data-statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importimi i librarive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#Calculate pearson correlation \n",
    "from scipy.stats import pearsonr\n",
    "#Calculate spearman correlation\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "#per mi paraqit grafikisht\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"train.csv\")\n",
    "\n",
    "#Remove missing values.\n",
    "data = data.dropna()\n",
    "\n",
    "data = data.rename(columns={'Vleresimi(F. Kadriu)': 'Vlerësuesi: Fatbardh Kadriu', 'Vleresimi(F. Gashi)': 'Vlerësuesi: Fatbardh Gashi', 'Vleresimi(A. Musa)': 'Vlerësuesi: Arbena Musa'})\n",
    "data.drop(['Id', 'Emri i komentuesit', 'Comment timestamp', 'Post timestamp', 'Like', 'URL'], axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>Komenti</th>\n",
       "      <th>Vlerësuesi: Fatbardh Kadriu</th>\n",
       "      <th>Vlerësuesi: Fatbardh Gashi</th>\n",
       "      <th>Vlerësuesi: Arbena Musa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Kjamet</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>So keq sa per fillim</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Gilanin mdoket pe harroni kejt</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Ni pytje per juve a jeni ne dijeni qe personat...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Komenti  \\\n",
       "130                                            Kjamet    \n",
       "131                               So keq sa per fillim   \n",
       "132                     Gilanin mdoket pe harroni kejt   \n",
       "133  Ni pytje per juve a jeni ne dijeni qe personat...   \n",
       "\n",
       "     Vlerësuesi: Fatbardh Kadriu  Vlerësuesi: Fatbardh Gashi  \\\n",
       "130                          2.0                         2.0   \n",
       "131                          1.0                         1.0   \n",
       "132                          0.0                         0.0   \n",
       "133                          2.0                         2.0   \n",
       "\n",
       "     Vlerësuesi: Arbena Musa  \n",
       "130                      2.0  \n",
       "131                      1.0  \n",
       "132                      2.0  \n",
       "133                      2.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shfaqja e rreshtave prej 130 deri 134 \n",
    "data.head(n=134)[130:]\n",
    "\n",
    "#Shfaqja e 5 rreshtave te fundit\n",
    "#data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1435, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.rename(columns={'Vlerësuesi: Fatbardh Kadriu': 'Vlerësuesi 1', 'Vlerësuesi: Fatbardh Gashi': 'Vlerësuesi 2', 'Vlerësuesi: Arbena Musa': 'Vlerësuesi 3'})\n",
    "vleresimet = data[['Vlerësuesi 1', 'Vlerësuesi 2', 'Vlerësuesi 3']]\n",
    "\n",
    "data.shape # Kthen liste te tipit (numri i rreshtave, numri i kolonave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vlerësuesi: Fatbardh Kadriuor\n",
      "Vleresime pozitive: 276\n",
      "Vleresime negative: 536\n",
      "Vleresime neutrale: 623\n"
     ]
    }
   ],
   "source": [
    "#Vleresimi 1\n",
    "print(\"Vlerësuesi: Fatbardh Kadriuor\")\n",
    "print(\"Vleresime pozitive:\", (data['Vlerësuesi 1'] == 1).sum())\n",
    "print(\"Vleresime negative:\", (data['Vlerësuesi 1'] == 2).sum())\n",
