act_report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality Issues\n",
    "\n",
    "**archive_df:**\n",
    "- Missing data in the following columns: in_reply_to_status_id, in_reply_to_user_id, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls [Solved]\n",
    "- This dataset includes retweets, which means there is duplicated data [Solved]\n",
    "- Timestamp and retweeted_status_timestamp is an object [Solved]\n",
    "- The source column still has the HTML tags [Solved]\n",
    "- Dogs name have 'None', or 'a', or 'an.' and some more lower case words as names [Solved]\n",
    "- The rating_numerator and rating_denominator is highly positive skewed [Solved]\n",
    "- Multiple dog stages occurs such as 'doggo puppo', 'doggo pupper', 'doggo floofer' [Solved]\n",
    "\n",
    "**image_pred_df**:\n",
    "\n",
    "- dog breeds are not consistently in p1,p2,p3 columns [Solved]\n",
    "\n",
    "**tweet_counts_df:**\n",
    "- Missing data [Solved]\n",
    "- tweet_id is an object [Solved]\n",
    "\n",
    "\n",
    "## Tidiness Issues\n",
    "\n",
    "**archive_df:**\n",
    "- The variable for the dog's stage (dogoo, floofer, pupper, puppo) is spread in different columns [Solved]\n",
    "\n",
    "**image_pred_df**:\n",
    "\n",
    "- This data set is part of the same observational unit as the data in the archive_df [Solved]\n",
    "\n",
    "**tweet_counts_df:**\n",
    "\n",
    "- This data set is also part of the same observational unit as the data in the archive_df [Solved]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Findings of the analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The pred_breed column is created based on the the confidence level of minimum 20% and 'p1_dog', 'p2_dog' and 'p3_dog' statements\n",
    "2. Based on dog types: doggo, floofer, pupper, puppo, 'doggo, puppo', 'doggo, pupper', 'doggo, floofer', only one categorical column is created named as 'stage'\n",
    "3. tweet_id is set as object type as it is not going to use for calculation.\n",
    "4. A Master dataframe is created using archive_clean, image_pred_clean, and tweet_counts_clean dataframes\n",
    "5. Dog Names Issue got rectified\n",
    "7. Inconsistency in pred_breed got removed\n",
    "8. All retweets get deleted to get unique tweets\n",
    "9. The columns such as in_reply_to_status_id, in_reply_to_user_id, retweeted_status_id, retweeted_status_user_id, and retweeted_status_timestamp is removed which is not needed\n",
    "10. Timestamp format got corrected to datetime format\n",
    "11. Extra HTML tags from source column get refracted\n",
    "12. Dog ratings get standardized for denom of 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some more finding and Analyzing, and Visualizing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Which rating was given to most of the dogs?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47830b7438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['rating_numerator'].value_counts().sort_index().plot('bar')\n",
    "plt.title ('Rating Numerator Distribution', size=15)\n",
    "plt.xlabel('Rating Numerator')\n",
    "plt.ylabel('Number of Ratings')\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the dogs get rating_numerator in between 10-13\n",
    "\n",
    "There is very few dogs even 1 or 2 with rating 0 and numerator rating as float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### What are the 10 most frequent dog names?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4782fb9d68>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['name'].value_counts()[0:10].sort_values(ascending=False).plot(kind = 'bar')\n",
    "plt.ylabel('Number of Dogs')\n",
    "plt.title('Top 10 frequent dog names', size=15)\n",
    "plt.xlabel('Dog Names')\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the dogs are of names: Charlie, Cooper, liver, Penny, Tucker, Lucy, Sadie, Winston, Daisy, Toby\n",
    "\n",
    "Also, check the count below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Oliver     10\n",
       "Cooper     10\n",
       "Charlie    10\n",
       "Penny       9\n",
       "Lucy        9\n",
       "Tucker      9\n",
       "Winston     8\n",
       "Sadie       8\n",
       "Daisy       7\n",
       "Lola        7\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 10 frequent dog names\n",
    "df['name'].value_counts()[0:10].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### What are the 10 most frequent predicted dog breeds?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4782fc45c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['pred_breed'].value_counts()[0:10].sort_values(ascending=False).plot(kind = 'bar')\n",
    "plt.ylabel('Number of Breed Prediction')\n",
    "plt.title('Top 10 frequent dog breeds', size=15)\n",
    "plt.xlabel('Dog Breed')\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the dogs have golden retriever, labrador retriever as breed which all are rated\n",
    "\n",
    "Please check the top 10 rated dog breeds below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden retriever      137\n",
       "labrador retriever     91\n",
       "pembroke               88\n",
       "chihuahua              74\n",
       "pug                    53\n",
       "samoyed                39\n",
       "chow                   39\n",
       "pomeranian             37\n",
       "toy poodle             36\n",
       "malamute               29\n",
       "Name: pred_breed, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 10 frequent dog breeds\n",
    "df['pred_breed'].value_counts()[0:10].sort_values(ascending=False)"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}