pHDataProcessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6N3Yx7hJQCNx"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pV8gfNwAQCN1"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('ph-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "AzPlneQnQCN4",
    "outputId": "cb64636f-84f2-4eaf-a52b-826c6ed7e511"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>blue</th>\n",
       "      <th>green</th>\n",
       "      <th>red</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>231</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>84</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>164</td>\n",
       "      <td>255</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>205</td>\n",
       "      <td>255</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>223</td>\n",
       "      <td>221</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   blue  green  red  label\n",
       "0    36     27  231      0\n",
       "1    36     84  250      1\n",
       "2    37    164  255      2\n",
       "3    22    205  255      3\n",
       "4    38    223  221      4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "wRttdB6-QCN9",
    "outputId": "79d56643-9367-4223-ac40-4ed6f2aa0232"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 653 entries, 0 to 652\n",
      "Data columns (total 4 columns):\n",
      "blue     653 non-null int64\n",
      "green    653 non-null int64\n",
      "red      653 non-null int64\n",
      "label    653 non-null int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 20.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "X5cqVk_cQCOB",
    "outputId": "55d07224-bef9-4ad2-c0e1-6a0f69090df1"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>blue</th>\n",
       "      <th>green</th>\n",
       "      <th>red</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>89.290965</td>\n",
       "      <td>130.094946</td>\n",
       "      <td>120.655436</td>\n",
       "      <td>7.055130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>74.974850</td>\n",
       "      <td>72.487085</td>\n",
       "      <td>89.581264</td>\n",
       "      <td>4.287951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             blue       green         red       label\n",
       "count  653.000000  653.000000  653.000000  653.000000\n",
       "mean    89.290965  130.094946  120.655436    7.055130\n",
       "std     74.974850   72.487085   89.581264    4.287951\n",
       "min      0.000000    0.000000    0.000000    0.000000\n",
       "25%     22.000000   68.000000   52.000000    3.000000\n",
       "50%     67.000000  146.000000   90.000000    7.000000\n",
       "75%    162.000000  189.000000  224.000000   11.000000\n",
       "max    255.000000  255.000000  255.000000   14.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "buKOgMNBQCOE"
   },
   "outputs": [],
   "source": [
    "# Mean label is 7.05 which shows that this the average pH is around 7 which is neutral.\n",
    "# Min pH is 0 and max pH is 14.\n",
    "# Thus data obtained is optimal for training our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LlUtkycCQCOH"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "0byODb_GQCOL",
    "outputId": "3f9e0d6d-4009-4973-8dfd-d5e2fe5eb40a",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['label'].value_counts().sort_index())\n",
    "plt.xlabel('pH values')\n",
    "plt.ylabel('Value Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "xLQE5vZuQCOO",
    "outputId": "b4386711-3085-4404-9f25-7d4756eca952"
   },
   "outputs": [
    {
     "data": {
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       "      <td>11</td>\n",
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       "      <td>12</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>653 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     blue  green  red  label\n",
       "0      36     27  231      0\n",
       "1      36     84  250      1\n",
       "2      37    164  255      2\n",
       "3      22    205  255      3\n",
       "4      38    223  221      4\n",
       "..    ...    ...  ...    ...\n",
       "648   201    123    0     10\n",
       "649   203     51   46     11\n",
       "650   169     62   48     12\n",
       "651   173     37   79     13\n",
       "652   131      2   77     14\n",
       "\n",
       "[653 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JeROFo9bQCOR"
   },
   "outputs": [],
   "source": [
    "# Find duplicate values and NA in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xlNukzDiQCOW"
   },
   "outputs": [],
   "source": [
    "duplicate_rows = df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GpwOGwO0QCOa"
   },
   "outputs": [],
   "source": [
    "null_rows = df[df.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dZYymhoMQCOd"
   },
   "outputs": [],
   "source": [
    "df.loc[df['label'] < 7, 'Nature'] = 'Acidic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ktzu3o3pQCOg"
   },
   "outputs": [],
   "source": [
    "df.loc[df['label'] == 7, 'Nature'] = 'Neutral'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h69rmRm1QCOj"
   },
   "outputs": [],
   "source": [
    "df.loc[df['label'] > 7, 'Nature'] = 'Basic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "TGVvcQPLQCOm",
    "outputId": "5a12c78f-6743-493d-acdb-59218b025b82"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Basic      307\n",
       "Acidic     302\n",
       "Neutral     44\n",
       "Name: Nature, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Nature'].value_counts()\n",
    "# Nature Column gives info about the nature of the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z-ojqodnQCOq"
   },
   "outputs": [],
   "source": [
    "df_Nature = df.groupby('Nature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "colab_type": "code",
    "id": "mUxZtparQCOt",
    "outputId": "ce836676-6dc5-402a-a405-a907dcb890c8"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "    blue  green  red  label   Nature\n",
       "0     36     27  231      0   Acidic\n",
       "1     36     84  250      1   Acidic\n",
       "2     37    164  255      2   Acidic\n",
       "3     22    205  255      3   Acidic\n",
       "4     38    223  221      4   Acidic\n",
       "7     13    156    0      7  Neutral\n",
       "8     92    166    0      8    Basic\n",
