final_proj_version_0.2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1cb3db40",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "from sklearn.model_selection import train_test_split,StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import LeaveOneOut\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier,plot_tree\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a0f6e7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"milknew.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7312556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>pH</th>\n",
       "      <th>Temprature</th>\n",
       "      <th>Taste</th>\n",
       "      <th>Odor</th>\n",
       "      <th>Fat</th>\n",
       "      <th>Turbidity</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.6</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.6</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.5</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>255</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.6</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>255</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pH  Temprature  Taste  Odor  Fat   Turbidity  Colour   Grade\n",
       "0  6.6          35      1     0     1          0     254    high\n",
       "1  6.6          36      0     1     0          1     253    high\n",
       "2  8.5          70      1     1     1          1     246     low\n",
       "3  9.5          34      1     1     0          1     255     low\n",
       "4  6.6          37      0     0     0          0     255  medium"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5fcdd69c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Fat ': 'Fat'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc5acf9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1059 entries, 0 to 1058\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   pH          1059 non-null   float64\n",
      " 1   Temprature  1059 non-null   int64  \n",
      " 2   Taste       1059 non-null   int64  \n",
      " 3   Odor        1059 non-null   int64  \n",
      " 4   Fat         1059 non-null   int64  \n",
      " 5   Turbidity   1059 non-null   int64  \n",
      " 6   Colour      1059 non-null   int64  \n",
      " 7   Grade       1059 non-null   object \n",
      "dtypes: float64(1), int64(6), object(1)\n",
      "memory usage: 66.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "696a6f42",
   "metadata": {},
   "source": [
    "# Check Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed57f4bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Null Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Column Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pH</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temprature</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taste</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odor</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fat</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turbidity</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colour</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grade</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Null Values\n",
       "Column Name             \n",
       "pH                     0\n",
       "Temprature             0\n",
       "Taste                  0\n",
       "Odor                   0\n",
       "Fat                    0\n",
       "Turbidity              0\n",
       "Colour                 0\n",
       "Grade                  0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.isnull().sum(), columns=[\"Null Values\"]).rename_axis(\"Column Name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "660c5568",
   "metadata": {},
   "source": [
    "# Check Value Distribution in Each Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5dde5ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_plot = [col for col in df.columns if col != 'Grade']\n",
    "\n",
    "# Create a list to store individual charts\n",
    "charts = []\n",
    "\n",
    "for col in columns_to_plot:\n",
    "    chart = alt.Chart(df).mark_bar().encode(\n",
    "        x=alt.X(col + ':N', title=col),\n",
    "        y='count()',\n",
    "        color='Grade:N'\n",
    "    )\n",
    "    charts.append(chart)\n",
    "\n",
    "# Concatenate the individual charts into a single chart\n",
    "final_chart = alt.hconcat(*charts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "720e0f17",
   "metadata": {},
   "outputs": [
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{\"data-b2ef25a62d413681a7a703f84ca89926\": [{\"pH\": 6.6, \"Temprature\": 35, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 254, \"Grade\": \"high\"}, {\"pH\": 6.6, \"Temprature\": 36, \"Taste\": 0, \"Odor\": 1, \"Fat\": 0, \"Turbidity\": 1, \"Colour\": 253, \"Grade\": \"high\"}, {\"pH\": 8.5, \"Temprature\": 70, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 246, \"Grade\": \"low\"}, {\"pH\": 9.5, \"Temprature\": 34, \"Taste\": 1, \"Odor\": 1, \"Fat\": 0, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.6, \"Temprature\": 37, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 6.6, \"Temprature\": 37, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 5.5, \"Temprature\": 45, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"low\"}, {\"pH\": 4.5, \"Temprature\": 60, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"low\"}, {\"pH\": 8.1, \"Temprature\": 66, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.7, \"Temprature\": 45, \"Taste\": 1, \"Odor\": 1, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 