DecisionTreeClassifier2.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "7eef683a",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier\n",
    "\n",
    "#### Sajjad Ranjbar Yazdi\n",
    "##### 9812223294"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0448bae4",
   "metadata": {},
   "source": [
    "#### Call required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3e37011",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35a47b9",
   "metadata": {},
   "source": [
    "#### Load the golf dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75d8bc7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('playgolf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e2c50d1f",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outlook</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Windy</th>\n",
       "      <th>Play Golf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlook  Temp Humidity  Windy Play Golf\n",
       "0     Rainy   Hot     High  False        No\n",
       "1     Rainy   Hot     High   True        No\n",
       "2  Overcast   Hot     High  False       Yes\n",
       "3     Sunny  Mild     High  False       Yes\n",
       "4     Sunny  Cool   Normal  False       Yes"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4adcfef4",
   "metadata": {},
   "source": [
    "#### Convert 'Outlook' column into one-hot encoded columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7bd6cc76",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlook_onehot = pd.get_dummies(data['Outlook'], prefix='Outlook')\n",
    "data = pd.concat([data, outlook_onehot], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b54e0c0e",
   "metadata": {},
   "source": [
    "#### Convert 'Temperature', 'Humidity', and 'Wind' columns using Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "27bc6cdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "data['Temp'] = le.fit_transform(data['Temp'])\n",
    "data['Humidity'] = le.fit_transform(data['Humidity'])\n",
    "data['Windy'] = le.fit_transform(data['Windy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1efe9174",
   "metadata": {},
   "source": [
    "#### Print the updated dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "198a4cb4",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Outlook</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Windy</th>\n",
       "      <th>Play Golf</th>\n",
       "      <th>Outlook_Overcast</th>\n",
       "      <th>Outlook_Rainy</th>\n",
       "      <th>Outlook_Sunny</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlook  Temp  Humidity  Windy Play Golf  Outlook_Overcast  Outlook_Rainy  \\\n",
       "0     Rainy     1         0      0        No                 0              1   \n",
       "1     Rainy     1         0      1        No                 0              1   \n",
       "2  Overcast     1         0      0       Yes                 1              0   \n",
       "3     Sunny     2         0      0       Yes                 0              0   \n",
       "4     Sunny     0         1      0       Yes                 0              0   \n",
       "\n",
       "   Outlook_Sunny  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              1  \n",
       "4              1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6875bc5",
   "metadata": {},
   "source": [
    "#### Separate features and target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8d1a6ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['Outlook', 'Play Golf'], axis=1)\n",
    "y = data['Play Golf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ff6bcd2",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Windy</th>\n",
       "      <th>Outlook_Overcast</th>\n",
       "      <th>Outlook_Rainy</th>\n",
       "      <th>Outlook_Sunny</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   Temp  Humidity  Windy  Outlook_Overcast  Outlook_Rainy  Outlook_Sunny\n",
       "0     1         0      0                 0              1              0\n",
       "1     1         0      1                 0              1              0\n",
       "2     1         0      0                 1              0              0\n",
       "3     2         0      0                 0              0              1\n",
       "4     0         1      0                 0              0              1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "afd679ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     No\n",
       "1     No\n",
       "2    Yes\n",
       "3    Yes\n",
       "4    Yes\n",
       "Name: Play Golf, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861bb5b7",
   "metadata": {},
   "source": [
    "#### Create a decision tree classifier object with max_depth=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a6d33582",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a22ccf",
   "metadata": {},
   "source": [
    "#### Fit the classifier to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9537a399",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b3ce935",
   "metadata": {},
   "source": [
    "#### Predict labels for training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4075d64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = clf.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1321e0bb",
   "metadata": {},
   "source": [
    "#### Calculate accuracy on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9badc47f",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_train = accuracy_score(y, y_pred_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95b3b1f",
   "metadata": {},
   "source": [
    "#### Print predicted labels for today and tomorrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01ee6713",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label for 'today': ['Yes']\n",
      "Predicted label for 'tomorrow': ['No']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sajjad/jupyter/environment/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "/home/sajjad/jupyter/environment/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted label for 'today':\", clf.predict([[0,1,0,1,0,0]]))\n",
    "print(\"Predicted label for 'tomorrow':\", clf.predict([[1,0,1,0,0,0]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27558e72",
   "metadata": {},
   "source": [
    "#### Print accuracy score on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cbd4095c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: %85.71428571428571\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy on training set: %\", accuracy_train*100, sep=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dad6f48",
   "metadata": {},
   "source": [
    "you can see this project on my github account\n",
    "- https://github.com/Data-analysis-FUM/DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eacf7b9c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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