code3eliminaterpofessiontoo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Import Python libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(r\"TrainingData.csv\")\n",
    "test_data = pd.read_csv(r\"TestData.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check how train dataset looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>married</th>\n",
       "      <th>house_ownership</th>\n",
       "      <th>car_ownership</th>\n",
       "      <th>profession</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>current_job_years</th>\n",
       "      <th>current_house_years</th>\n",
       "      <th>risk_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1303835</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Mechanical_engineer</td>\n",
       "      <td>Rewa</td>\n",
       "      <td>Madhya_Pradesh</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7574516</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Software_Developer</td>\n",
       "      <td>Parbhani</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3991815</td>\n",
       "      <td>66</td>\n",
       "      <td>4</td>\n",
       "      <td>married</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Technical_writer</td>\n",
       "      <td>Alappuzha</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6256451</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>yes</td>\n",
       "      <td>Software_Developer</td>\n",
       "      <td>Bhubaneswar</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5768871</td>\n",
       "      <td>47</td>\n",
       "      <td>11</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Civil_servant</td>\n",
       "      <td>Tiruchirappalli[10]</td>\n",
       "      <td>Tamil_Nadu</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   income  age  experience  married house_ownership car_ownership  \\\n",
       "0   1  1303835   23           3   single          rented            no   \n",
       "1   2  7574516   40          10   single          rented            no   \n",
       "2   3  3991815   66           4  married          rented            no   \n",
       "3   4  6256451   41           2   single          rented           yes   \n",
       "4   5  5768871   47          11   single          rented            no   \n",
       "\n",
       "            profession                 city           state  \\\n",
       "0  Mechanical_engineer                 Rewa  Madhya_Pradesh   \n",
       "1   Software_Developer             Parbhani     Maharashtra   \n",
       "2     Technical_writer            Alappuzha          Kerala   \n",
       "3   Software_Developer          Bhubaneswar          Odisha   \n",
       "4        Civil_servant  Tiruchirappalli[10]      Tamil_Nadu   \n",
       "\n",
       "   current_job_years  current_house_years  risk_flag  \n",
       "0                  3                   13          0  \n",
       "1                  9                   13          0  \n",
       "2                  4                   10          0  \n",
       "3                  2                   12          1  \n",
       "4                  3                   14          1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252000, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>married</th>\n",
       "      <th>house_ownership</th>\n",
       "      <th>car_ownership</th>\n",
       "      <th>profession</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>current_job_years</th>\n",
       "      <th>current_house_years</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7393090</td>\n",
       "      <td>59</td>\n",
       "      <td>19</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Geologist</td>\n",
       "      <td>Malda</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
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       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Firefighter</td>\n",
       "      <td>Jalna</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
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       "      <td>12</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>no</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>Thane</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1944421</td>\n",
       "      <td>49</td>\n",
       "      <td>9</td>\n",
       "      <td>married</td>\n",
       "      <td>rented</td>\n",
       "      <td>yes</td>\n",
       "      <td>Analyst</td>\n",
       "      <td>Latur</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>13429</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>single</td>\n",
       "      <td>rented</td>\n",
       "      <td>yes</td>\n",
       "      <td>Comedian</td>\n",
       "      <td>Berhampore</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   income  age  experience  married house_ownership car_ownership  \\\n",
       "0   1  7393090   59          19   single          rented            no   \n",
       "1   2  1215004   25           5   single          rented            no   \n",
       "2   3  8901342   50          12   single          rented            no   \n",
       "3   4  1944421   49           9  married          rented           yes   \n",
       "4   5    13429   25          18   single          rented           yes   \n",
       "\n",
       "    profession        city        state  current_job_years  \\\n",
       "0    Geologist       Malda  West Bengal                  4   \n",
       "1  Firefighter       Jalna  Maharashtra                  5   \n",
       "2       Lawyer       Thane  Maharashtra                  9   \n",
       "3      Analyst       Latur  Maharashtra                  3   \n",
       "4     Comedian  Berhampore  West Bengal                 13   \n",
       "\n",
       "   current_house_years  \n",
       "0                   13  \n",
       "1                   10  \n",
       "2                   14  \n",
       "3                   12  \n",
       "4                   11  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28000, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert datatype of selected fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"profession\"]=pd.factorize(train_data.profession)[0]\n",
    "train_data[\"income\"]=pd.factorize(train_data.income)[0]\n",
    "train_data[\"age\"]=pd.factorize(train_data.age)[0]\n",
    "train_data[\"married\"]=pd.factorize(train_data.married)[0]\n",
    "train_data[\"house_ownership\"]=pd.factorize(train_data.house_ownership)[0]\n",
    "train_data[\"car_ownership\"]=pd.factorize(train_data.car_ownership)[0]\n",
    "train_data[\"city\"]=pd.factorize(train_data.city)[0]\n",
    "train_data[\"state\"]=pd.factorize(train_data.state)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"profession\"]=pd.factorize(test_data.profession)[0]\n",
    "test_data[\"income\"]=pd.factorize(test_data.income)[0]\n",
    "test_data[\"age\"]=pd.factorize(test_data.age)[0]\n",
    "test_data[\"married\"]=pd.factorize(test_data.married)[0]\n",
    "test_data[\"house_ownership\"]=pd.factorize(test_data.house_ownership)[0]\n",
    "test_data[\"car_ownership\"]=pd.factorize(test_data.car_ownership)[0]\n",
    "test_data[\"city\"]=pd.factorize(test_data.city)[0]\n",
    "test_data[\"state\"]=pd.factorize(test_data.state)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drop the dependent variable from the train dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>married</th>\n",
       "      <th>house_ownership</th>\n",
       "      <th>car_ownership</th>\n",
       "      <th>profession</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>current_job_years</th>\n",
       "      <th>current_house_years</th>\n",
       "      <th>risk_flag</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Id  income  age  experience  married  house_ownership  car_ownership  \\\n",
       "0   1       0    0           3        0                0              0   \n",
       "1   2       1    1          10        0                0              0   \n",
       "2   3       2    2           4        1                0              0   \n",
       "3   4       3    3           2        0                0              1   \n",
       "4   5       4    4          11        0                0              0   \n",
       "\n",
       "   profession  city  state  current_job_years  current_house_years  risk_flag  \n",
       "0           0     0      0                  3                   13          0  \n",
       "1           1     1      1                  9                   13          0  \n",
       "2           2     2      2                  4                   10          0  \n",
       "3           1     3      3                  2                   12          1  \n",
       "4           3     4      4                  3                   14          1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain=train_data.drop(\"risk_flag\",axis=1)\n",
    "xtrain=xtrain.drop(\"city\",axis=1)\n",
    "xtrain=xtrain.drop(\"state\",axis=1)\n",
    "xtrain=xtrain.drop(\"profession\",axis=1)\n",
    "ytrain=train_data[\"risk_flag\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/sklearn/tree/tree.py:163: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n"
     ]
    },
    {
     "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,\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "DTClassifier= DecisionTreeClassifier(criterion='entropy', random_state=0)\n",
    "DTClassifier.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict on test_data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtest=test_data.drop(\"city\",axis=1)\n",
    "xtest=xtest.drop(\"state\",axis=1)\n",
    "xtest=xtest.drop(\"profession\",axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= DTClassifier.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 0])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=pd.DataFrame(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>4</th>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>27998</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27999</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0\n",
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "...   ..\n",
       "27995  0\n",
       "27996  0\n",
       "27997  1\n",
       "27998  1\n",
       "27999  0\n",
       "\n",
       "[28000 rows x 1 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred.to_csv('pred1.csv', encoding='utf-8',float_format='%1.0f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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