    "print(\"Vleresime neutrale:\", (data['Vlerësuesi 1'] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fatbardh Kadriu')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 410.4x410.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Vlerësuesi 1'].value_counts().plot.pie(autopct='%1.1f%%',figsize=(5.7,5.7),\n",
    "                                                     labels=['Neutrale', 'Negative', 'Pozitive'],\n",
    "                                                    colors = ( \"#F1C40F\", \"#E74C3C\", \"#2ECC71\"))\n",
    "plt.title('Fatbardh Kadriu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vlerësuesi: Fatbardh Gashi\n",
      "Vleresime pozitive: 133\n",
      "Vleresime negative: 397\n",
      "Vleresime neutrale: 905\n"
     ]
    }
   ],
   "source": [
    "#Vleresimi 2\n",
    "print(\"Vlerësuesi: Fatbardh Gashi\")\n",
    "print(\"Vleresime pozitive:\", (data['Vlerësuesi 2'] == 1).sum())\n",
    "print(\"Vleresime negative:\", (data['Vlerësuesi 2'] == 2).sum())\n",
    "print(\"Vleresime neutrale:\", (data['Vlerësuesi 2'] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fatbardh Gashi')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 410.4x410.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Vlerësuesi 2'].value_counts().plot.pie(autopct='%1.1f%%',figsize=(5.7,5.7),\n",
    "                                                     labels=['Neutrale', 'Negative', 'Pozitive'],\n",
    "                                                    colors = ( \"#F1C40F\", \"#E74C3C\", \"#2ECC71\"))\n",
    "plt.title('Fatbardh Gashi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vlerësuesi: Arbena Musa\n",
      "Vleresime pozitive: 185\n",
      "Vleresime negative: 715\n",
      "Vleresime neutrale: 535\n"
     ]
    }
   ],
   "source": [
    "#Vleresimi 3\n",
    "print(\"Vlerësuesi: Arbena Musa\")\n",
    "print(\"Vleresime pozitive:\", (data['Vlerësuesi 3'] == 1).sum())\n",
    "print(\"Vleresime negative:\", (data['Vlerësuesi 3'] == 2).sum())\n",
    "print(\"Vleresime neutrale:\", (data['Vlerësuesi 3'] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Arbena Musa')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4s0DeWwG5wT/80WyHlW0X+FPa3AMMcJ0lTN5cVc+Ly1Yx4eW3uPCdj3htxZdc9M5HjOrXh0cOOYAXo2M5aechjOiz7abBmhUJhvfpxeCepZQWFfG1oYOYtXrLmV3e/XItACP79ubRz7/g7op9+GjNej5ZuyGQ91cgioC/xWPR4a6D5Kptd+2a9vwIONJ1iLC5ct/duHLf3QB4fUWCOz9ZzO1j92ZFQyODe/YgpcrNH3/G2SO2vRbKLr178vbqNaxPJuldVMRrK75k9IAtR8i//WghvztoT5pUSflH3xQhbEgls/7eCkwZ8CfgWNdBcpGNbDspHouOAm5wnaOQPL54OZWvzCI6/W3Ke/Xk28O8nWfxjQ2cOfN9AMYN7M8JQwdxzIw5HDHjHVKqfGf45lJ+Lr6SMWX9Ke/VkwGlJVQMjHDEq7MB2D/Sb9sXNek6Jh6L2hx7bbDjbDvBPxe8BpjoOosxeeBL4IDy6prPXQfJJTay7ZzLsKI1prMG4M0ibVqxkW0H4rHoAcAsoKfrLMbkGbt+QitWtu3wpx+fCYxzncWYPJTA25xgp/NimxE6chVWtMZ0Vxlwl+sQucJGttsRj0XH4Y1q7fA4Y9JzTnl1zf2uQ7hmZduGeCxaBMwGRrvOYkwIrAb2L6+uWeo6iEu2GaFtZ2FFa0ymDARucx3CNRvZbsW/XNy/gWGusxgTMoeUV9fMdB3CFRvZbuu/sKI1JhsK+gxMG9m2Eo9FdwDm4+1FNcZk3nHl1TUvuA7hgo1st3QVVrTGZNOv/avnFRwrW188Ft0duMh1DmNCbixwmusQLljZbnY90MN1CGMKwHX+2ZkFxcqWTScwnO46hzEFYk8KcP4+K1vP74CC3I5kjCNXx2PR3q5DBKngy9afN8lmXzAmWEPxDrMsGAVftsDPXAcwpkBdHo9FB7oOEZSCLtt4LHogMMl1DmMKVBnwn65DBKWgyxZvAkdjjDsX+Bd+Cr2CeJNt8X99+Y7rHMYUuOHACa5DBKFgyxb4PtDHdQhjTGFsSijIayP4v7bMA3Z3ncUYgwKjyqtr5rsOkk2FOrL9Ola0xuQKAS50HSLbCrVsL3YdwBizhe/515IOrYIr23gsug9wlOscxpgtDAJOdR0imwqubPEO97JTc43JPaHeUVZQO8jisWgEWAz0c53FGNOmivLqmtmuQ2RDoY1sT8KK1phc9kPXAbKlEMvWGJO7Tg/rjrKCKVv/cm7Hus5hjGlXX0J6Fb6CKVu8orUzxozJfSe6DpANhVS2tgnBmPwQymslFMTRCP58R8uAHVxnMcZ0yvjy6pq3XYfIpEIZ2R6OFa0x+STmOkCmFUrZ2iYEY/JL6LbbWtkaY3LR2HgsuqvrEJkU+rKNx6LjgWGucxhjuixUmxJCX7bYqNaYfGVlm2dCeRiJMQXgyHgs2td1iEwJddnGY9F+wIGucxhjuqUncIzrEJkS6rIFKgj/ezQmzA51HSBT2i0iESkXkXL/9hAR+ZaI7B9MtIw42HUAY0xaKlwHyJTtlq2I/AD4J/CGiPwQeBpv++dUETk3oHzpsrI1Jr+Ni8eiobjYf0k7j/0I2B/oDSwE9lTVuIgMBF4B/hxAvnRZ2RqT3wYAI/Fmw85r7W1GaFLV9aq6EpivqnEAVV2NN/VwTovHojsBw13nMMakLRSbEtor25SIlPq3v96yUER6dbBerrBRrTHhEPqy/Rb+CFZVP2+1fBDwk2yGyhArW2PCIRRlu91ttqr62XaWL8abNDHXWdkaEw7jXAfIhHzYHNBdE1wHMMZkRFk8Fh3pOkS6Qlm28Vh0FDDQdQ5jTMbk/aaEUJYtMNp1AGNMRuV92W53m62ITFHVU0XkXbY81EsAVdWDsp6u++yQL2PCJe+327Z3UsN/+d/z8apZobrosDEm/wdQ292MoKpL/ZsrgEWquhDvKjyjgSUBZEuHla0x4bKz6wDp6sw22xlALxHZBXgZ+B7wl2yGygArW2PCpZ9/ydS81ZmyFVVdj3eSw22q+k1gv+zGSpuVrTHhM9R1gHR0qmxFZCJwJvCMv6y9bb1OxWPRIvL8L8UY06a83pTQmbL9MfA/wOOq+r6I7IF31a9cNZQc/jAwxnRbXg+iOiwlVX0VeFVE+vr3PwEuyXawNNgmBGPCKa/LtsORrYhMFJEPgA/9+6NF5I6sJ+s+K1tjwin0mxFuBo4FVgKoah1weDZDpcnK1phwCvfIFkBVF221KJmFLJliZWtMOOV12XZmR9IiETkUUBHpgbe99sPsxkpLuesAxpisCP1mhAuBi4BdgM+BMf79XNXbdQBjTFaEe2SrqivwjrHNFz1dBzDGZEUf1wHS0WHZisi9tDHBo6p+PyuJ0mdla0w45fXx853ZjPA03pljz+BdGyECrM1mqDRZ2RoTTuKfIZqXOrMZ4bHW90Xkb8BLWUuUPitbY8KrBGh0HaI7ujMsH0UOX1uyx3Gfz6NYN5AS0ZRACkgJpERQxL+NpkRIIaTEX+bd1pQU0fKYSsvjReo93vKY912liBRFmpKiltukpAj1v6ekSL3bxSjFfoZi774Uk6IYJG8/qY1xoJSwlq2IrGHLbbZx4PKsJUpTyZjV++MdMZE3VEnhHbvc8pXyv5Lopvvq30+BpLzvtPouKRQFFG1137utqv49lU3LUCAlm5enUFXxn0G8Z1fvw6llmbZ8eKmI9+pbfHj5yzd9yIm/TlHLz7X6ICvSzfeLWj7USMHmD7xNH1wtj235waYiy1J9pVmK7QOrQAgU5+uxnZ3ZjNA/iCAZtM3OvFwnQhHe9vPSbR9sa4223mL7b7vNpwmBD4r3fPPXJdGxiGz7Z2dCqc51gG7qzLURKlsuQiMi3xGRKhEZkf1oxnTsa8l5B1/c/OZbqKZcZzGBaHIdoLs68+vXncB6ERkN/D9gIXB/VlMZ0wWnJt8/9PvN77yOat79VmO6rNl1gO7qTNk2q/eP+BvALap6C5DLmxY2ug5ggve95JzDTkl+MMN1DpN1oS7bNSLyP8BZwDMiUkxb2xZzx5euAxg3/qt55qRjkx9Pd53DZE2yrqIqb3976UzZngY0AN9X1TjeNRJ+l9VU6Um4DmDcuaqpZnJl8rNXXecwWZG3o1roRNn6BfsYm08WWAE8ns1QaVrtOoBx64amlyaNSS21wg2fXD5ztUPbLVsROdL/fj7wd+Au/6FdgCeyH63bbGRruLXxucP3Sq2ocZ3DZNRS1wHS0d7ItmU2houASqAeQFU/BnbMcq50WNkaBORPjU9VDk8lXnedxWTMEtcB0tFe2c7yvzeq6qbT40SkhNw+ccDK1gBQBEX3NT5+8E669k3XWUxGhLZs9/e/TxeRK4DeInI08ChQnfVk3WdlazYpQUseavj76IG6YbbrLCZt4SxbVf2Nf/NyYDnwLvAD4FngquxH6zYrW7OFHqR6TmmYsk8/bZjrOotJS16XbbvXRhCRImCuqh4A/CmYSGlb5jqAyT29SPaZ0vDoiJN7nvbhBind13Ue0y2LXQdIR7uHfql3vnmdiOTsJRXbMN91AJOb+tM4YErDlJ16aPM811lMt+T1yLYzJzUMBd4XkZdF5KmWr2wH665IZWIt8IXrHCY3ldGww8MNf4+UaHKh6yymy/K6bDtz8fBrs54i8+aT24enGYeGsH7HBxsfW3xGj1MWJ6VoF9d5TKek8K6lnbc6cwbZq8ACoNS//RaQ63t2bVOCadfOunaXexufaBJV+y0oPyyvq6gK9+m6eXgGGVjZmk7YXRO73d341Jeiusp1FtOhvN6EAJ3bZptvZ5CBla3ppH105ajbGp9dhmq96yymXZ+7DpCuzpRtQ56dQQZWtqYLRuuyfX/XNG0BqutdZzHbla+z4WzSmbJ9Nc/OIAMrW9NFh6Q+P+japukfotrgOotp06yOfyS3daZsf0Z+nUFGpDKxDLDtcKZLjkx9WnF5c20dqnm9Iyak3nIdIF0S1mmb6mvLngeOdZ3D5J+Hiw94/faSCYfgnUFp3FtaV1G1s+sQ6drucbYi8i7tbJtV1YOykihzZmJla7rh28n3Dl0rpTX3FY85DJGwzgKfT/J+VAvtn9RwQmApssMuq2e67bzmd6Jr6Pnq1JL9Jrl4/UXXvkj9a59QMrAPe085G4Alt8xgzYxPkNJieuw6gGG/OIbi/r22WXfF32az8vH3AGWHkw5kyBnjAFh6aw1rXl9Ar72GMPyXxwGw+pkPSNZvZPDp4wJ7b92Q99trof1ttj8FdlHVhW19BRUwDVa2Ji2XNr8x6ejkvOkuXntgbD92v+2bWyzr/5UR7PXI2ez18Fn0HD6QL+7ddsC3cd4KVj7+HqPuP529HjqLNa99QsNnq0mubWDd3CXs9fBZkFI2zFtBamMzq5/+gEH/MTqot9VdoRjZtle2HwM3icgCEfmNiIwJKlQmRCoTy/HOfDOm265umjF5YnLR9KBft9+4XSmJbDlq7X/ICKTE+y/b58ChNH2x7ZRcGxesos+BQynqVYqUFNF33K58+co8EEGbUqgqqYZmpKSI5X+dxaDTxiIlxYG8pzSEu2xV9RZVnQhMwtuzf6+IfCgiV4vIXoElTI+Nbk3afts0bfJBqXhOTSC56qn36H/obtss7zVyEOve+ZzmxAZSG5tYU7uApmVrKe7bgwFH7snHZz5Ij50jFPfrwfoP4gyYPDL48F2zoK6iaqXrEJnQ4YVo/E0GvwF+IyJjgXuAXwA5/3GIt5PsVNchTP77Q+Ozh5/b4xuvfVw06DDXWZb9eSZSXETZ8fts81iv3Qex49kT+OSiqRT3KaXXqMFIsbePb8dzJrDjORMAWHTdNMp/cCgrn3iXtW8spNeeQ9jpvK8E+j46KRSjWujctRFKRSQmIg8CzwH/Bk7OerLMmOk6gAkHfwLJibumvvynyxyrnn6fNa99yvDrj9/ugRI7nHQAez14JiP/dCrFA3rRY/jALR7f8C/v2js9Rwwk8cyHjLjhBDbOX0HDZ6uznr8bQrFzDNqfyvxoEbkH75zkC/BOZhipqqepaq5fiKbFLGCd6xAmHIrR4r82Th2/o6MJJNe8voDl981it6oTKepVut2fa17lnXXcGK+n/h/zKDt27y0ej//xdcovnIg2J9FkCgApElIbc/JcjtCMbLd7UoOIvAI8BDymeXxVpPrasqeAmOscJjwaKN54Ss9TP0xI77HZeo2FVzzLurcX0ZzYSOmgPux0wUS++MubaFOS4gG9AehzQDm7XnEUTcvX8vl109j9Vu/ohXnnPULyy41ISRFDL51E/4M3T7Ty5fR5bPz3cna6YCIAS26ewdp/LqDXqCEMv/74bL2d7loHDK6rqNroOkgmhPYMshb1tWU/AP7oOocJlw2UrPtWz9M+WSs9D3SdJcSm1lVU5csmyw4VwumIz7oOYMKnN819pzQ8OryXNv3LdZYQe9x1gEwKfdlGKhOLgPdc5zDh400g+eiQHtpsV5nLvGbgadchMin0Zeuz0a3JioFsHPRww9/72QSSGfdqXUVVwnWITLKyNSZNQ1i/0wONU4uLNJX3U7fkkHw54qnTCqVsa4EvXYcw4bWLrtn13sYnGkR1uessIaBY2eanSGWiGXjBdQ4TbntoYve7GqsTopqTZwfkkbfrKqryfs6xrRVE2foech3AhN++umLULY3PLUV1jesseSx0o1oorLJ9FljhOoQJv7Ea3++3TdM+sQkku83KNp9FKhNN2OjWBGRi6vPR13gTSDZ2/NOmlY/rKqredx0iGwqmbH33uQ5gCsdXU59W/LT59XdsAskuCeWoFgqsbCOVidnYCQ4mQCcmP/rKhc2zZqKacp0lT4R2QFRQZesL7V+myU1nJt+tPCtZV+s6Rx6oCesmBCjMsn0QSLoOYQrLBc2zoyc1f5hTsz3koDtdB8imgivbSGViKTDNdQ5TeH7S/M9JX01+YoXbtuXAY65DZFPBla3vLtcBTGG6pmn6pK84mEAyD9xTV1EV6iM3CrVsn8Kb3seYwN3YNG3yAallM1znyCEpCmAAVJBlG6lMpIAq1zlM4bqj8ZnoHqlVr7nOkSOerKuo+rQrK4hIUkTmiMh7IvKoiPTp4vrjReRW//ZkETm01WMXisjZXXm+zswJbZcAAA+OSURBVCjIsvXdB3zhOoQpTAJyT+OTE3dJ1TudQDJHdGfgs0FVx6jqAUAjcGFXVlbVWap6iX93MnBoq8f+qKr3dyNTuwq2bCOViY3AH1znMIWrGC1+oPGx8YN1XWgmNeyGN+sqqtId4dcAe4rIDiLyhIjMFZE3ROQgABF51h8FzxGRL0XkHH80+7SI7IZX1Jf6j0dF5BoRuUxE9hWRTZN7ishuIjLXv10hIq+KyNsi8oKIDO0oZMGWre8OwM5fN86UoKUPN/z9wAG68R3XWRy5KZ2VRaQEOB54F7gWeEdVDwKuAO4HUNWvqeoY4FxgIa3OUlPVBXhzFP7eHynXtHrsQ6CHiOzhLzoNmCIipcBtwCmqWgHcA/yqo6wFXbaRysRK4F7XOUxh60my15SGKaP6amOhnd24gO4f7tVbROYAs4DPgD8DhwF/BVDVfwCDRGQAgIgM9h87Q1W7cm3rKcCp/u3TgEeAvYEDgGl+hquAXTt6ooIuW18VdpKDcawPzf0ebZgyrJc2feQ6S4Buqquo6u7/vZZttmNU9WL1Lvgjbfycikgx8DDwS1Xt6gfaI8CpIrIXoKr6sf8677d6/QNV9ZiOnqjgyzZSmfgE79PLGKf60zjgkYZHB5VqshAmkJxH5g/3mgGcCd4RBsAKVa0HbgDmqurD21lvDdC/rQdUdT7eYOzneMUL8BEwREQm+q9VKiL7dxSu4MvWdxXeHk1jnNqBjYMfbni0b4kmP3OdJct+VldR1ZTh57wGGO/vxLoBOMdffhlwTKudZCdutV418M2WHWRtPO8jwHfwB2X+KPoU4DciUgfModXRDNsjqtqN9xQ+9bVlNwP/5TqHMQCfS//Pz+xxcnFKijrcy52Hausqqg5zHSJoNrLd7DpsUkiTI3bVNbve0/jkxpBOIPkT1wFcsLL1+Ucm3OA6hzEtRurq3e9sfHoVqgnXWTJoSl1F1UzXIVywst3SzcAi1yGMabG/Lt/75qbnF4dkAslG4GeuQ7hiZduKf1bZz13nMKa1itTS/X/d9NJ8VDe4zpKmP3T1GghhYmW7rb8Cda5DGNPaYalFY65qmvF+Hk8guQq43nUIl+xohDbU15Z9FXjJdY6gbWxQjv/ROhobleYkfOOIUq44txffvXo98z7zjj3/cq0yoJ/w2l/6d2pdgKvv2MBLM5s5cM9i7vq5d3Gmh59vZHW98sNTewb7JvPc48X7vFFVMnEC3oH6+eS/6yqqfu86hEslrgPkokhl4uX62rIH8I6tKxg9e0D1LX3p10doalaO/eE6jv5KM3/55ear11152wYi/bY9UWd76+61WzFvvpfk9fv6c96163l/fpI9di3iwecamXpT3yDfXih8M/mvQ9ZRWntXyfhDEWnrjKlcNB+43XUI12wzwvb9N7DSdYggiQj9+nj/f5uaoSmptP7vrKo8/koTpxxV2ul1i4qgsclbd0ODUloCtz7UwIWn9KS0JF+6Ird8J/lu5RnJd2s6/smc8ZOwz8LQGVa22xGpTCzHO/OkoCSTymHfXcOesXqOGF/C+P03//Lzel2SIQOLGDms7d9g21q3fx/hxMmlRL+3lt2GFhHpK8z+MMnXo9sWtum8HzbPOvzE5n/lw3xm99dVVD3pOkQusG22HaivLZsGHOU6R9ASa5TvXLGO317am/328Mr10hs3sMcuRVx8evvbWdtat8WPbljP+d/qyZyPkvzjzWYOGFnET7/bK2vvI+yuLj3i1VeKd5/kOsd2LAQOqquoqncdJBfYyLZj5wFrXYcIWll/4bCxJbz0RjMAzc1K9atNfOurHY9It163Rd2/vZ1sew4r4uHnG7nvuj588GmK+Yvsomvd9cumVyaNTy7OxRFuCjjbinYzK9sORCoTC4H/5zpHEFasTpFY4/2ms6FBmT6rmb1GeP9EWm7vsmPb/2TaW7fFr/5vI1ee14umZkimvGVFAus3ZukNFYjfN70wab/UF7k2geRNdRVVuZbJKSvbzvkj8A/XIbItvlI54ZK1HHrOGo44by1HTCjhuEpvJPvYy02cvNWOsaUrUpxy2boO1wV4ekYT4/YpZujgIsr6CxP2L2bi2WsQgQNH5dtRTLnnj41PR3dPrap1ncM3F+9KeqYV22bbSfW1ZbviXUptkOssxrQliSRP73HyW0uLIoc4jNEATKirqHrXYYacZCPbTopUJj4HzgLs08nkpGK0+MHGqeMG6fpZDmNcZUXbNivbLohUJp4Dfu06hzHbU0qqx8MNj+4f0Y1zHLz8dLo3LXlBsLLtuqvx/lEZk5N6kez9aMOUPftq4/sBvmw9cE5dRVUqwNfMK1a2XRSpTCSB04FlrrMYsz19aO43pWHKLj21OagJJC+qq6gK+1Q+abGy7YZIZSKOV7j2KW5yVoTGskcaHt2hVJPZvqzhjXUVVQ9k+TXynpVtN0UqE6/gTTBnTM4axIYhf2v4e69iTWXrovhTKZDj0NNlZZue64GnXIcwpj07sW7o/Y1TtUhT8Qw/9VvAd+oqquwInU6w42zTVF9b1gd4BTjYdRZj2vOx7DD/3B7fGKAigzPwdAuBr9RVVNm+i06ykW2aIpWJ9cAJeNfsNCZnjdJVI+9ofHolqunOIl0PfN2KtmusbDPAvxzj8cAK11mMac8Bunzv3zc9vwjV7l5cqRn4j7qKqiAPKwsFK9sMiVQmPgZOBPJ9Uj4TcuNTSw/4VdPLH6PanUsAXVRXUfVixkMVACvbDIpUJv4JnIkdEmZy3OGpz8Ze2TTjXVSburDajXUVVXdnLVTIWdlmWKQy8TjwY9c5jOnIcan5E37c/MYsVDtzQWE7xCtNVrZZEKlM3Ab8wnUOYzpycvLDiec3z/4n7R+W9Ap2iFfarGyzJFKZ+CVwpescxnTk7GTdYd9Ovre9CSRfwjvywPZFpMnKNosilYn/xX71Mnngoua3Dj+h+aPpWy1+AYhZ0WaGlW2WRSoTvwMudZ3DmI5c3lw7+fDkgun+3eeAb9RVVNmkRRliZ5AFpL627EfArYC4zmJMe35ROvm2fxTvcVldRVWj6yxhYmUboPrasguBO7DCNbnrAeB7kcpEc4c/abrENiMEKFKZ+CPwfaArxzYaE5RbgbOtaLPDRrYO1NeWHQk8BpS5zmKM75pIZeJa1yHCzMrWkfrasr2BZ4CRrrOYgrYRODdSmXjIdZCws7J1qL62bBDwOBB1ncUUpMXASZHKhMvZeAuGbbN1KFKZWAkcBdzvOospODOBCVa0wbGRbY6ory27Am/mBztSwWTb/cAFkcpEg+sghcTKNofU15adBNwDDHSdxYRSCrg8Upm40XWQQmRlm2Pqa8uGAw8Ch7nOYkJlBd5hXc+5DlKobJttjolUJj4DJgPXYdfFNZnxPHCgFa1bNrLNYfW1ZZPxzujZxXEUk5824l0I6Q+RyoT9R3fMyjbH+YeH3QvEXGcxeWUOcGakMvGB6yDGY2WbJ+pryy4GbgD6uM5icloKuBH4eaQyYReSySFWtnmkvrZsBHA78HXXWUxOWgh8N1KZmO46iNmWlW0eqq8tOxnvoiE7u85ickID3mj2fyOVifWuw5i2WdnmqfrasgjeSRAXYUeVFLIXgIsjlYmPXQcx7bOyzXP1tWUTgLuAsa6zmEB9BlwaqUxMdR3EdI6NiPJcpDLxFjABuARY7jiOyb5G4H+Bfa1o84uNbEOkvrasH958Z5cBEcdxTGYpMBW4IlKZ+LfrMKbrrGxDqL62bAfgZ8CPgN6O45j0PYF3ce8610FM91nZhlh9bdnOwM+Bc4FSx3FM1z2FV7LvuA5i0mdlWwDqa8tGAr8ATgdKHMcxHXsGr2TtWrMhYmVbQOpry3bF25F2ATDAcRyzpWa8kexvIpWJN12HMZlnZVuA6mvL+gPn4R2ja3OguRUH/gTcFalMLHYdxmSPlW0Bq68tE+B4vNI9HpslIkg1eKdeT41UJmxq+wJgZWuATdt1zwbOAPZ0HCes1uJdMvOOSGXiXddhTLCsbM02/LPSzgBOA4Y6jpPv1uPt8HoUeMauXVC4rGzNdtXXlhXhzRpxBnAyUOY0UP6wgjXbsLI1nVJfW9YDOAY4Fjga2NttopxTj3dRGCtY0yYrW9Mt/sSUR+EV71HAYLeJArcOeA14xf96O1KZSLqNZHKZlW2aRESBKlX9iX//MqCfql7TjecqA85Q1Tu6se4CYLyqrujquunyj2oYg1e8k4BxQHnQObJsA/A6m8v1LTuKwHSFlW2aRGQjsBSYoKor0izb3YCnVfWANh4rVtXtjpxclm1b6mvLhuJd9nEsXvmOBXZ3Gqrz1gBz8ebxesf//q5NM2PSYadupq8ZuBvvaltXtn5ARIYAfwSG+4t+rKq1InINsFZVb/R/7j3gBLw5xkaKyBxgGt5Oll/glfkYYD8ReQIYBvQCblHVu7cOJCLfwTtTrAcwE/jP9oo6GyKViaV4uZ9tWVZfWzYQ/33g/ZkMB0b434cS7CU/E8AS/2sh8EGrr0U2G63JNCvbzLgdmCsiv91q+S3A71X1NREZjrcDZd92nudnwAGqOgZARCYDB/vLPvV/5vuqukpEegNvichjqrqy5QlEZF+8Q7YqVbVJRO4AzgTuT/9tpidSmVjN5l/Dt1BfW1aKN2V7S/nuCPQF+m31vfXtErwpYVp/bdzqfj1e6S9p/WU7sEzQrGwzQFXrReR+vNHkhlYPHYU3Gm25HxGR/l18+jdbFS3AJSLyTf/2MGAUsLLV418FKvCKGLxLLH7RxdcMnL/9c4H/ZUzoWNlmzs3AbODeVsuKgImq2rqAEZFmtvyVuVc7z7uu1XqT8Qp8oqquF5HpbawrwH2q+j9dfQPGmOyxaXEyRFVXAVPwrh3b4kW8C3gDICJj/JsL8HYaISLj2LzjaA3Q3sh3ALDaL9p9gEPa+JmXgVNEZEf/+XcQkRFdfkPGmIyyss2sm9jyeNNLgPEiMldEPgAu9Jc/Buzg7wj7IfBvAH/ba62IvCciv2vj+Z8HSkRkLnAd8MbWP6CqHwBXAS/6PzcNO+XWGOfs0C9jjAmAjWyNMSYAVrbGGBMAK1tjjAmAla0xxgTAytYYYwJgZWuMMQGwsjXGmABY2RpjTACsbI0xJgBWtsYYEwArW2OMCYCVrTHGBMDK1hhjAmBla4wxAbCyNcaYAFjZGmNMAKxsjTEmAFa2xhgTACtbY4wJgJWtMcYEwMrWGGMCYGVrjDEBsLI1xpgAWNkaY0wArGyNMSYAVrbGGBMAK1tjjAmAla0xxgTAytYYYwJgZWuMMQGwsjXGmABY2RpjTACsbI0xJgD/H5RdVdEGaBf1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 410.4x410.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Vlerësuesi 3'].value_counts().plot.pie(autopct='%1.1f%%',figsize=(5.7,5.7),\n",