       "9    184    191    0      9    Basic\n",
       "10   200    136    1     10    Basic\n",
       "11   198     76    0     11    Basic\n",
       "12   180     38   51     12    Basic\n",
       "22    75    169   51      7  Neutral\n",
       "37    73    170   52      7  Neutral\n",
       "52    73    169   51      7  Neutral\n",
       "67     0    153    0      7  Neutral"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Nature.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u0nS5WdKQCOw"
   },
   "outputs": [],
   "source": [
    "# Training our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "0FdrLgoRQCOz",
    "outputId": "2bb7e279-990c-44de-9049-a7031b35585b"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   blue  green  red  label  Nature\n",
       "0    36     27  231      0  Acidic\n",
       "1    36     84  250      1  Acidic\n",
       "2    37    164  255      2  Acidic\n",
       "3    22    205  255      3  Acidic\n",
       "4    38    223  221      4  Acidic"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I2J6_MEUQCO4"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 766
    },
    "colab_type": "code",
    "id": "KdgMUww9QCO9",
    "outputId": "117e039f-abfd-4074-fb6d-76419d7073d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fba82a9b5c0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "colab_type": "code",
    "id": "LdKmL00aQCPD",
    "outputId": "b32e94ba-996e-44eb-f960-fcc84cde1d18"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba82239ef0>"
      ]
     },
     "execution_count": 24,
     "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": [
    "sns.distplot(df['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "e0LWQzJhQCPG",
    "outputId": "e71e0fb3-9d44-4bc2-ca0e-e440ee0f7725"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba807badd8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())\n",
    "# No positive correlation can be observed bwetween 2 columns\n",
    "# But interestingly we can see high negative correlation between red-blue \n",
    "# columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jir7d9QRQCPJ"
   },
   "outputs": [],
   "source": [
    "# Splitting into train and test test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "CEWgZmxEQCPN",
    "outputId": "2236200f-4721-4b5b-db55-8cac3a07b91c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['blue', 'green', 'red', 'label', 'Nature'], dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "igsZVnQKQCPQ"
   },
   "outputs": [],
   "source": [
    "X = df[['blue', 'green', 'red']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I8DBOwN7QCPT"
   },
   "outputs": [],
   "source": [
    "y = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "stYO32HQQCPV"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SG52SvknQCPY"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0e84dxMCQCPb"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DUOS4MQiQCPd"
   },
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "Faek_E3YQCPh",
    "outputId": "c2042b77-30cf-4471-fdf9-f718d890fba8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ERS2C81CQCPk",
    "outputId": "3287a3c6-175c-4a39-e797-3cf1e6c683be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.972687221289089"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "HMzb7LJ1QCPp",
    "outputId": "9afe0c64-1168-43fc-842e-cb81bc39c976"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.0258191 , -0.004937  , -0.02186446])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E9sFSd52QCPr"
   },
   "outputs": [],
   "source": [
    "cdf = pd.DataFrame(lm.coef_, X.columns, columns=['Coefficients'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "colab_type": "code",
    "id": "9Pnz5mK6QCPu",
    "outputId": "fda21813-4732-4266-c5fb-b7aebe0bd24d"
   },
   "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>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>blue</td>\n",
       "      <td>0.025819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>green</td>\n",
       "      <td>-0.004937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>red</td>\n",
       "      <td>-0.021864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Coefficients\n",
       "blue       0.025819\n",
       "green     -0.004937\n",
       "red       -0.021864"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vi8MJEiOQCPx"
   },
   "source": [
    "Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BoD2yLtiQCPy"
   },
   "outputs": [],
   "source": [
    "pred_LinearReg = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "1m7d-pIhQCP1",
    "outputId": "d40a6194-02d1-46a6-dd8b-10f15c2e8935"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fba780f26d8>"
      ]
     },
     "execution_count": 40,
     "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": [
    "plt.scatter(y_test, pred_LinearReg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XTNmxltTQCP4"
   },
   "source": [
    "Thus our predictions are quite far from the actual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 598
    },
    "colab_type": "code",
    "id": "1NcIRc-6QCP5",
    "outputId": "0a5aeb03-935d-4a20-9106-1e1c68a9d58e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.95967966,  1.29771303,  9.58734594,  3.25775227,  2.54288997,\n",
       "        7.29341622,  7.21498137,  9.89589627,  9.52105127,  3.57317101,\n",
       "        9.85413207,  7.90804147,  1.21237168,  9.24602715,  1.32451449,\n",
       "        9.40980186,  6.72713775,  9.41444543, 11.75410858,  9.45499989,\n",
       "        1.16413582,  5.80210584,  3.53815727, 11.31743126,  7.18178538,\n",
       "        9.1032795 ,  9.52105127, 10.10750878,  7.75917279, 13.7271411 ,\n",
       "        5.80210584,  4.29028821,  2.0574497 , 11.29842162, 11.02416232,\n",
       "        2.57712072,  9.52105127, 12.70965636,  1.17894681,  8.99975328,\n",
       "       11.56163808,  2.8197243 ,  9.78768289,  3.1024656 ,  1.18768669,\n",
       "        5.04987262,  1.41408878,  4.1778976 ,  7.92310555, 10.93174229,\n",
       "        2.04245745,  1.58378055,  2.14546407, 11.87230587,  9.1032795 ,\n",
       "        2.1687619 ,  5.42228196,  1.41408878,  3.65554971,  2.43506047,\n",
       "        2.07750169,  7.53816396, 12.34753968, 12.5550748 ,  9.78768289,\n",
       "        5.19257314, 11.76335518,  3.0626157 ,  1.94729302,  3.11810395,\n",
