide         0.187049   0.005562             1.000000   \n",
+       "total sulfur dioxide        0.203028   0.047400             0.667666   \n",
+       "density                     0.355283   0.200632             0.021946   \n",
+       "pH                          0.085652   0.265026             0.070377   \n",
+       "sulphates                   0.005527   0.371260             0.051658   \n",
+       "alcohol                     0.042075   0.221141             0.069408   \n",
+       "quality                     0.013732   0.128907             0.050656   \n",
+       "\n",
+       "                      total sulfur dioxide   density        pH  sulphates  \\\n",
+       "fixed acidity                     0.113181  0.668047  0.682978   0.183006   \n",
+       "volatile acidity                  0.076470  0.022026  0.234937   0.260987   \n",
+       "citric acid                       0.035533  0.364947  0.541904   0.312770   \n",
+       "residual sugar                    0.203028  0.355283  0.085652   0.005527   \n",
+       "chlorides                         0.047400  0.200632  0.265026   0.371260   \n",
+       "free sulfur dioxide               0.667666  0.021946  0.070377   0.051658   \n",
+       "total sulfur dioxide              1.000000  0.071269  0.066495   0.042947   \n",
+       "density                           0.071269  1.000000  0.341699   0.148506   \n",
+       "pH                                0.066495  0.341699  1.000000   0.196648   \n",
+       "sulphates                         0.042947  0.148506  0.196648   1.000000   \n",
+       "alcohol                           0.205654  0.496180  0.205633   0.093595   \n",
+       "quality                           0.185100  0.174919  0.057731   0.251397   \n",
+       "\n",
+       "                       alcohol   quality  \n",
+       "fixed acidity         0.061668  0.124052  \n",
+       "volatile acidity      0.202288  0.390558  \n",
+       "citric acid           0.109903  0.226373  \n",
+       "residual sugar        0.042075  0.013732  \n",
+       "chlorides             0.221141  0.128907  \n",
+       "free sulfur dioxide   0.069408  0.050656  \n",
+       "total sulfur dioxide  0.205654  0.185100  \n",
+       "density               0.496180  0.174919  \n",
+       "pH                    0.205633  0.057731  \n",
+       "sulphates             0.093595  0.251397  \n",
+       "alcohol               1.000000  0.476166  \n",
+       "quality               0.476166  1.000000  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(wine_corr.abs())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ML: PRE-PROCESSING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train - Test SPLIT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#For faster computation, we convert our data into an array\n",
+    "array = wine_Df.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_Df.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = array[:, 0:10]\n",
+    "y = array[:, 11] #Outcome ~['quality']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "FEATURE SCALING\n",
+    "\n",
+    "This is done because of the disparity within the data set, to normalize the distribution and equate the influence of variables [0,1].\n",
+    "To manage the validity of our results, we are just going to scale all our predictor variables, the outcome variable ~ quality remains un-adjusted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_scaled = preprocessing.scale(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-2.29584994e-15, -2.08243709e-15,  2.25665976e-15, -2.09067199e-15,\n",
+       "        6.82749954e-15,  2.53619983e-17,  8.57223140e-17,  5.14567239e-13,\n",
+       "       -5.62929705e-15, -4.22787612e-15])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_scaled.mean(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_scaled.std(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[15.5      0.645    0.49    ...  1.00315  2.92     0.74   ]\n",
+      " [ 9.9      0.49     0.58    ...  1.0004   3.29     0.58   ]\n",
+      " [12.       0.63     0.5     ...  0.99791  3.07     0.6    ]\n",
+      " ...\n",
+      " [ 8.4      0.65     0.6     ...  0.9973   3.2      0.52   ]\n",
+      " [ 7.7      0.23     0.37    ...  0.9971   3.41     0.71   ]\n",
+      " [ 7.4      0.6      0.26    ...  0.99616  3.29     0.56   ]] \n",
+      "\n",
+      "[[ 4.08678018  0.66573121  1.11905348 ...  3.42065844 -2.55479755\n",
+      "   0.48669525]\n",
+      " [ 0.91301863 -0.2079985   1.57869652 ...  1.9616843  -0.15933855\n",
+      "  -0.45370964]\n",
+      " [ 2.10317921  0.58117672  1.17012493 ...  0.64064954 -1.58366553\n",
+      "  -0.33615903]\n",
+      " ...\n",
+      " [ 0.06290393  0.69391604  1.68083942 ...  0.31702255 -0.74201777\n",
+      "  -0.80636147]\n",
+      " [-0.33381626 -1.67360962  0.5061961  ...  0.21091534  0.61756707\n",
+      "   0.31036933]\n",
+      " [-0.5038392   0.41206774 -0.05558983 ... -0.28778855 -0.15933855\n",
+      "  -0.57126025]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_train, '\\n')\n",
+    "\n",
+    "print(X_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ML APPLICATION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Selecting a ML Algorithm is quite dependent on the type of our outcome variable =, which in this case is quantitative and makes regression techniques suitable for the analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Selected Models\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.svm import SVC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LR: [mean]0.532095 [std](0.035148)\n",
+      "LDA: [mean]0.566724 [std](0.048011)\n",
+      "KNN: [mean]0.460754 [std](0.045821)\n",
+      "CART: [mean]0.581129 [std](0.044136)\n",
+      "NB: [mean]0.467740 [std](0.051505)\n",
+      "SVM: [mean]0.497203 [std](0.039654)\n"
+     ]
+    }
+   ],
+   "source": [
+    "models = []\n",
+    "\n",
+    "models.append(('LR', LogisticRegression(solver='liblinear', \n",
+    "                                        multi_class = 'ovr')))\n",
+    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
+    "models.append(('KNN', KNeighborsClassifier()))\n",
+    "models.append(('CART', DecisionTreeClassifier()))\n",
