STEP 4 :Modeling the Random Forest Model.ipynb

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   "source": [
    "## 4- Modeling "
   ]
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   "source": [
    "* **Training the Random Forest model** "
   ]
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     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:    2.7s\n",
      "[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed:    4.3s\n",
      "[Parallel(n_jobs=-1)]: Done 1750 out of 1750 | elapsed:    5.9s finished\n"
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      "text/plain": [
       "RandomForestClassifier(max_depth=7, min_samples_leaf=6, min_samples_split=6,\n",
       "                       n_estimators=1750, n_jobs=-1, oob_score=True,\n",
       "                       random_state=42, verbose=1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
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   "source": [
    "RF = RandomForestClassifier(criterion='gini',\n",
    "                                           n_estimators=1750,\n",
    "                                           max_depth=7,\n",
    "                                           min_samples_split=6,\n",
    "                                           min_samples_leaf=6,\n",
    "                                           max_features='auto',\n",
    "                                           oob_score=True,\n",
    "                                           random_state=42,\n",
    "                                           n_jobs=-1,\n",
    "                                           verbose=1) \n",
    "RF.fit(X_train, y_train)"
   ]
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    "tags": []
   },
   "source": [
    "* **Predict our model**"
   ]
  },
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   "execution_count": 37,
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     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=4)]: Done 1750 out of 1750 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=4)]: Done 1750 out of 1750 | elapsed:    0.6s finished\n"
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   "source": [
    "y_pred_train = RF.predict(X_train)\n",
    "\n",
    "y_pred_test = RF.predict(X_test)"
   ]
  },
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    "tags": []
   },
   "source": [
    "* **Accuracy of the model**"
   ]
  },
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   "execution_count": 38,
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      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:    0.7s\n"
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     "text": [
      "Model Prediction Score 85.96\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 1750 out of 1750 | elapsed:    1.0s finished\n"
     ]
    }
   ],
   "source": [
    "accu = RF.score(X_train, y_train)\n",
    "print( \"Model Prediction Score\", (accu * 100).round(2))"
   ]
  },
  {
   "cell_type": "markdown",
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     "duration": 0.220163,
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     "start_time": "2020-12-26T14:07:42.035076",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* **Training Accuracy\\ Testing Accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-26T14:07:42.732650Z",
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     "text": [
      "Training accuracy:  0.8595505617977528\n",
      "Testing accuracy:  0.7877094972067039\n"
     ]
    }
   ],
   "source": [
    "print(\"Training accuracy: \", accuracy_score(y_train, y_pred_train))\n",
    "print(\"Testing accuracy: \", accuracy_score(y_test, y_pred_test))"
   ]
  }
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