naive-bayes-classifier-for-play-tennis-dataset.ipynb

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     "text": [
      "/kaggle/input/play-tennis/play_tennis.csv\n"
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "df = pd.read_csv(\"/kaggle/input/play-tennis/play_tennis.csv\")"
   ]
  },
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       "    day   outlook  temp humidity    wind play\n",
       "0    D1     Sunny   Hot     High    Weak   No\n",
       "1    D2     Sunny   Hot     High  Strong   No\n",
       "2    D3  Overcast   Hot     High    Weak  Yes\n",
       "3    D4      Rain  Mild     High    Weak  Yes\n",
       "4    D5      Rain  Cool   Normal    Weak  Yes\n",
       "5    D6      Rain  Cool   Normal  Strong   No\n",
       "6    D7  Overcast  Cool   Normal  Strong  Yes\n",
       "7    D8     Sunny  Mild     High    Weak   No\n",
       "8    D9     Sunny  Cool   Normal    Weak  Yes\n",
       "9   D10      Rain  Mild   Normal    Weak  Yes\n",
       "10  D11     Sunny  Mild   Normal  Strong  Yes\n",
       "11  D12  Overcast  Mild     High  Strong  Yes\n",
       "12  D13  Overcast   Hot   Normal    Weak  Yes\n",
       "13  D14      Rain  Mild     High  Strong   No"
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    {
     "data": {
      "text/plain": [
       "play\n",
       "Yes    9\n",
       "No     5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
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   "source": [
    "df['play'].value_counts()"
   ]
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   "source": [
    "caluclating all the probabilities required to get the required result for the given features by the naive bayes algorithm"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "0.6428571428571429\n",
      "0.35714285714285715\n"
     ]
    }
   ],
   "source": [
    "Pyes = 9/14\n",
    "Pno =5/14\n",
    "print(Pyes)\n",
    "print(Pno)"
   ]
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       "play      No  Yes\n",
       "outlook          \n",
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    "Pon =0\n",
    "Prn=2/5\n",
    "Psn=3/5\n",
    "\n",
    "Poy=4/9\n",
    "Pry=3/9\n",
    "Psy=2/9\n"
   ]
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    "Pcn=1/5\n",
    "Phn=2/5\n",
    "Pmn=2/5\n",
    "\n",
    "Pcy=3/9\n",
    "Phy=2/9\n",
    "Pmy=4/9"
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       "      <th>play</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "play      No  Yes\n",
       "humidity         \n",
       "High       4    3\n",
       "Normal     1    6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['humidity'],df['play'])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "id": "a13e3517",
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   "source": [
    "Pnh=4/5\n",
    "Pnn=1/5\n",
    "\n",
    "Pyh=3/9\n",
    "Pyn=6/9"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
   "id": "0fbc6bf4",
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    {
     "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>play</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Strong</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weak</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "play    No  Yes\n",
       "wind           \n",
       "Strong   3    3\n",
       "Weak     2    6"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "pd.crosstab(df['wind'],df['play'])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
   "id": "7f79351a",
   "metadata": {
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   "source": [
    "Pns=3/5\n",
    "Pnw=2/5\n",
    "Pys=3/9\n",
    "Pyw=6/9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d4586806",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# Problem:\n",
    "\n",
    "# outlook=Sunny ,Temp=Hot ,Humididty=High ,Wind=Weak\n",
    "# play or not??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "672ff23f",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# Solution \n",
    "\n",
    "# p(Yes | Suuny,Hot,High, Weak) = p(Sunny|Yes) * p(Hot|Yes) * p(High|Yes) * p(Weak|Yes) * p(Yes)\n",
    "\n",
    "# p(No | Suuny,Hot,High, Weak) = p(Sunny|No) * p(Hot|No) * p(High|No) * p(Weak|No) * p(No)\n",
    "\n",
    "# compare and decide using the maximum a posteriori rule above "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4c1a72d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-18T18:23:35.127887Z",
     "iopub.status.busy": "2024-01-18T18:23:35.127065Z",
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     "shell.execute_reply": "2024-01-18T18:23:35.133018Z"
    },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.007054673721340387\n",
      "0.02742857142857143\n",
      "ans is 0.02742857142857143\n"
     ]
    }
   ],
   "source": [
    "PY = Psy*Phy*Pyh*Pyw*Pyes\n",
    "PN = Psn*Phn*Pnh*Pnw*Pno\n",
    "print(PY)\n",
    "print(PN)\n",
    "print('ans is',max(PN,PY))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b59ed21b",
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   "source": [
    "so based on posteriori rule the ans will be NO. as PN>PY"
   ]
  }
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