    "                                                     labels=['Negative', 'Neutrale', 'Pozitive'],\n",
    "                                                    colors = ( \"#E74C3C\", \"#F1C40F\", \"#2ECC71\"))\n",
    "plt.title(\"Arbena Musa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vlerësime të njëjta Vlerësuesi 1 me Vlerësuesin 2 -> 1028 nga 1435\n",
      "Vlerësime të njëjta Vlerësuesi 1 me Vlerësuesin 3 -> 937 nga 1435\n",
      "Vlerësime të njejtë Vlerësuesi 2 me Vlerësuesin 3 -> 914 nga 1435\n"
     ]
    }
   ],
   "source": [
    "print(\"Vlerësime të njëjta Vlerësuesi 1 me Vlerësuesin 2 ->\",\n",
    "     (data['Vlerësuesi 1'] == data['Vlerësuesi 2']).sum(),\n",
    "      \"nga\",\n",
    "      data.shape[0])\n",
    "\n",
    "print(\"Vlerësime të njëjta Vlerësuesi 1 me Vlerësuesin 3 ->\",\n",
    "     (data['Vlerësuesi 1'] == data['Vlerësuesi 3']).sum(),\n",
    "      \"nga\",\n",
    "      data.shape[0])\n",
    "\n",
    "print(\"Vlerësime të njejtë Vlerësuesi 2 me Vlerësuesin 3 ->\", \n",
    "      (data['Vlerësuesi 2'] == data['Vlerësuesi 3']).sum(), \n",
    "      \"nga\", \n",
    "      data.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pearson's Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vlerësuesi 1 vs 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson's corroleation në mes Vlerësuesit 1 dhe Vlerësuesit 2 është  0.5327671537760319\n"
     ]
    }
   ],
   "source": [
    "corr, _ = pearsonr(data['Vlerësuesi 1'],\n",
    "                   data['Vlerësuesi 2'])\n",
    "print(\"Pearson's corroleation në mes Vlerësuesit 1 dhe Vlerësuesit 2 është \", corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vlerësuesi 1 vs 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson's corroleation në mes Vlerësuesit 1 dhe Vlerësuesit 3 është  0.4175727496008779\n"
     ]
    }
   ],
   "source": [
    "corr1, _ = pearsonr(data['Vlerësuesi 1'],\n",
    "                   data['Vlerësuesi 3'])\n",
    "print(\"Pearson's corroleation në mes Vlerësuesit 1 dhe Vlerësuesit 3 është \", corr1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vlerësuesi 2 vs 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson's corroleation në mes Vlerësuesit 2 dhe Vlerësuesit 3 është  0.4198326017650549\n"
     ]
    }
   ],
   "source": [
    "corr2, _ = pearsonr(data['Vlerësuesi 2'],\n",
    "                   data['Vlerësuesi 3'])\n",
    "print(\"Pearson's corroleation në mes Vlerësuesit 2 dhe Vlerësuesit 3 është \", corr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spearman's Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vlerësuesi 1 vs 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman's corroleation në mes Vlerësuesit 1 dhe Vlerësuesit 2 është  0.5241949370279608\n"
     ]
    }
   ],
   "source": [
    "corr3, _ = spearmanr(data['Vlerësuesi 1'],\n",
    "                     data['Vlerësuesi 2'])\n",
