Naive Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be1a3a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import scipy.stats as stats\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "06cda0c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def convert(data):\n",
    "    number = preprocessing.LabelEncoder()\n",
    "    data['operator'] = number.fit_transform(data.operator)\n",
    "    data['methodReturn'] = number.fit_transform(data.methodReturn)\n",
    "    data['isKilled'] = number.fit_transform(data.isKilled)\n",
    "    return data\n",
    "\n",
    "# Read in data and display first 5 rows\n",
    "features = pd.read_csv('/home/akash/Documents/repos/MLProject/training_data.csv',encoding= 'unicode_escape')\n",
    "#print(features)\n",
    "#print('The shape of our features is:', features.shape)\n",
    "\n",
    "#Convert string to float\n",
    "features=convert(features)\n",
    "#print(features)\n",
    "#print('The shape of our features is:', features.shape) \n",
    "\n",
    "# Use numpy to convert to arrays\n",
    "# Labels are the values we want to predict\n",
    "labels = np.array(features['isKilled'])\n",
    "# Remove the labels from the features\n",
    "# axis 1 refers to the columns\n",
    "features= features.drop('isKilled', axis = 1)\n",
    "# Saving feature names for later use\n",
    "feature_list = list(features.columns)\n",
    "# Convert to numpy array\n",
    "features = np.array(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bca6a62d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB()  mean accuracy:  79.543 % std:  0.0 %\n",
      "Number of mislabeled points out of a total 591813 points : 121038\n"
     ]
    }
   ],
   "source": [
    "model = GaussianNB()\n",
    "cv_scores = cross_val_score(model, features, labels, cv=5)\n",
    "    \n",
    "print(model, ' mean accuracy: ', round(cv_scores.mean()*100, 3), '% std: ', round(cv_scores.var()*100, 3),'%')\n",
    "y_pred = model.fit(features, labels).predict(features)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\" % (features.shape[0], (labels != y_pred).sum()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "23ede49c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "\n",
    "# cv_method = RepeatedStratifiedKFold(n_splits=5, \n",
    "#                                     n_repeats=3, \n",
    "#                                     random_state=999)\n",
    "\n",
    "# from sklearn.preprocessing import PowerTransformer\n",
    "# params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}\n",
    "\n",
    "# gs_NB = GridSearchCV(estimator=model, \n",
    "#                      param_grid=params_NB, \n",
    "#                      cv=cv_method,\n",
    "#                      verbose=1, \n",
    "#                      scoring='accuracy')\n",
    "\n",
    "# Data_transformed = PowerTransformer().fit_transform(features)\n",
    "\n",
    "# gs_NB.fit(Data_transformed, labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84e24490",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(gs_NB.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3dd7982",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB(var_smoothing=0.0001873817422860383)  mean accuracy:  79.54 % std:  0.0 %\n",
      "Number of mislabeled points out of a total 591813 points : 121054\n"
     ]
    }
   ],
   "source": [
    "model = GaussianNB(var_smoothing=0.0001873817422860383)\n",
    "cv_scores = cross_val_score(model, features, labels, cv=5)\n",
    "    \n",
    "print(model, ' mean accuracy: ', round(cv_scores.mean()*100, 3), '% std: ', round(cv_scores.var()*100, 3),'%')\n",
    "y_pred = model.fit(features, labels).predict(features)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\" % (features.shape[0], (labels != y_pred).sum()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e61ce3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "sns.heatmap((metrics.confusion_matrix(labels,y_pred)),annot=True,fmt='.5g',cmap=\"YlGn\").set_title('Test Data');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b8450dde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB()  mean accuracy:  63.161 % std:  0.003 %\n",
      "Number of mislabeled points out of a total 148492 points : 54892\n"
     ]
    }
   ],
   "source": [
    "features = pd.read_csv('/home/akash/Downloads/Machine/Machine Learning Final Project/Data/testing_data.csv',encoding= 'unicode_escape')\n",
    "features_test=convert(features)\n",
    "labels_test = np.array(features['isKilled'])\n",
    "features_test= features_test.drop('isKilled', axis = 1)\n",
    "features_test = np.array(features_test)\n",
    "\n",
    "model_test = GaussianNB()\n",
    "\n",
    "cv_scores = cross_val_score(model, features_test, labels_test, cv=5)\n",
    "    \n",
    "print(model_test, ' mean accuracy: ', round(cv_scores.mean()*100, 3), '% std: ', round(cv_scores.var()*100, 3),'%')\n",
    "y_pred = model.fit(features_test, labels_test).predict(features_test)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\" % (features_test.shape[0], (labels_test != y_pred).sum()))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "805b9ab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB(var_smoothing=0.0001873817422860383)  mean accuracy:  63.161 % std:  0.003 %\n",
      "Number of mislabeled points out of a total 148492 points : 54892\n"
     ]
    }
   ],
   "source": [
    "features = pd.read_csv('/home/akash/Downloads/Machine/Machine Learning Final Project/Data/testing_data.csv',encoding= 'unicode_escape')\n",
    "features_test=convert(features)\n",
    "labels_test = np.array(features['isKilled'])\n",
    "features_test= features_test.drop('isKilled', axis = 1)\n",
    "features_test = np.array(features_test)\n",
    "\n",
    "model_test = GaussianNB(var_smoothing=0.0001873817422860383)\n",
    "\n",
    "cv_scores = cross_val_score(model, features_test, labels_test, cv=5)\n",
    "    \n",
    "print(model_test, ' mean accuracy: ', round(cv_scores.mean()*100, 3), '% std: ', round(cv_scores.var()*100, 3),'%')\n",
    "y_pred = model.fit(features_test, labels_test).predict(features_test)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\" % (features_test.shape[0], (labels_test != y_pred).sum()))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6c4954f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "sns.heatmap((metrics.confusion_matrix(labels_test,y_pred)),annot=True,fmt='.5g',cmap=\"YlGn\").set_title('Test Data');\n"
   ]
  }
 ],
 "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}