       "        9.49182709,  7.78366165, 11.06389851,  3.1024656 ,  7.20979128,\n",
       "        7.95967966,  9.92171536,  9.64717446,  4.60556098,  9.67369812,\n",
       "       10.5887837 , 11.82850083,  2.99690008,  7.60256751,  3.07856081,\n",
       "       11.93930361, 11.19005979,  9.85413207,  7.27143001,  6.76086612,\n",
       "        3.95683202, 11.02416232,  9.54464926, 11.24169798,  4.50614274,\n",
       "        8.93894615,  3.3283363 ,  2.4648975 ,  1.66375486, 10.57185624,\n",
       "        5.82858386,  3.43501502,  5.41739214,  9.41444543,  9.78768289,\n",
       "        3.07504905,  9.85413207,  9.83139947,  5.72945901, 11.77945204,\n",
       "        9.02744055,  9.30877853,  7.90804147,  8.51081842,  1.58378484,\n",
       "       11.47867812,  4.60556098,  9.32194977,  9.54203041,  5.15267382,\n",
       "        7.27044765, 10.52942982,  5.5777698 ,  4.29127056,  5.27512392,\n",
       "       10.96308872,  4.29127056,  2.04245745, 12.06427502,  4.35449966,\n",
       "        3.01207788,  3.04296855, 12.82821083, 11.76350693, 12.34086964,\n",
       "       10.86845518,  3.29019478,  2.1687619 , 11.74050837,  4.83614459,\n",
       "        5.05033262, 11.44934726, 10.5887837 ,  3.21363464,  1.52904782,\n",
       "        3.81230141,  3.49191617, 11.02416232, 11.92696346, 10.25715764,\n",
       "        9.30877853,  9.59243745,  7.96221809,  3.68163748,  3.42171348,\n",
       "        9.41444543, 11.72766209,  3.83798587, 12.24677123,  5.5777698 ,\n",
       "       10.6731427 ,  3.00604052, 11.9233585 ,  9.54203041])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_LinearReg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "colab_type": "code",
    "id": "T5U0qN3tQCP8",
    "outputId": "0899758b-8253-4b9a-dc34-6c1573f6c939"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba780b27b8>"
      ]
     },
     "execution_count": 42,
     "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": [
    "sns.distplot((y_test-pred_LinearReg), label=\"Distribution plot of residuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-wbU4S0LQCP-"
   },
   "outputs": [],
   "source": [
    "# It vaguely resembles normal dist plot but is still quite far"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ajyEKIX_QCQA"
   },
   "source": [
    "Evaluation Matrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3jPAaCkrQCQB"
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "F05rDm7rQCQG",
    "outputId": "acea3c51-c575-4c23-d0ac-1aa66820648c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8821920059984991"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_test, pred_LinearReg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "MLO1etrlQCQK",
    "outputId": "d787f6a5-15da-4d22-c175-5c3a034d5585"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.560619639059021"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_test, pred_LinearReg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Ixy80h2-QCQP",
    "outputId": "4fea1ea9-dca3-4c37-bdde-85582b53587b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.358096613597293"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(metrics.mean_squared_error(y_test, pred_LinearReg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6I2TprqoQCQV"
   },
   "source": [
    "\n",
    "Logistic Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "77YxAcfFQCQW"
   },
   "outputs": [],
   "source": [
    "# Used to predict discrete values only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "TbSG-8ztQCQb",
    "outputId": "c058978d-a408-47de-fa14-7404402b3a66"
   },
   "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>blue</th>\n",
       "      <th>green</th>\n",
       "      <th>red</th>\n",
       "      <th>label</th>\n",
       "      <th>Nature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>231</td>\n",
       "      <td>0</td>\n",
       "      <td>Acidic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>84</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>Acidic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>164</td>\n",
       "      <td>255</td>\n",
       "      <td>2</td>\n",
       "      <td>Acidic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>205</td>\n",
       "      <td>255</td>\n",
       "      <td>3</td>\n",
       "      <td>Acidic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>223</td>\n",
       "      <td>221</td>\n",
       "      <td>4</td>\n",
       "      <td>Acidic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>648</td>\n",
       "      <td>201</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Basic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>649</td>\n",
       "      <td>203</td>\n",
       "      <td>51</td>\n",
       "      <td>46</td>\n",
       "      <td>11</td>\n",
       "      <td>Basic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650</td>\n",
       "      <td>169</td>\n",
       "      <td>62</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>Basic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>651</td>\n",
       "      <td>173</td>\n",
       "      <td>37</td>\n",
       "      <td>79</td>\n",
       "      <td>13</td>\n",
       "      <td>Basic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>652</td>\n",
       "      <td>131</td>\n",
       "      <td>2</td>\n",
       "      <td>77</td>\n",
       "      <td>14</td>\n",
       "      <td>Basic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>653 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     blue  green  red  label  Nature\n",
       "0      36     27  231      0  Acidic\n",
       "1      36     84  250      1  Acidic\n",
       "2      37    164  255      2  Acidic\n",
       "3      22    205  255      3  Acidic\n",
       "4      38    223  221      4  Acidic\n",
       "..    ...    ...  ...    ...     ...\n",
       "648   201    123    0     10   Basic\n",
       "649   203     51   46     11   Basic\n",
       "650   169     62   48     12   Basic\n",
       "651   173     37   79     13   Basic\n",
       "652   131      2   77     14   Basic\n",
       "\n",
       "[653 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "colab_type": "code",
    "id": "EcNLMU7LQCQg",
    "outputId": "6fc48345-0e74-4b65-aa8b-2340be3e658a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba7802eda0>"