+    "models.append(('NB', GaussianNB()))\n",
+    "models.append(('SVM', SVC(gamma='auto')))\n",
+    "\n",
+    "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
+    "\n",
+    "results = []\n",
+    "names = []\n",
+    "for name, model in models:\n",
+    "    kfold = StratifiedKFold(n_splits = 10, random_state = 1, shuffle = True)\n",
+    "    cv_results = cross_val_score(model, X_train, y_train, cv=kfold, \n",
+    "                                 scoring = 'accuracy')\n",
+    "    results.append(cv_results)\n",
+    "    names.append(name)\n",
+    "    print('%s: [mean]%f [std](%f)' % (name, cv_results.mean(), cv_results.std()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the previous chunk, we examined various suitable model algorithms at once to pick the most suitable model, based on the mean and std-dv.\n",
+    "Let's try the LDA model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Score: 0.56658\n",
+      "Best Parameters: {'solver': 'svd'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Search most suitable parameters\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "LDA = LinearDiscriminantAnalysis()\n",
+    "\n",
+    "parameter_grid = {'solver': ['svd', 'lsqr', 'eigen']}\n",
+    "\n",
+    "grid_search = GridSearchCV(LDA, param_grid = parameter_grid,\n",
+    "                          cv = kfold)\n",
+    "\n",
+    "\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Best Score: {:.5f}\".format(grid_search.best_score_))\n",
+    "print(\"Best Parameters: {}\".format(grid_search.best_params_))"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Evaluation\n",
+    "### MODEL: Linear Discriminant Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LDA = LinearDiscriminantAnalysis(solver = 'svd')\n",
+    "\n",
+    "LDA.fit(X_train, y_train)\n",
     "\n",
-    "- Select the best performing model and write your comments about why choose this model.\n",
-    "- Analyse results and make comment about how you can improve model."
+    "predictions = LDA.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### MODEL EVALUATION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5875 \n",
+      "\n",
+      "[[  0   0   0   0   0   0]\n",
+      " [  0   0   7   3   1   0]\n",
+      " [  3   1 148  52   2   0]\n",
+      " [  0   1  71 113  16   0]\n",
+      " [  0   0   7  28  21   0]\n",
+      " [  0   0   0   4   2   0]] \n",
+      "\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         3.0       0.00      0.00      0.00         0\n",
+      "         4.0       0.00      0.00      0.00        11\n",
+      "         5.0       0.64      0.72      0.67       206\n",
+      "         6.0       0.56      0.56      0.56       201\n",
+      "         7.0       0.50      0.38      0.43        56\n",
+      "         8.0       0.00      0.00      0.00         6\n",
+      "\n",
+      "   micro avg       0.59      0.59      0.59       480\n",
+      "   macro avg       0.28      0.28      0.28       480\n",
+      "weighted avg       0.57      0.59      0.58       480\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(accuracy_score(y_test, predictions), '\\n')\n",
+    "print(confusion_matrix(y_test, predictions), '\\n')\n",
+    "print(classification_report(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Predictions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Predictions\n",
+       "0          5.0\n",
+       "1          5.0\n",
+       "2          6.0\n",
+       "3          5.0\n",
+       "4          6.0"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions = pd.DataFrame(predictions)\n",
+    "predictions.rename(columns = {0:'Predictions'}, inplace =True)\n",
+    "predictions.head()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "X_test.length()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "auctual = pd.DataFrame(y_test)\n",
+    "auctual.rename(columns = {0: 'Auctual'}, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>Auctual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Auctual\n",
+       "0      5.0\n",
+       "1      5.0\n",
+       "2      6.0\n",
+       "3      6.0\n",
+       "4      6.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auctual.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = pd.concat([predictions, auctual], axis = 1, join = 'inner')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>Predictions</th>\n",
+       "      <th>Auctual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Predictions  Auctual\n",
+       "0          5.0      5.0\n",
+       "1          5.0      5.0\n",
+       "2          6.0      6.0\n",
+       "3          5.0      6.0\n",
+       "4          6.0      6.0"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions.rename(columns={0: 'Predictions', 0: 'Auctual'}, inplace = True)\n",
+    "predictions.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission = predictions.to_csv(\"C:/Users/HP/PycharmProjects/GlobalAi/red-white-wine-dataset/predictions.csv\",index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## REMARK:\n",
+    "    We see a lot of mis-classification, the predictions are barely accurate and would require better tuning of parameters or the complete overhaul and selecting better models, regression techniques, accuracy scoring..."
    ]
   }
  ],
@@ -445,9 +2945,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

From 687c17e1f44c7f6c8415115fee58344dd5cb2bbc Mon Sep 17 00:00:00 2001
From: Akan Daniel <51209193+AkanimohOD19A@users.noreply.github.com>
Date: Sat, 28 Nov 2020 23:08:02 +0100
Subject: [PATCH 2/2] Rename Project to reflect AMAH Akan Daniel

Rename title to reflect name
---
 ...19-11-2020 ML Course Nigeria Project 'AMAH Akan Daniel'.ipynb} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Project/{19-11-2020 ML Course Nigeria Project 'name'.ipynb => 19-11-2020 ML Course Nigeria Project 'AMAH Akan Daniel'.ipynb} (100%)

diff --git a/Project/19-11-2020 ML Course Nigeria Project 'name'.ipynb b/Project/19-11-2020 ML Course Nigeria Project 'AMAH Akan Daniel'.ipynb
similarity index 100%
rename from Project/19-11-2020 ML Course Nigeria Project 'name'.ipynb
rename to Project/19-11-2020 ML Course Nigeria Project 'AMAH Akan Daniel'.ipynb