247, \"Grade\": \"medium\"}, {\"pH\": 6.7, \"Temprature\": 45, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 245, \"Grade\": \"medium\"}, {\"pH\": 5.6, \"Temprature\": 50, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 8.6, \"Temprature\": 55, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 7.4, \"Temprature\": 90, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 45, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 6.5, \"Temprature\": 38, \"Taste\": 1, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 4.7, \"Temprature\": 38, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 3.0, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 9.0, \"Temprature\": 43, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 245, \"Grade\": \"medium\"}, {\"pH\": 6.6, \"Temprature\": 45, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"high\"}, {\"pH\": 6.5, \"Temprature\": 36, \"Taste\": 0, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 4.5, \"Temprature\": 38, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 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1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 6.6, \"Temprature\": 38, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 6.7, \"Temprature\": 38, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 3.0, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.6, \"Temprature\": 43, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"medium\"}, {\"pH\": 8.6, \"Temprature\": 55, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 45, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 6.8, \"Temprature\": 45, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 6.5, \"Temprature\": 38, \"Taste\": 1, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 4.7, \"Temprature\": 38, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 3.0, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 9.0, \"Temprature\": 43, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 245, \"Grade\": \"medium\"}, {\"pH\": 6.5, \"Temprature\": 37, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 3.0, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 9.0, \"Temprature\": 43, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 245, \"Grade\": \"medium\"}, {\"pH\": 6.6, \"Temprature\": 45, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"high\"}, {\"pH\": 6.5, \"Temprature\": 36, \"Taste\": 0, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 6.8, \"Temprature\": 45, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 6.5, \"Temprature\": 38, \"Taste\": 1, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 4.7, \"Temprature\": 38, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 3.0, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 9.0, \"Temprature\": 43, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 245, \"Grade\": \"medium\"}, {\"pH\": 6.6, \"Temprature\": 45, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"high\"}, {\"pH\": 6.5, \"Temprature\": 36, \"Taste\": 0, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 4.5, \"Temprature\": 38, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 45, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 245, \"Grade\": \"high\"}, {\"pH\": 6.5, \"Temprature\": 35, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 246, \"Grade\": \"medium\"}, {\"pH\": 6.8, \"Temprature\": 36, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 253, \"Grade\": \"high\"}, {\"pH\": 8.5, \"Temprature\": 70, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 246, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 34, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 1, \"Colour\": 240, \"Grade\": \"medium\"}, {\"pH\": 6.5, \"Temprature\": 37, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 245, \"Grade\": \"low\"}, {\"pH\": 6.6, \"Temprature\": 37, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 6.8, \"Temprature\": 45, \"Taste\": 0, \"Odor\": 1, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 240, \"Grade\": \"medium\"}, {\"pH\": 7.4, \"Temprature\": 65, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 41, \"Taste\": 0, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 6.5, \"Temprature\": 38, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 6.6, \"Temprature\": 38, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 3.0, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 9.0, \"Temprature\": 43, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 248, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 6.6, \"Temprature\": 50, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"low\"}, {\"pH\": 6.5, \"Temprature\": 36, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 247, \"Grade\": \"medium\"}, {\"pH\": 6.6, \"Temprature\": 50, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 45, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 245, \"Grade\": \"high\"}, {\"pH\": 6.6, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 9.0, \"Temprature\": 43, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 248, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 6.6, \"Temprature\": 50, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"low\"}, {\"pH\": 6.5, \"Temprature\": 36, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 247, \"Grade\": \"medium\"}, {\"pH\": 6.6, \"Temprature\": 38, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 6.8, \"Temprature\": 45, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 245, \"Grade\": \"high\"}, {\"pH\": 9.5, \"Temprature\": 34, \"Taste\": 1, \"Odor\": 1, \"Fat\": 0, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.5, \"Temprature\": 37, \"Taste\": 0, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"medium\"}, {\"pH\": 6.6, \"Temprature\": 37, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 5.5, \"Temprature\": 45, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 250, \"Grade\": \"low\"}, {\"pH\": 6.5, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 0, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 250, \"Grade\": \"medium\"}, {\"pH\": 8.1, \"Temprature\": 66, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.7, \"Temprature\": 45, \"Taste\": 1, \"Odor\": 1, \"Fat\": 0, \"Turbidity\": 0, \"Colour\": 247, \"Grade\": \"medium\"}, {\"pH\": 6.7, \"Temprature\": 38, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 255, \"Grade\": \"high\"}, {\"pH\": 3.0, \"Temprature\": 40, \"Taste\": 1, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}, {\"pH\": 6.8, \"Temprature\": 43, \"Taste\": 1, \"Odor\": 0, \"Fat\": 1, \"Turbidity\": 0, \"Colour\": 250, \"Grade\": \"high\"}, {\"pH\": 8.6, \"Temprature\": 55, \"Taste\": 0, \"Odor\": 1, \"Fat\": 1, \"Turbidity\": 1, \"Colour\": 255, \"Grade\": \"low\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2bc35e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df[columns_to_plot].corr()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\", linewidths=.5)\n",
    "\n",
    "plt.title(\"Variable Correlation Heatmap (excluding 'Grade')\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2df2f294",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['pH','Temprature','Taste','Odor','Fat','Turbidity','Colour']]\n",
    "y = df['Grade'] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edd9d9fa",
   "metadata": {},
   "source": [
    "# Parameters Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df6d7081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02153136, -0.91410734,  0.91050291, ...,  0.69960809,\n",
       "        -0.98221664,  0.5016002 ],\n",
       "       [-0.02153136, -0.81503461, -1.09829413, ..., -1.4293717 ,\n",
       "         1.01810533,  0.26933321],\n",
       "       [ 1.33656414,  2.55343832,  0.91050291, ...,  0.69960809,\n",
       "         1.01810533, -1.35653574],\n",
       "       ...,\n",
       "       [-2.59476494, -0.41874368,  0.91050291, ...,  0.69960809,\n",
       "         1.01810533,  0.73386719],\n",
       "       [ 0.12142606, -0.12152548,  0.91050291, ...,  0.69960809,\n",
       "        -0.98221664, -0.42746777],\n",
       "       [ 1.40804285,  1.06734732, -1.09829413, ...,  0.69960809,\n",
       "         1.01810533,  0.73386719]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f433fb0",
   "metadata": {},
   "source": [
    "# Applying K-fold to Training and Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76556b1",
   "metadata": {},
   "source": [
    "## Decision-Tree & Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "563afe7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dt = DecisionTreeClassifier(random_state=42)\n",
    "model_lda = LinearDiscriminantAnalysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "483f30a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9952830188679245, 1.0, 0.9952830188679245, 0.9952830188679245, 0.990521327014218]\n",
      "[0.7216981132075472, 0.7877358490566038, 0.7594339622641509, 0.7405660377358491, 0.7298578199052133]\n"
     ]
    }
   ],
   "source": [
    "k = 5\n",
    "skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=42)\n",
    "\n",
    "dt_fold_accuracies = []\n",
    "lda_fold_accuracies = []\n",
    "\n",
    "for train_index, test_index in skf.split(X, y):\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    \n",
    "    model_dt.fit(X_train, y_train)\n",
    "    dt_y_pred = model_dt.predict(X_test)\n",
    "    dt_accuracy = accuracy_score(y_test, dt_y_pred)\n",
    "    dt_fold_accuracies.append(dt_accuracy)\n",
    "    \n",
    "    model_lda.fit(X_train, y_train)\n",
    "    lda_y_pred = model_lda.predict(X_test)\n",
    "    lda_accuracy = accuracy_score(y_test, lda_y_pred)\n",
    "    lda_fold_accuracies.append(lda_accuracy)\n",
    "\n",
    "print(dt_fold_accuracies)\n",
    "print(lda_fold_accuracies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e2c695d",
   "metadata": {},
   "source": [
    "# Visualization(part1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8b839608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(np.arange(1, k + 1), dt_fold_accuracies, color='green', alpha=0.7)\n",
    "plt.xlabel('Fold Number')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Stratified K-Fold Cross-Validation Accuracies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "14620234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(1, k + 1), lda_fold_accuracies, color='green', alpha=0.7)\n",
    "plt.xlabel('Fold Number')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Linear Discriminant Analysis Cross-Validation Accuracies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b54818",
   "metadata": {},
   "source": [
    "## Logistic Regression & Random Forest Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ef8c37ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lr = LogisticRegression(random_state=42)\n",
    "model_rf = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a41333",
   "metadata": {},
   "source": [
    "### do it with k-fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cf84c701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8537735849056604, 0.8679245283018868, 0.8726415094339622, 0.839622641509434, 0.8246445497630331]\n",