      ]
     },
     "execution_count": 50,
     "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": [
    "sns.countplot(x='label', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ni3y0DPlQCQl"
   },
   "outputs": [],
   "source": [
    "# Deal with Nature Column as a dummy variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "ZH8LQg3SQCQo",
    "outputId": "f2b20224-2013-4db8-98fe-292be734f788"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Acidic</th>\n",
       "      <th>Basic</th>\n",
       "      <th>Neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>648</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>649</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>651</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>652</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>653 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Acidic  Basic  Neutral\n",
       "0         1      0        0\n",
       "1         1      0        0\n",
       "2         1      0        0\n",
       "3         1      0        0\n",
       "4         1      0        0\n",
       "..      ...    ...      ...\n",
       "648       0      1        0\n",
       "649       0      1        0\n",
       "650       0      1        0\n",
       "651       0      1        0\n",
       "652       0      1        0\n",
       "\n",
       "[653 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df['Nature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lE5IXWieQCQr"
   },
   "outputs": [],
   "source": [
    "# Use OHE to work on categorical data(WIP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wmoWkrsTQCQv"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5z9l3E6bQCQy"
   },
   "outputs": [],
   "source": [
    "enc = OneHotEncoder(handle_unknown='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Lxd4xHR1QCQ3"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vgRzCRlfQCQ5"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-YvpVi7-QCQ-"
   },
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 190
    },
    "colab_type": "code",
    "id": "hxsuy-7EQCRA",
    "outputId": "a318cbce-1219-47d9-e47f-a613886277f4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nitish/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/nitish/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2T7CQNK9QCRD"
   },
   "outputs": [],
   "source": [
    "pred_LogReg = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OsFCGj_CQCRF"
   },
   "outputs": [],
   "source": [
    "# Class Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "colab_type": "code",
    "id": "NRYbkSfkQCRI",
    "outputId": "11292430-230c-4db4-9b6b-c46ffcfcf006"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.40      0.80      0.53         5\n",
      "           1       0.60      0.90      0.72        10\n",
      "           2       0.50      0.08      0.13        13\n",
      "           3       0.30      0.25      0.27        12\n",
      "           4       0.17      0.67      0.28         6\n",
      "           5       0.62      0.36      0.45        14\n",
      "           6       0.75      0.55      0.63        11\n",
      "           7       0.69      0.75      0.72        12\n",
      "           8       1.00      0.64      0.78        14\n",
      "           9       0.67      0.67      0.67        12\n",
      "          10       0.45      0.62      0.53         8\n",
      "          11       0.39      0.88      0.54         8\n",
      "          12       0.00      0.00      0.00        19\n",
      "          13       0.62      0.80      0.70        10\n",
      "          14       0.67      0.80      0.73        10\n",
      "\n",
      "    accuracy                           0.52       164\n",
      "   macro avg       0.52      0.58      0.51       164\n",
      "weighted avg       0.52      0.52      0.49       164\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nitish/.local/lib/python3.6/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, pred_LogReg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "mQpvWOT1QCRL",
    "outputId": "c9e25e1c-a7bc-46e4-8005-36166fee1dbe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  9  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 3  3  1  3  3  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 2  1  0  3  6  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  0  1  4  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  1  1  5  5  2  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  1  1  1  6  2  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  1  2  0  0  9  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  2  1  0  1  9  1  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  1  0  0  0  8  3  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  1  0  1  5  1  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  1  7  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  2  1 10  0  4  2]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  8  2]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  1  0  0  1  8]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.confusion_matrix(y_test, pred_LogReg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "YOCYyaE6QCRN",
    "outputId": "0aa67ab9-524d-4f98-874a-63076a00d201"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fba77ee8550>"
      ]
     },
     "execution_count": 63,
     "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": [
    "plt.scatter(y_test, pred_LogReg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "JCXe6_dnQCRQ",
    "outputId": "43eee123-126b-423c-ac9f-ad069ebe17c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8821920059984991\n",
      "0.774390243902439\n"
     ]
    }
   ],
   "source": [
    "print(metrics.mean_absolute_error(y_test, pred_LinearReg))\n",
    "print(metrics.mean_absolute_error(y_test, pred_LogReg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "yDMsms3kQCRU",
    "outputId": "f8c60597-1db5-445a-95aa-7c43eff88bca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.358096613597293\n",
      "1.299624711308577\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(metrics.mean_squared_error(y_test, pred_LinearReg)))\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, pred_LogReg)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sYeYlrH7QCRW"
   },
   "outputs": [],
