04_decisiontree_b.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load sample data set \n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>job</th>\n",
       "      <th>degree</th>\n",
       "      <th>salary_more_then_100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  company                  job     degree  salary_more_then_100k\n",
       "0  google      sales executive  bachelors                      0\n",
       "1  google      sales executive    masters                      0\n",
       "2  google     business manager  bachelors                      1\n",
       "3  google     business manager    masters                      1\n",
       "4  google  computer programmer  bachelors                      0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset path\n",
    "file_path = 'data/salaries.csv'\n",
    "\n",
    "# reading our dataset as a dataframe\n",
    "df= pd.read_csv(file_path)\n",
    "\n",
    "# taking a first look on our data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv(\"salaries.csv\")\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('salary_more_then_100k',axis=1)\n",
    "y = df['salary_more_then_100k']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>degree</th>\n",
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       "      <th>0</th>\n",
       "      <td>google</td>\n",
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       "      <td>bachelors</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
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       "      <td>masters</td>\n",
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       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
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       "      <td>masters</td>\n",
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       "    <tr>\n",
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       "      <td>bachelors</td>\n",
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      "text/plain": [
       "  company                  job     degree\n",
       "0  google      sales executive  bachelors\n",
       "1  google      sales executive    masters\n",
       "2  google     business manager  bachelors\n",
       "3  google     business manager    masters\n",
       "4  google  computer programmer  bachelors"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: salary_more_then_100k, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company                  job     degree  company_n  job_n  degree_n\n",
       "0       google      sales executive  bachelors          2      2         0\n",
       "1       google      sales executive    masters          2      2         1\n",
       "2       google     business manager  bachelors          2      0         0\n",
       "3       google     business manager    masters          2      0         1\n",
       "4       google  computer programmer  bachelors          2      1         0\n",
       "5       google  computer programmer    masters          2      1         1\n",
       "6   abc pharma      sales executive    masters          0      2         1\n",
       "7   abc pharma  computer programmer  bachelors          0      1         0\n",
       "8   abc pharma     business manager  bachelors          0      0         0\n",
       "9   abc pharma     business manager    masters          0      0         1\n",
       "10    facebook      sales executive  bachelors          1      2         0\n",
       "11    facebook      sales executive    masters          1      2         1\n",
       "12    facebook     business manager  bachelors          1      0         0\n",
       "13    facebook     business manager    masters          1      0         1\n",
       "14    facebook  computer programmer  bachelors          1      1         0\n",
       "15    facebook  computer programmer    masters          1      1         1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting catagories into dummies\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "X['company_n'] = LabelEncoder().fit_transform(X['company'])\n",
    "X['job_n'] = LabelEncoder().fit_transform(X['job'])\n",
    "X['degree_n'] = LabelEncoder().fit_transform(X['degree'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['company', 'job', 'degree', 'company_n', 'job_n', 'degree_n'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    company_n  job_n  degree_n\n",
       "0           2      2         0\n",
       "1           2      2         1\n",
       "2           2      0         0\n",
       "3           2      0         1\n",
       "4           2      1         0\n",
       "5           2      1         1\n",
       "6           0      2         1\n",
       "7           0      1         0\n",
       "8           0      0         0\n",
       "9           0      0         1\n",
       "10          1      2         0\n",
       "11          1      2         1\n",
       "12          1      0         0\n",
       "13          1      0         1\n",
       "14          1      1         0\n",
       "15          1      1         1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= X.drop(['company', 'job', 'degree'],axis=\"columns\")\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   company_n  job_n  degree_n\n",
       "0          2      2         0\n",
       "1          2      2         1\n",
       "2          2      0         0\n",
       "3          2      0         1\n",
       "4          2      1         0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#machine learning algorithm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#creat and fit our model\n",
    "model= DecisionTreeClassifier().fit(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Is salary of Google, Computer Engineer, Bachelors degree > 100 k ?\n",
    "\n",
    "model.predict([[2,2,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Is salary of Google, Computer Engineer, Masters degree > 100 k ?\n",
    "\n",
    "model.predict([[2,1,1]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy of model\n",
    "#split data into test and train (80/20)%rule\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "X_train, X_test, y_train, y_test =train_test_split(X,y,test_size=0.2, random_state=1)\n",
    "\n",
    "#creat a model\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "predicted_values =model.predict(X_test)\n",
    "predicted_values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy of the model\n",
    "score = accuracy_score(y_test, predicted_values)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use(\"Solarize_Light2\")\n",
    "model = DecisionTreeClassifier().fit(X,y)\n",
    "plot_tree(model, filled=True)\n",
    "plt.title(\"decision tree trained model of Salaries data\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "db9da9d256f457015986a8acd5c7819a5914eb9a575ecff51b37f60a7d912a81"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}