research.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[17], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumpy version: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39m__version__\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPandas version: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpd\u001b[38;5;241m.\u001b[39m__version__\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Rajan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\__init__.py:62\u001b[0m\n\u001b[0;32m     59\u001b[0m \u001b[38;5;66;03m# let init-time option registration happen\u001b[39;00m\n\u001b[0;32m     60\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig_init\u001b[39;00m  \u001b[38;5;66;03m# pyright: ignore[reportUnusedImport] # noqa: F401\u001b[39;00m\n\u001b[1;32m---> 62\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     63\u001b[0m     \u001b[38;5;66;03m# dtype\u001b[39;00m\n\u001b[0;32m     64\u001b[0m     ArrowDtype,\n\u001b[0;32m     65\u001b[0m     Int8Dtype,\n\u001b[0;32m     66\u001b[0m     Int16Dtype,\n\u001b[0;32m     67\u001b[0m     Int32Dtype,\n\u001b[0;32m     68\u001b[0m     Int64Dtype,\n\u001b[0;32m     69\u001b[0m     UInt8Dtype,\n\u001b[0;32m     70\u001b[0m     UInt16Dtype,\n\u001b[0;32m     71\u001b[0m     UInt32Dtype,\n\u001b[0;32m     72\u001b[0m     UInt64Dtype,\n\u001b[0;32m     73\u001b[0m     Float32Dtype,\n\u001b[0;32m     74\u001b[0m     Float64Dtype,\n\u001b[0;32m     75\u001b[0m     CategoricalDtype,\n\u001b[0;32m     76\u001b[0m     PeriodDtype,\n\u001b[0;32m     77\u001b[0m     IntervalDtype,\n\u001b[0;32m     78\u001b[0m     DatetimeTZDtype,\n\u001b[0;32m     79\u001b[0m     StringDtype,\n\u001b[0;32m     80\u001b[0m     BooleanDtype,\n\u001b[0;32m     81\u001b[0m     \u001b[38;5;66;03m# missing\u001b[39;00m\n\u001b[0;32m     82\u001b[0m     NA,\n\u001b[0;32m     83\u001b[0m     isna,\n\u001b[0;32m     84\u001b[0m     isnull,\n\u001b[0;32m     85\u001b[0m     notna,\n\u001b[0;32m     86\u001b[0m     notnull,\n\u001b[0;32m     87\u001b[0m     \u001b[38;5;66;03m# indexes\u001b[39;00m\n\u001b[0;32m     88\u001b[0m     Index,\n\u001b[0;32m     89\u001b[0m     CategoricalIndex,\n\u001b[0;32m     90\u001b[0m     RangeIndex,\n\u001b[0;32m     91\u001b[0m     MultiIndex,\n\u001b[0;32m     92\u001b[0m     IntervalIndex,\n\u001b[0;32m     93\u001b[0m     TimedeltaIndex,\n\u001b[0;32m     94\u001b[0m     DatetimeIndex,\n\u001b[0;32m     95\u001b[0m     PeriodIndex,\n\u001b[0;32m     96\u001b[0m     IndexSlice,\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;66;03m# tseries\u001b[39;00m\n\u001b[0;32m     98\u001b[0m     NaT,\n\u001b[0;32m     99\u001b[0m     Period,\n\u001b[0;32m    100\u001b[0m     period_range,\n\u001b[0;32m    101\u001b[0m     Timedelta,\n\u001b[0;32m    102\u001b[0m     timedelta_range,\n\u001b[0;32m    103\u001b[0m     Timestamp,\n\u001b[0;32m    104\u001b[0m     date_range,\n\u001b[0;32m    105\u001b[0m     bdate_range,\n\u001b[0;32m    106\u001b[0m     Interval,\n\u001b[0;32m    107\u001b[0m     interval_range,\n\u001b[0;32m    108\u001b[0m     DateOffset,\n\u001b[0;32m    109\u001b[0m     \u001b[38;5;66;03m# conversion\u001b[39;00m\n\u001b[0;32m    110\u001b[0m     to_numeric,\n\u001b[0;32m    111\u001b[0m     to_datetime,\n\u001b[0;32m    112\u001b[0m     to_timedelta,\n\u001b[0;32m    113\u001b[0m     \u001b[38;5;66;03m# misc\u001b[39;00m\n\u001b[0;32m    114\u001b[0m     Flags,\n\u001b[0;32m    115\u001b[0m     Grouper,\n\u001b[0;32m    116\u001b[0m     factorize,\n\u001b[0;32m    117\u001b[0m     unique,\n\u001b[0;32m    118\u001b[0m     value_counts,\n\u001b[0;32m    119\u001b[0m     NamedAgg,\n\u001b[0;32m    120\u001b[0m     array,\n\u001b[0;32m    121\u001b[0m     Categorical,\n\u001b[0;32m    122\u001b[0m     set_eng_float_format,\n\u001b[0;32m    123\u001b[0m     Series,\n\u001b[0;32m    124\u001b[0m     DataFrame,\n\u001b[0;32m    125\u001b[0m )\n\u001b[0;32m    127\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SparseDtype\n\u001b[0;32m    129\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtseries\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m infer_freq\n",