   "source": [
    "# We can see considerable improvements in using Logistic Regression\n",
    "# over Linear Regression.\n",
    "# Mean Error is now only 0.77 compared to 1.88.\n",
    "# Mean Squared Error is also now only 1.29 compared to 2.35."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "colab_type": "code",
    "id": "EPd4Y0_hQCRZ",
    "outputId": "69e28743-90f1-4e55-d32f-1b632d633b19",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Actual - Predicted using Logistic Regression')"
      ]
     },
     "execution_count": 67,
     "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": [
    "sns.distplot((y_test-pred_LogReg))\n",
    "plt.xlabel('Actual - Predicted using Logistic Regression')\n",
    "# Residual plot is also now closer to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0k6kPSe4QCRb"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nEGZAL9emjcL"
   },
   "source": [
    "##K Nearest Neighbours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BuQxL9xgmtLa"
   },
   "source": [
    "Step 1 - Scale the data as KNN depends on distance between the points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Zltom2annSSv"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oBDdlaPHnaYU"
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "lP6V99FvneqS",
    "outputId": "7c491c35-15a5-45b6-8678-25e74e6dd484"
   },
   "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>blue</th>\n",
       "      <th>green</th>\n",
       "      <th>red</th>\n",
       "      <th>label</th>\n",
       "      <th>Nature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>231</td>\n",
       "      <td>0</td>\n",
       "      <td>Acidic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>84</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>Acidic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>164</td>\n",
       "      <td>255</td>\n",
       "      <td>2</td>\n",
       "      <td>Acidic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>205</td>\n",
       "      <td>255</td>\n",
       "      <td>3</td>\n",
       "      <td>Acidic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>223</td>\n",
       "      <td>221</td>\n",
       "      <td>4</td>\n",
       "      <td>Acidic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   blue  green  red  label  Nature\n",
       "0    36     27  231      0  Acidic\n",
       "1    36     84  250      1  Acidic\n",
       "2    37    164  255      2  Acidic\n",
       "3    22    205  255      3  Acidic\n",
       "4    38    223  221      4  Acidic"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_lCocK4Znwxo",
    "outputId": "d1f415d8-243a-43db-ba45-3b1c42007001"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df.drop([\"label\",\"Nature\"], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0C67Ueu5oBF_"
   },
   "outputs": [],
   "source": [
    "scaled_feat = scaler.transform(df.drop([\"label\",\"Nature\"], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "X8NzqnbbrdB-"
   },
   "outputs": [],
   "source": [
    "df_feat = pd.DataFrame(scaled_feat, columns=df.columns[:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "y6YfvjJYrogS",
    "outputId": "47266b55-7df0-43c8-d9cb-fb371d25ec66"
   },
   "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>blue</th>\n",
       "      <th>green</th>\n",
       "      <th>red</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.711329</td>\n",
       "      <td>-1.423343</td>\n",
       "      <td>1.232726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.711329</td>\n",
       "      <td>-0.636393</td>\n",
       "      <td>1.444986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.697981</td>\n",
       "      <td>0.468098</td>\n",
       "      <td>1.500845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.898202</td>\n",
       "      <td>1.034149</td>\n",
       "      <td>1.500845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.684633</td>\n",
       "      <td>1.282660</td>\n",
       "      <td>1.121010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       blue     green       red\n",
       "0 -0.711329 -1.423343  1.232726\n",
       "1 -0.711329 -0.636393  1.444986\n",
       "2 -0.697981  0.468098  1.500845\n",
       "3 -0.898202  1.034149  1.500845\n",
       "4 -0.684633  1.282660  1.121010"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4hoOuOE5rvZW"
   },
   "outputs": [],
   "source": [
    "XKNN = df_feat\n",
    "yKNN = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VfG0RYq1sNtp"
   },
   "outputs": [],
   "source": [
    "X_trainKNN, X_testKNN, y_trainKNN, y_testKNN = train_test_split(X, y, test_size=0.25, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7X80gkjYs_Ld"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sXbgE8ixtjEz"
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "id": "qYbVbOqWuKIH",
    "outputId": "48235b47-e945-4b2e-e75e-5bee6801e7b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_trainKNN, y_trainKNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TbMkBVfCuYHX"
   },
   "outputs": [],
   "source": [
    "pred_KNN = knn.predict(X_testKNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "colab_type": "code",
    "id": "fmpXgP12ugOS",
    "outputId": "1a90abaa-79d1-4ed4-f6e3-be4b3238500d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      0.40      0.31         5\n",
      "           1       0.47      0.80      0.59        10\n",
      "           2       0.75      0.46      0.57        13\n",
      "           3       0.75      0.75      0.75        12\n",
      "           4       0.21      0.50      0.30         6\n",
      "           5       0.89      0.57      0.70        14\n",
      "           6       0.80      0.73      0.76        11\n",
      "           7       0.90      0.75      0.82        12\n",
      "           8       0.85      0.79      0.81        14\n",
      "           9       1.00      0.83      0.91        12\n",
      "          10       0.70      0.88      0.78         8\n",
      "          11       0.50      0.62      0.56         8\n",
      "          12       0.85      0.58      0.69        19\n",
      "          13       0.69      0.90      0.78        10\n",
      "          14       0.86      0.60      0.71        10\n",
      "\n",
      "    accuracy                           0.68       164\n",