    "print(\"Spearman's corroleation në mes Vlerësuesit 1 dhe Vlerësuesit 2 është \", corr3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vlerësuesi 1 vs 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman's corroleation në mes Vlerësuesit 1 dhe Vlerësuesit 3 është  0.41167560073069104\n"
     ]
    }
   ],
   "source": [
    "corr4, _ = spearmanr(data['Vlerësuesi 1'],\n",
    "                     data['Vlerësuesi 3'])\n",
    "print(\"Spearman's corroleation në mes Vlerësuesit 1 dhe Vlerësuesit 3 është \", corr4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vlerësuesi 2 vs 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman's corroleation në mes Vlerësuesit 2 dhe Vlerësuesit 3 është  0.4002331868812734\n"
     ]
    }
   ],
   "source": [
    "corr5, _ = spearmanr(data['Vlerësuesi 2'],\n",
    "                     data['Vlerësuesi 3'])\n",
    "print(\"Spearman's corroleation në mes Vlerësuesit 2 dhe Vlerësuesit 3 është \", corr5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Vlerësuesi 1</th>\n",
       "      <th>Vlerësuesi 2</th>\n",
       "      <th>Vlerësuesi 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Vlerësuesi 1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.532767</td>\n",
       "      <td>0.417573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vlerësuesi 2</th>\n",
       "      <td>0.532767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.419833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vlerësuesi 3</th>\n",
       "      <td>0.417573</td>\n",
       "      <td>0.419833</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Vlerësuesi 1  Vlerësuesi 2  Vlerësuesi 3\n",
       "Vlerësuesi 1      1.000000      0.532767      0.417573\n",
       "Vlerësuesi 2      0.532767      1.000000      0.419833\n",
       "Vlerësuesi 3      0.417573      0.419833      1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vleresimet.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.matshow(vleresimet.corr())\n",
    "# plt.xticks(range(len(vleresimet.columns)), vleresimet.columns)\n",
    "# plt.yticks(range(len(vleresimet.columns)), vleresimet.columns)\n",
    "# plt.colorbar()\n",
    "# plt.show()\n",
    "corrMatrix = vleresimet.corr()\n",
    "sns.heatmap(corrMatrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nxjerrja e vleresimit final nga 3 vleresimet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I kthejme ne array vlerat e te tri kolonave\n",
    "x = data['Vlerësuesi 1'].values # type(x) -> <class 'numpy.ndarray'>\n",
    "y = data['Vlerësuesi 2'].values  # type(y) -> <class 'numpy.ndarray'>\n",
    "z = data['Vlerësuesi 3'].values   # type(z) -> <class 'numpy.ndarray'>\n",
    "\n",
    "newValues = [] # type(newValues) -> <class 'list'>\n",
    "for i in range(0, len(x)):\n",
    "    if(x[i] == y[i] or x[i] == z[i]):\n",
    "        newValues.append(x[i])\n",
    "    elif(y[i] == x[i] or y[i] == z[i]):\n",
    "        newValues.append(y[i])\n",
    "    elif(z[i] == x[i] or z[i] == y[i]):\n",
    "        newValues.append(z[i])\n",
    "    else:\n",
    "        newValues.append(0)\n",
    "\n",
    "data['Sentimenti'] = newValues\n",
    "data.to_csv(\"trainset.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataseti me vleresimin final "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset = pd.read_csv(\"trainset.csv\")\n",
    "trainset.drop([#'Id', 'Emri i komentuesit', 'Like', \n",
