act_report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize some part of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('twitter_archive_master.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insight 1 :\n",
    "○ The 'golden_retriever' dog type is the most common dog that has been rated.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden_retriever             556\n",
       "Labrador_retriever           380\n",
       "Pembroke                     352\n",
       "Chihuahua                    316\n",
       "pug                          216\n",
       "chow                         164\n",
       "Samoyed                      160\n",
       "Pomeranian                   152\n",
       "toy_poodle                   152\n",
       "malamute                     116\n",
       "cocker_spaniel               108\n",
       "French_bulldog               100\n",
       "Chesapeake_Bay_retriever      92\n",
       "miniature_pinscher            88\n",
       "seat_belt                     84\n",
       "German_shepherd               80\n",
       "Siberian_husky                80\n",
       "Staffordshire_bullterrier     76\n",
       "Eskimo_dog                    72\n",
       "Shetland_sheepdog             72\n",
       "teddy                         72\n",
       "Maltese_dog                   72\n",
       "Shih-Tzu                      68\n",
       "beagle                        68\n",
       "Cardigan                      68\n",
       "Rottweiler                    68\n",
       "Italian_greyhound             64\n",
       "kuvasz                        64\n",
       "Lakeland_terrier              64\n",
       "web_site                      60\n",
       "                            ... \n",
       "bonnet                         4\n",
       "bib                            4\n",
       "ping-pong_ball                 4\n",
       "shield                         4\n",
       "revolver                       4\n",
       "crash_helmet                   4\n",
       "fire_engine                    4\n",
       "school_bus                     4\n",
       "lawn_mower                     4\n",
       "nail                           4\n",
       "sliding_door                   4\n",
       "lynx                           4\n",
       "bearskin                       4\n",
       "studio_couch                   4\n",
       "carton                         4\n",
       "hay                            4\n",
       "mortarboard                    4\n",
       "dining_table                   4\n",
       "rotisserie                     4\n",
       "bighorn                        4\n",
       "long-horned_beetle             4\n",
       "picket_fence                   4\n",
       "bookshop                       4\n",
       "cup                            4\n",
       "espresso                       4\n",
       "marmot                         4\n",
       "earthstar                      4\n",
       "bison                          4\n",
       "leopard                        4\n",
       "four-poster                    4\n",
       "Name: Prediction1, Length: 374, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Prediction1'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### as we can see that the golden_retriever dog type is the most dog that has been rated in the whole dataset were in the other had there is a lot of dogs types that has just been rated 4 times such as like (four-poster, leopard, bison, earthstar, marmot) , where is sixty dogs has been classified as a 'web_site' :P which shows some flaws in the neural network or it could be just missy data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insight 2 : \n",
    "○ there is a correlation between the number of tweets and the number of favourites it gets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'scatter plot of retweets and scatter plot')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='favorites',y='retweets', alpha = 0.5)\n",
    "plt.xlabel('the number of favorites')\n",
    "plt.ylabel('the number of retweets')\n",
    "plt.title('scatter plot of retweets and scatter plot')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### as we can see there is a correlation between the number of retweets and the favoirits for every tweet which shows how humans could react more positevly for the tweets or posts that they likes better ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insight 3 : \n",
    "○ The overall average for ratings is 12/10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      1796\n",
       "10      1676\n",
       "11      1584\n",
       "13      1044\n",
       "9        604\n",
       "8        380\n",
       "7        208\n",
       "14       140\n",
       "5        132\n",
       "6        128\n",
       "3         76\n",
       "4         64\n",
       "2         36\n",
       "1         20\n",
       "0          8\n",
       "45         4\n",
       "84         4\n",
       "75         4\n",
       "27         4\n",
       "50         4\n",
       "24         4\n",
       "44         4\n",
       "60         4\n",
       "80         4\n",
       "88         4\n",
       "121        4\n",
       "144        4\n",
       "204        4\n",
       "420        4\n",
       "1776       4\n",
       "26         4\n",
       "165        4\n",
       "99         4\n",
       "143        4\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rating_numerator'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### it seems like that the most rate has been given to dogs is 12/10 and also that 10/10 is the most second rate has been given , and we can see that 14/10 has been given for just 140 types of dogs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We noticed that the most 10 dog types that has been rated are: \n",
    "golden_retriever ,\n",
    "Labrador_retriever, \n",
    "Pembroke ,\n",
    "Chihuahua,\n",
    "pug,\n",
    "chow,\n",
    "Samoyed,\n",
    "Pomeranian,\n",
    "toy_poodle,\n",
    "malamute.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also noticed that when the facourites are going higher the retweets getting higher too... that might be because people who liked a certain tweet they use to engage more with that tweet. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We noticed that the most rate has been put for a dog is 12 , and also that there is exist rating with 'less that 10'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}