      "   macro avg       0.70      0.68      0.67       164\n",
      "weighted avg       0.75      0.68      0.70       164\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_testKNN, pred_KNN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "Xr9EFcAKu9Zv",
    "outputId": "953c3f06-5d79-4637-c302-d8a485987323"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  3  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 2  8  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 3  4  6  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  0  1  9  1  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  1  1  3  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  0  1  4  8  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  1  2  0  8  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  2  0  1  9  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  2  0  1  0 11  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  1  0  0  0 10  1  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  1  0  7  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  1  0  0  0  5  2  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  1  0  2  4 11  1  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  9  1]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  1  0  3  6]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.confusion_matrix(y_testKNN, pred_KNN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "GltX0qE8vSoB",
    "outputId": "6d2d97a4-0507-4deb-e214-78a524bedf82"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fba76fca860>"
      ]
     },
     "execution_count": 83,
     "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": [
    "plt.scatter(y_testKNN, pred_KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "iOC_jkMdvd42",
    "outputId": "7130bc22-9fe8-4a0f-ba16-4d230677697b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5670731707317073\n"
     ]
    }
   ],
   "source": [
    "print(metrics.mean_absolute_error(y_testKNN, pred_KNN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kxW8p703vowA",
    "outputId": "1343c300-d988-481e-cc50-e53fe09a7ed4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1660858019721259\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(metrics.mean_squared_error(y_testKNN, pred_KNN)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "colab_type": "code",
    "id": "TIKFI5rKv0zY",
    "outputId": "d111aee8-5d83-4144-9efe-3e78e6493c1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Actual - Predicted using KNN')"
      ]
     },
     "execution_count": 86,
     "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": [
    "sns.distplot((y_testKNN-pred_KNN))\n",
    "plt.xlabel('Actual - Predicted using KNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "ztRMPfpnwDUw",
    "outputId": "d8f57e20-69f0-4845-a01c-5e9eb6a23fa9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5670731707317073\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "mini = 0;\n",
    "min_val = 2;\n",
    "\n",
    "for i in range(1,489):\n",
    "  knn = KNeighborsClassifier(n_neighbors=i)\n",
    "  knn.fit(X_trainKNN, y_trainKNN)\n",
    "  pred_i = knn.predict(X_testKNN)\n",
    "  if(min_val > metrics.mean_absolute_error(y_testKNN, pred_i)):\n",
    "    min_val = metrics.mean_absolute_error(y_testKNN, pred_i)\n",
    "    mini = i\n",
    "print(min_val)\n",
    "print(mini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ZAwuNNhhzamu",
    "outputId": "c82067e8-35fc-4fd9-9cff-5e9ed90399fb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qHorsdVh4L-I"
   },
   "source": [
    "#### K=1 has the highest accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IK9v0g2l-uBH"
   },
   "source": [
    "## Decision Trees Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oquaAdFF4ZgE"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ggzpwhN2--Vv"
   },
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "lJaVpF12_CF3",
    "outputId": "bb25ee9d-c470-4ffd-d8a9-6766833a072e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_trainKNN, y_trainKNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PpR2AcmWF5xq"
   },
   "outputs": [],
   "source": [
    "pred_dtree = dtree.predict(X_testKNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "colab_type": "code",
    "id": "DpT57_27GB9S",
    "outputId": "581125c1-9d96-4243-e572-f5252fc4b8b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.43      0.60      0.50         5\n",
      "           1       0.44      0.70      0.54        10\n",
      "           2       0.62      0.38      0.48        13\n",
      "           3       0.64      0.58      0.61        12\n",
      "           4       0.40      0.67      0.50         6\n",
      "           5       0.78      0.50      0.61        14\n",
      "           6       0.69      0.82      0.75        11\n",
      "           7       0.82      0.75      0.78        12\n",
      "           8       0.85      0.79      0.81        14\n",
      "           9       0.82      0.75      0.78        12\n",
      "          10       0.86      0.75      0.80         8\n",
      "          11       0.64      0.88      0.74         8\n",
      "          12       0.80      0.63      0.71        19\n",
      "          13       0.57      0.80      0.67        10\n",
      "          14       0.75      0.60      0.67        10\n",
      "\n",
      "    accuracy                           0.67       164\n",
      "   macro avg       0.67      0.68      0.66       164\n",
      "weighted avg       0.70      0.67      0.67       164\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_testKNN, pred_dtree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "vAl4oh4BGQb9",
    "outputId": "6ee79293-e138-4a5d-edae-2d1bc906484e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3  2  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 2  7  0  0  1  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  5  5  2  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  1  1  7  2  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  0  1  4  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  2  0  2  7  1  1  0  1  0  0  0  0  0]\n",