      "File \u001b[1;32mc:\\Users\\Rajan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\api.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_libs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m      2\u001b[0m     NaT,\n\u001b[0;32m      3\u001b[0m     Period,\n\u001b[0;32m      4\u001b[0m     Timedelta,\n\u001b[0;32m      5\u001b[0m     Timestamp,\n\u001b[0;32m      6\u001b[0m )\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_libs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmissing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m NA\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     10\u001b[0m     ArrowDtype,\n\u001b[0;32m     11\u001b[0m     CategoricalDtype,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     14\u001b[0m     PeriodDtype,\n\u001b[0;32m     15\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\Rajan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\_libs\\__init__.py:18\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_libs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpandas_parser\u001b[39;00m  \u001b[38;5;66;03m# isort: skip # type: ignore[reportUnusedImport]\u001b[39;00m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_libs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpandas_datetime\u001b[39;00m  \u001b[38;5;66;03m# noqa: F401 # isort: skip # type: ignore[reportUnusedImport]\u001b[39;00m\n\u001b[1;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_libs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterval\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Interval\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_libs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtslibs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     20\u001b[0m     NaT,\n\u001b[0;32m     21\u001b[0m     NaTType,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     26\u001b[0m     iNaT,\n\u001b[0;32m     27\u001b[0m )\n",
      "File \u001b[1;32minterval.pyx:1\u001b[0m, in \u001b[0;36minit pandas._libs.interval\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "print(f\"Numpy version: {np.__version__}\")\n",
    "print(f\"Pandas version: {pd.__version__}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSimulated Dataset.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdesired_encoding\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"Simulated Dataset.csv\", encoding=\"desired_encoding\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tweet_id          0\n",
      "topic             0\n",
      "likes             0\n",
      "retweets          0\n",
      "replies           0\n",
      "virality_score    0\n",
      "temporal_decay    0\n",
      "Engagement        0\n",
      "Followers         0\n",
      "Timestamp         0\n",
      "user_activity     0\n",
      "location          0\n",
      "content           0\n",
      "hashtags          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Drop rows with missing values (if any)\n",
    "data = data.dropna()\n",
    "\n",
    "# Verify\n",
    "print(data.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTimestamp\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mto_datetime(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTimestamp\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Extract useful features from the timestamp\u001b[39;00m\n\u001b[0;32m      4\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhour\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTimestamp\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mdt\u001b[38;5;241m.\u001b[39mhour\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "data['Timestamp'] = pd.to_datetime(data['Timestamp'])\n",
    "\n",
    "# Extract useful features from the timestamp\n",
    "data['hour'] = data['Timestamp'].dt.hour\n",
    "data['day'] = data['Timestamp'].dt.day\n",
    "data['weekday'] = data['Timestamp'].dt.weekday\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "\n",
    "# Compute sentiment polarity and subjectivity\n",
    "data['sentiment_polarity'] = data['content'].apply(lambda x: TextBlob(x).sentiment.polarity)\n",
    "data['sentiment_subjectivity'] = data['content'].apply(lambda x: TextBlob(x).sentiment.subjectivity)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             likes    retweets      replies  virality_score  temporal_decay  \\\n",
      "count  5000.000000  5000.00000  5000.000000     5000.000000     5000.000000   \n",
      "mean   4970.342200  2527.46040  1513.713200        0.491934        0.496652   \n",
      "min       0.000000     1.00000     0.000000        0.000415        0.010008   \n",
      "25%    2449.750000  1275.00000   758.750000        0.237106        0.254187   \n",
      "50%    4911.000000  2521.00000  1514.500000        0.489283        0.494247   \n",
