Play Tennis  Implementation Using Sklearn Decision Tree Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Outlook</th>\n",
       "      <th>Temprature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Play_Tennis</th>\n",
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       "      <td>D1</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>Weak</td>\n",
       "      <td>No</td>\n",
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       "      <td>D2</td>\n",
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       "      <td>High</td>\n",
       "      <td>Strong</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D3</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D4</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D5</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>5</th>\n",
       "      <td>D6</td>\n",
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       "      <td>Normal</td>\n",
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       "      <td>No</td>\n",
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       "      <th>6</th>\n",
       "      <td>D7</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Strong</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>D8</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>Weak</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>D9</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D10</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Mild</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>D11</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Mild</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Strong</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>D12</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>Strong</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>D13</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>Hot</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>D14</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>Strong</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Day   Outlook Temprature Humidity    Wind Play_Tennis\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"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"Play Tennis.csv\")\n",
    "value=['Outlook','Temprature','Humidity','Wind']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)           #Dataset Lenght"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 6)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape  #To see the number of rows and columns in our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>Outlook</th>\n",
       "      <th>Temprature</th>\n",
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       "      <th>Play_Tennis</th>\n",
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       "      <td>Yes</td>\n",
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      "text/plain": [
       "  Day   Outlook Temprature Humidity    Wind Play_Tennis\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"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()         #To inspect the first five records of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Day</th>\n",
       "      <th>Outlook</th>\n",
       "      <th>Temprature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Play_Tennis</th>\n",
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       "      <th>9</th>\n",
       "      <td>D10</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Mild</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>D11</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Mild</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Strong</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Hot</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>13</th>\n",
       "      <td>D14</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Mild</td>\n",
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       "      <td>No</td>\n",
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      "text/plain": [
       "    Day   Outlook Temprature Humidity    Wind Play_Tennis\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"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()         #To inspect the last five records of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Day</th>\n",
       "      <th>Outlook</th>\n",
       "      <th>Temprature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Play_Tennis</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14</td>\n",
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       "      <td>14</td>\n",
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      "text/plain": [
       "       Day Outlook Temprature Humidity  Wind Play_Tennis\n",
       "count   14      14         14       14    14          14\n",
       "unique  14       3          3        2     2           2\n",
       "top     D8    Rain       Mild   Normal  Weak         Yes\n",
       "freq     1       5          6        7     8           9"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()     #To see statistical details of the dataset:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing the Data (Data Slicing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Day  Outlook  Temprature  Humidity  Wind  Play_Tennis\n",
       "0     0        2           1         0     1            0\n",
       "1     6        2           1         0     0            0\n",
       "2     7        0           1         0     1            1\n",
       "3     8        1           2         0     1            1\n",
       "4     9        1           0         1     1            1\n",
       "5    10        1           0         1     0            0\n",
       "6    11        0           0         1     0            1\n",
       "7    12        2           2         0     1            0\n",
       "8    13        2           0         1     1            1\n",
       "9     1        1           2         1     1            1\n",
       "10    2        2           2         1     0            1\n",
       "11    3        0           2         0     0            1\n",
       "12    4        0           1         1     1            1\n",
       "13    5        1           2         0     0            0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#machine learning algorithms can only learn from numbers (int, float, doubles .. )\n",
    "#so let us encode it to int\n",
    "from sklearn import preprocessing\n",
    "string_to_int= preprocessing.LabelEncoder()                     #encode your data\n",
    "df=df.apply(string_to_int.fit_transform) #fit and transform it\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To divide our data into attribute set and Label:\n",
    "feature_cols = ['Outlook','Temprature','Humidity','Wind']\n",
    "X = df[feature_cols ]                               #contains the attribute \n",
    "y = df.Play_Tennis                                  #contains the label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To divide our data into training and test sets:\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', 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, random_state=100,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# perform training \n",
    "from sklearn.tree import DecisionTreeClassifier                             # import the classifier\n",
    "classifier =DecisionTreeClassifier(criterion=\"entropy\", random_state=100)     # create a classifier object\n",
    "classifier.fit(X_train, y_train)                                              # fit the classifier with X and Y data or "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict the response for test dataset\n",
    "y_pred= classifier.predict(X_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.4\n"
     ]
    }
   ],
   "source": [
    "# Model Accuracy, how often is the classifier correct?\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now let's compare some of our predicted values with the actual values and see how accurate we were:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "0        0          1\n",
       "8        1          1\n",
       "7        0          1\n",
       "11       1          0\n",
       "3        1          1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_p=pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})  \n",
    "data_p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating the Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 2]\n",
      " [1 2]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00         2\n",
      "          1       0.50      0.67      0.57         3\n",
      "\n",
      "avg / total       0.30      0.40      0.34         5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix  \n",
    "print(confusion_matrix(y_test, y_pred))  \n",
    "print(classification_report(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.externals.six import StringIO\n",
    "from IPython.display import Image\n",
    "import pydotplus\n",
    "dot_data = StringIO()\n",
    "export_graphviz(classifier, out_file=dot_data,\n",
    "filled=True, rounded=True,\n",
    "special_characters=True,feature_names =value,class_names=['0','1'])\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "graph.write_png('Play Tennis.png')\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}