      " [ 0  0  0  1  0  1  9  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  1  0  1  9  1  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  2  0 11  1  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  1  0  0  0  9  1  1  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  1  0  0  6  1  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  7  1  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  1  0  0  2 12  3  1]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  1  8  1]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  1  3  6]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.confusion_matrix(y_testKNN, pred_dtree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "jJJdQmAfGa4-",
    "outputId": "919ba6b1-1ee0-4ea6-bdb7-38fbb2ccbe72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5304878048780488\n"
     ]
    }
   ],
   "source": [
    "print(metrics.mean_absolute_error(y_testKNN, pred_dtree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "2D8ru-zEGi4E",
    "outputId": "713498aa-bde0-4ea6-faae-8e7d21ef759d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fba72c97630>"
      ]
     },
     "execution_count": 127,
     "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": [
    "plt.scatter(y_testKNN, pred_dtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "BdzbToNVGtca",
    "outputId": "d8c7755b-e67a-49a0-a4a3-e461ae10acfc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0678220368764753\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(metrics.mean_squared_error(y_testKNN, pred_dtree)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zFRt0ic2K5NX"
   },
   "source": [
    "## Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xz6riJx0K_M6"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DBVujNiKNavi"
   },
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "Q5rCdpcKNwLT",
    "outputId": "2c571eea-39e5-4857-c32b-304abad1e63c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=30,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t4qCzVI1N12Q"
   },
   "outputs": [],
   "source": [
    "pred_rfc = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  3,  8,  4,  2,  8,  6, 12, 13,  0, 12,  7,  4,  7,  3,  9,  6,\n",
       "        8, 14,  8,  7,  5,  1, 12,  7,  8, 13, 14,  7,  8,  5,  4,  3, 11,\n",
       "       10,  2, 13, 11,  7, 12,  9,  2, 12,  1,  4,  5,  3,  5,  7, 10,  2,\n",
       "        3,  1, 12,  8,  3,  1,  3,  5,  2,  4,  7, 10, 10, 12,  5,  9,  1,\n",
       "        5,  4,  8, 13, 13,  1,  6,  7, 12, 13,  1, 14, 11, 12,  4,  7,  1,\n",
       "        9, 10, 12,  6,  6,  0, 10, 13, 10,  9, 14,  1,  1,  2, 11,  6,  1,\n",
       "        6,  8, 12,  1, 12,  9,  5,  9, 13, 14,  7, 10,  2, 10,  1,  8, 14,\n",
       "        5,  6, 13,  5,  4,  0, 13,  4,  2, 11,  5,  4,  1, 12,  9,  8, 13,\n",
       "        5,  3,  9,  0,  2, 12, 11,  2,  2,  1,  5, 10, 14, 14, 14, 13,  8,\n",
       "        0,  1,  8,  9,  0, 12,  5, 11,  1,  9, 14])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_rfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "colab_type": "code",
    "id": "jhHhyGAEOAu1",
    "outputId": "2ac9483a-1b7f-4dcf-c4ab-19d712a2c088"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.60      0.55         5\n",
      "           1       0.47      0.80      0.59        10\n",
      "           2       0.64      0.54      0.58        13\n",
      "           3       0.88      0.58      0.70        12\n",
      "           4       0.40      0.67      0.50         6\n",
      "           5       0.50      0.50      0.50        14\n",
      "           6       1.00      0.73      0.84        11\n",
      "           7       0.75      0.75      0.75        12\n",
      "           8       0.92      0.86      0.89        14\n",
      "           9       0.91      0.83      0.87        12\n",
      "          10       0.60      0.75      0.67         8\n",
      "          11       0.86      0.75      0.80         8\n",
      "          12       0.87      0.68      0.76        19\n",
      "          13       0.75      0.90      0.82        10\n",
      "          14       0.70      0.70      0.70        10\n",
      "\n",
      "    accuracy                           0.71       164\n",
      "   macro avg       0.72      0.71      0.70       164\n",
      "weighted avg       0.74      0.71      0.71       164\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, pred_rfc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "RPB8HVRXOOSe",
    "outputId": "e690832f-5e71-41e1-c857-ea664c50706f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3  2  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  8  1  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  5  7  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  1  2  7  1  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  0  1  4  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  1  0  3  7  0  2  0  1  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  3  8  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  1  2  0  9  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  1  1  0  0 12  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  1  0  0  0 10  1  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  1  0  0  6  1  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  1  6  1  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  1  0  2  0 13  1  2]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  9  1]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  1  2  7]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fba71e1d828>"
      ]
     },
     "execution_count": 130,
     "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": [
    "print(metrics.confusion_matrix(y_test, pred_rfc))\n",
    "plt.matshow(metrics.confusion_matrix(y_test, pred_rfc),fignum=0)\n",
    "plt.si"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "SVSeMugxObCD",
    "outputId": "e139cced-62a3-4224-b719-70679179b3af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5060975609756098\n",
      "1.0735171281308202\n"
     ]
    }
   ],
   "source": [
    "print(metrics.mean_absolute_error(y_testKNN, pred_rfc))\n",
    "print(np.sqrt(metrics.mean_squared_error(y_testKNN, pred_rfc)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "4aCiRwAXO-rL",
    "outputId": "d9f0c810-b42c-4a10-f380-c8737758bbd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fba7600bf98>"
      ]
     },
     "execution_count": 106,
     "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": [