      "75%    7495.750000  3805.50000  2266.250000        0.739385        0.741495   \n",
      "max    9998.000000  5000.00000  3000.000000        0.999834        0.989927   \n",
      "std    2895.556981  1453.25071   859.488381        0.288254        0.281650   \n",
      "\n",
      "         Engagement     Followers                   Timestamp  user_activity  \\\n",
      "count   5000.000000    5000.00000                        5000     5000.00000   \n",
      "mean    8559.849200  504860.45200  2023-06-30 22:40:12.492000       50.65620   \n",
      "min      228.000000     524.00000         2023-01-01 00:56:00        1.00000   \n",
      "25%     6004.250000  249630.25000         2023-03-30 06:59:30       26.00000   \n",
      "50%     8575.500000  506025.50000         2023-06-30 01:02:30       51.00000   \n",
      "75%    11099.250000  763720.00000         2023-10-01 04:38:15       76.00000   \n",
      "max    16664.000000  999957.00000         2023-12-31 23:00:00      100.00000   \n",
      "std     3276.500375  294066.64375                         NaN       28.64556   \n",
      "\n",
      "              hour          day      weekday  sentiment_polarity  \\\n",
      "count  5000.000000  5000.000000  5000.000000         5000.000000   \n",
      "mean     11.496600    15.619400     3.004200            0.107125   \n",
      "min       0.000000     1.000000     0.000000           -0.100000   \n",
      "25%       5.000000     8.000000     1.000000            0.000000   \n",
      "50%      12.000000    15.000000     3.000000            0.000000   \n",
      "75%      17.000000    23.000000     5.000000            0.136364   \n",
      "max      23.000000    31.000000     6.000000            0.625000   \n",
      "std       6.899907     8.808908     2.001245            0.182764   \n",
      "\n",
      "       sentiment_subjectivity  \n",
      "count             5000.000000  \n",
      "mean                 0.218901  \n",
      "min                  0.000000  \n",
      "25%                  0.000000  \n",
      "50%                  0.125000  \n",
      "75%                  0.454545  \n",
      "max                  0.900000  \n",
      "std                  0.249703  \n"
     ]
    }
   ],
   "source": [
    "print(data.describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Histogram for likes\n",
    "data['likes'].hist(bins=50)\n",
    "plt.title('Distribution of Likes')\n",
    "plt.show()\n",
    "\n",
    "# Histogram for retweets\n",
    "data['retweets'].hist(bins=50)\n",
    "plt.title('Distribution of Retweets')\n",
    "plt.show()\n",
    "\n",
    "# Add similar code for replies and virality_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a 2x2 subplot for histograms\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
    "\n",
    "# Plot histogram for likes\n",
    "axes[0, 0].hist(data['like'], bins=50, edgecolor='black', alpha=0.7)\n",
    "axes[0, 0].set_title('Distribution of Likes')\n",
    "axes[0, 0].set_xlabel('Likes')\n",
    "axes[0, 0].set_ylabel('Frequency')\n",
    "\n",
    "# Plot histogram for retweets\n",
    "axes[0, 1].hist(data['retweets'], bins=50, edgecolor='black', alpha=0.7)\n",
    "axes[0, 1].set_title('Distribution of Retweets')\n",
    "axes[0, 1].set_xlabel('Retweets')\n",
    "axes[0, 1].set_ylabel('Frequency')\n",
    "\n",
    "# Plot histogram for replies\n",
    "axes[1, 0].hist(data['replies'], bins=50, edgecolor='black', alpha=0.7)\n",
    "axes[1, 0].set_title('Distribution of Replies')\n",
    "axes[1, 0].set_xlabel('Replies')\n",
    "axes[1, 0].set_ylabel('Frequency')\n",
    "\n",
    "# Plot histogram for virality_score\n",
    "axes[1, 1].hist(data['virality_score'], bins=50, edgecolor='black', alpha=0.7)\n",
    "axes[1, 1].set_title('Distribution of Virality Score')\n",
    "axes[1, 1].set_xlabel('Virality Score')\n",
    "axes[1, 1].set_ylabel('Frequency')\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Correlation matrix\n",
    "corr_data = data[['Engagement','likes','replies','Followers','virality_score','retweets','temporal_decay','user_activity']]\n",
    "corr_matrix = corr_data.corr()\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from datetime import datetime\n",
    "\n",
    "# Assuming today's date for temporal decay calculation\n",
    "current_date = datetime.now()\n",
    "\n",
    "# Calculate temporal decay in days\n",
    "data['temporal_decay_days'] = (current_date - data['Timestamp']).dt.days\n",
    "\n",
    "# Normalize decay\n",