    "               #'Comment timestamp', 'Post timestamp', \n",
    "               #'URL',\n",
    "              'Vlerësuesi 1', \n",
    "              'Vlerësuesi 2', \n",
    "              'Vlerësuesi 3'], axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Komenti</th>\n",
       "      <th>Sentimenti</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Kjamet</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>So keq sa per fillim</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Gilanin mdoket pe harroni kejt</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Ni pytje per juve a jeni ne dijeni qe personat...</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Komenti  Sentimenti\n",
       "130                                            Kjamet          2.0\n",
       "131                               So keq sa per fillim         1.0\n",
       "132                     Gilanin mdoket pe harroni kejt         0.0\n",
       "133  Ni pytje per juve a jeni ne dijeni qe personat...         2.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset.head(n=134)[130:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentimenti (Vlerësimi final): \n",
      "Vlerësime pozitive: 177\n",
      "Vlerësime negative: 522\n",
      "Vlerësime neutrale: 736\n",
      "Vlerësime totale: 1435\n"
     ]
    }
   ],
   "source": [
    "#Sentimenti\n",
    "print(\"Sentimenti (Vlerësimi final): \")\n",
    "print(\"Vlerësime pozitive:\", (trainset['Sentimenti'] == 1).sum())\n",
    "print(\"Vlerësime negative:\", (trainset['Sentimenti'] == 2).sum())\n",
    "print(\"Vlerësime neutrale:\", (trainset['Sentimenti'] == 0).sum())\n",
    "print(\"Vlerësime totale:\", (trainset['Sentimenti']).count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sentimenti')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 410.4x410.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainset['Sentimenti'].value_counts().plot.pie(autopct='%1.1f%%',figsize=(5.7,5.7),\n",
    "                                                     labels=['Neutrale', 'Negative', 'Pozitive'],\n",
    "                                                    colors = ( \"#F1C40F\", \"#E74C3C\", \"#2ECC71\"))\n",
    "plt.title('Sentimenti')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23bbd329d00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7U05mcLTLt4B3z0Cey4CtwE8YtPubgf0Y7Dj8Zve879Dy7+6y3wmsn3DWX2Gw2nkrcHP3OHkW8wJHAl/tst4G/Hk3PnNZF+V+GU/uLJ7JrAy2u9/SPW7f9u9ohvO+GNjY/Sz8M/C8Wc3aff+ewP8APzs0NrN5tz28xIQkNW6eNg1JknpgEUhS4ywCSWqcRSBJjbMIJKlxFoE0Bd1VKfecdg4JvEOZNBXdmcjrquqBaWeRXCOQtiPJG5Lc2t0X4aNJDklyTTd2TZJf6Ja7KMlrht73g+75ZUm+OHQ9/Usy8DbgAODaJNdO508nPWm3aQeQZlGSFzI46/OEqnogyb4Mro1/cVV9JMnvA+cCv7nERx0FvJDBNWSu7z7v3CRnAS93jUCzwDUCabQTgU9u+0VdVd9ncE+MS7v5H2Vw2Y6l3FhVW6rqCQaX9VjoIav0jFgE0mhh6UsCb5v/GN2/pe7ifiuHlvnx0PTjuBauGWQRSKNdA/xOkv1gcE9f4D8YXGEU4PeAL3XT9wC/1E2fwuD2mkt5mMEtQ6Wp838n0ghVdXuS9wDXJXmcwdVQ3wZcmOQdDO6cdVq3+D8An05yI4MC+eEYX7EB+FySrVX18uX/E0jj8/BRSWqcm4YkqXEWgSQ1ziKQpMZZBJLUOItAkhpnEUhS4ywCSWrc/wN2IqbHzKvKGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(y='Sentimenti', data=trainset, palette=['#03A0FF', '#00FF00', '#FF0000'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1435, 2)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset.shape"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}