    "plt.scatter(y_testKNN, pred_rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "j_g1efayUvqj",
    "outputId": "048da914-a634-45c5-e180-227c68b5be0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7073170731707317\n",
      "0.6707317073170732\n",
      "0.6829268292682927\n",
      "0.524390243902439\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(y_test, pred_rfc))\n",
    "print(metrics.accuracy_score(y_test, pred_dtree))\n",
    "print(metrics.accuracy_score(y_test, pred_KNN))\n",
    "print(metrics.accuracy_score(y_test, pred_LogReg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7073170731707317\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(y_test, pred_rfc,normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YgB2Boq3ccLV"
   },
   "source": [
    "## Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nitish/.local/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_svm = svm_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  0  0  0  4  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  4  0  0  6  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  4  0  8  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  1  5  5  0  1  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  6  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  7  6  0  1  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  6  0  5  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  3  0  0  9  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  5  0  0  0  9  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  1  1  0  0  0  9  1  0  0  0  0]\n",
      " [ 0  0  0  0  4  0  0  0  0  0  4  0  0  0  0]\n",
      " [ 0  0  0  0  3  0  0  0  0  0  0  5  0  0  0]\n",
      " [ 0  0  0  0 10  0  0  0  0  0  0  0  8  1  0]\n",
      " [ 0  0  0  0  4  0  0  0  0  0  0  0  0  6  0]\n",
      " [ 0  0  0  0  7  0  0  0  0  0  0  0  0  0  3]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.confusion_matrix(y_test, pred_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.20      0.33         5\n",
      "           1       0.80      0.40      0.53        10\n",
      "           2       0.80      0.31      0.44        13\n",
      "           3       1.00      0.42      0.59        12\n",
      "           4       0.08      1.00      0.14         6\n",
      "           5       0.86      0.43      0.57        14\n",
      "           6       0.83      0.45      0.59        11\n",
      "           7       0.90      0.75      0.82        12\n",
      "           8       1.00      0.64      0.78        14\n",
      "           9       1.00      0.75      0.86        12\n",
      "          10       0.80      0.50      0.62         8\n",
      "          11       1.00      0.62      0.77         8\n",
      "          12       1.00      0.42      0.59        19\n",
      "          13       0.86      0.60      0.71        10\n",
      "          14       1.00      0.30      0.46        10\n",
      "\n",
      "    accuracy                           0.51       164\n",
      "   macro avg       0.86      0.52      0.59       164\n",
      "weighted avg       0.89      0.51      0.61       164\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, pred_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1463414634146343\n",
      "3.749796742458996\n",
      "0.5121951219512195\n"
     ]
    }
   ],
   "source": [
    "print(metrics.mean_absolute_error(y_test, pred_svm))\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, pred_svm)))\n",
    "print(metrics.accuracy_score(y_test,pred_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_grid = {'C': [100,1000,10000],'gamma': [0.01,0.001,0.0001,0.00001],'kernel':['sigmoid','rbf']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid = GridSearchCV(SVC(),param_grid,verbose=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'grid' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-118-3949096c802a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'grid' is not defined"
     ]
    }
   ],
   "source": [
    "# grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'grid' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-119-28c2e4d7952c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'grid' is not defined"
     ]
    }
   ],
   "source": [
    "# grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred_grid = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(metrics.confusion_matrix(y_test, pred_grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(metrics.classification_report(y_test, pred_grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(metrics.mean_absolute_error(y_test, pred_grid))\n",
    "# print(np.sqrt(metrics.mean_squared_error(y_test, pred_grid)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pred_grid' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-121-6453bc33f916>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpred_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pred_grid' is not defined"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(y_test,pred_grid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving model into a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_pH','wb') as f:\n",
    "    pickle.dump(rfc,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_pH','rb') as f:\n",
    "    import_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.iloc[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.iloc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import_model.predict(X_test.iloc[[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = [[75,169,51]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.iloc[[0]].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import_model.predict(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
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   "execution_count": 126,
   "metadata": {},
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   ],
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    "pred_rfc"
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
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   "name": "pHDataProcessing.ipynb",
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   "version": "0.3.2"
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   "display_name": "Python 3",
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