      "[0.9952830188679245, 1.0, 0.9952830188679245, 1.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "k = 5\n",
    "skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=42)\n",
    "\n",
    "# Lists to store accuracies of each fold\n",
    "lr_fold_accuracies = []\n",
    "rf_fold_accuracies = []\n",
    "\n",
    "# Cross-validation loop\n",
    "for train_index, test_index in skf.split(X, y):\n",
    "    # Splitting the data\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    \n",
    "    # Training and evaluating Logistic Regression\n",
    "    model_lr.fit(X_train, y_train)\n",
    "    lr_y_pred = model_lr.predict(X_test)\n",
    "    lr_accuracy = accuracy_score(y_test, lr_y_pred)\n",
    "    lr_fold_accuracies.append(lr_accuracy)\n",
    "    \n",
    "    # Training and evaluating Random Forest\n",
    "    model_rf.fit(X_train, y_train)\n",
    "    rf_y_pred = model_rf.predict(X_test)\n",
    "    rf_accuracy = accuracy_score(y_test, rf_y_pred)\n",
    "    rf_fold_accuracies.append(rf_accuracy)\n",
    "\n",
    "# Printing the results\n",
    "print(lr_fold_accuracies)\n",
    "print(rf_fold_accuracies)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e100af19",
   "metadata": {},
   "source": [
    "## Tunning the parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "562af957",
   "metadata": {},
   "source": [
    "Define the parameter grid to search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f2e48252",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb93206f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "Best Score: 0.9981132075471699\n"
     ]
    }
   ],
   "source": [
    "model_rf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "\n",
    "k = 5\n",
    "skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=42)\n",
    "\n",
    "grid_search = GridSearchCV(estimator=model_rf, param_grid=param_grid, cv=skf, scoring='accuracy')\n",
    "\n",
    "grid_search.fit(X, y)\n",
    "\n",
    "best_params = grid_search.best_params_\n",
    "best_score = grid_search.best_score_\n",
    "\n",
    "print(f\"Best Parameters: {best_params}\")\n",
    "print(f\"Best Score: {best_score}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b1f200",
   "metadata": {},
   "source": [
    "# Visualization(part2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c0d59e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(np.arange(1, k + 1), rf_fold_accuracies, color='green', alpha=0.7)\n",
    "plt.xlabel('Fold Number')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Random Forest K-Fold Cross-Validation Accuracies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4276540e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(1, k + 1), lr_fold_accuracies, color='green', alpha=0.7)\n",
    "plt.xlabel('Fold Number')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Logistic Regression Analysis Cross-Validation Accuracies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a490bd34",
   "metadata": {},
   "source": [
    "### create tunning result demostration graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "67fe4bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = len(rf_fold_accuracies)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.bar(range(1, k+1), rf_fold_accuracies, color='blue', alpha=0.6, label='K-Fold Accuracies')\n",
    "\n",
    "plt.axhline(y=best_score, color='r', linestyle='--', label=f'Tunned: {best_score}')\n",
    "\n",
    "plt.ylim(0.95, 1)\n",
    "plt.xlabel('Fold Number')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Comparison of K-Fold Accuracies with Tunned Score')\n",
    "plt.xticks(range(1, k+1))  \n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8814ddc",
   "metadata": {},
   "source": [
    "### do it with LOOCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "37032cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lr_1 = LogisticRegression(random_state=42)\n",
    "model_rf_1 = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0ec41c95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression LOOCV Accuracy: 0.8508026440037771\n",
      "Random Forest LOOCV Accuracy: 0.9981114258734656\n"
     ]
    }
   ],
   "source": [
    "loo = LeaveOneOut()\n",
    "\n",
    "\n",
    "lr_accuracies_1 = []\n",
    "rf_accuracies_1 = []\n",
    "\n",
    "\n",
    "for train_index, test_index in loo.split(X):\n",
    "\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    \n",
    "\n",
    "    model_lr_1.fit(X_train, y_train)\n",
    "    lr_y_pred = model_lr_1.predict(X_test)\n",
    "    lr_accuracy = accuracy_score(y_test, lr_y_pred)\n",
    "    lr_accuracies_1.append(lr_accuracy)\n",
    "    \n",
    "\n",
    "    model_rf_1.fit(X_train, y_train)\n",
    "    rf_y_pred = model_rf_1.predict(X_test)\n",
    "    rf_accuracy = accuracy_score(y_test, rf_y_pred)\n",
    "    rf_accuracies_1.append(rf_accuracy)\n",
    "\n",
    "lr_overall_accuracy = sum(lr_accuracies_1) / len(lr_accuracies_1)\n",
    "rf_overall_accuracy = sum(rf_accuracies_1) / len(rf_accuracies_1)\n",
    "\n",
    "print(f\"Logistic Regression LOOCV Accuracy: {lr_overall_accuracy}\")\n",
    "print(f\"Random Forest LOOCV Accuracy: {rf_overall_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4406a5f9",
   "metadata": {},
   "source": [
    "Since LOOCV is more time consuming in terms of computation than K-fold in nature(LOOCV computes N times for training, we will not do tuning with it.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abdb2e05",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc89484f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}