    "data['temporal_decay_normalized'] = 1 / (1 + np.exp(data['temporal_decay_days']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['engagement_rate'] = (data['likes'] + data['retweets'] + data['replies']) / data['Followers']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Define feature set (X) and target (y)\n",
    "features = ['likes', 'retweets', 'replies', 'sentiment_polarity', 'temporal_decay_normalized', 'Followers', 'user_activity']\n",
    "X = data[features]\n",
    "y = data['virality_score']\n",
    "\n",
    "# Split into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error: 0.29536125411396275\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Rajan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Train model\n",
    "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate\n",
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "print(f'Root Mean Squared Error: {rmse}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = model.feature_importances_\n",
    "sns.barplot(x=importances, y=features)\n",
    "plt.title('Feature Importances')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=data['sentiment_polarity'], y=data['virality_score'])\n",
    "plt.title('Sentiment vs Virality')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('#Research', 323), (' #Research', 311), ('#SeriesFinale', 185), (' #Scholarships', 177), (' #Elections', 177), ('#Education', 176), (' #StockMarket', 176), (' #Economy', 175), (' #Learning', 175), ('#Adventure', 171)]\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "# Flatten hashtags list\n",
    "hashtags = [hashtag for sublist in data['hashtags'].str.split(',').tolist() for hashtag in sublist]\n",
    "counter = Counter(hashtags)\n",
    "\n",
    "# Top 10 hashtags\n",
    "top_hashtags = counter.most_common(10)\n",
    "print(top_hashtags)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    likes  retweets  replies  virality_score  engagement_state\n",
      "0  6683.0    2174.0   2514.0        0.552374                 3\n",
      "1  5459.0    4947.0   2004.0        0.192285                 3\n",
      "2  3599.0    2719.0   1767.0        0.396164                 3\n",
      "3  4238.0    2830.0   1150.0        0.416113                 3\n",
      "4  1846.0    4503.0   2669.0        0.132136                 3\n"
     ]
    }
   ],
   "source": [
    "# Define engagement states\n",
    "def classify_state(row):\n",
    "    total_engagement = row['likes'] + row['retweets'] + row['replies']\n",
    "    if total_engagement == 0:\n",
    "        return 0\n",
    "    elif 1 <= total_engagement <= 50:\n",
    "        return 1\n",
    "    elif 51 <= total_engagement <= 500:\n",
    "        return 2\n",
    "    elif total_engagement > 500:\n",
    "        return 3 if row['virality_score'] < 0.8 else 4\n",
    "\n",
    "# Apply state classification\n",
    "data['engagement_state'] = data.apply(classify_state, axis=1)\n",
    "\n",
    "# Display the first few rows\n",
    "print(data[['likes', 'retweets', 'replies', 'virality_score', 'engagement_state']].head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transition Matrix:\n",
      "[[           nan            nan            nan            nan\n",
      "             nan]\n",
      " [           nan            nan            nan            nan\n",
      "             nan]\n",
      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  1.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.47892910e-04 8.04412494e-01\n",
      "  1.95339613e-01]\n",
      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 8.18464730e-01\n",
      "  1.81535270e-01]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rajan\\AppData\\Local\\Temp\\ipykernel_28232\\182380858.py:17: RuntimeWarning: invalid value encountered in divide\n",
      "  transition_matrix = transition_matrix / transition_matrix.sum(axis=1, keepdims=True)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Generate state transitions\n",
    "transitions = data['engagement_state'].values\n",
    "\n",
    "# Initialize transition matrix\n",
    "num_states = 5  # States 0–4\n",
    "transition_matrix = np.zeros((num_states, num_states))\n",
    "\n",
    "# Compute transition probabilities\n",
    "for i in range(len(transitions) - 1):\n",
    "    current_state = transitions[i]\n",
    "    next_state = transitions[i + 1]\n",
    "    transition_matrix[current_state, next_state] += 1\n",
    "\n",
    "# Normalize to probabilities\n",
    "transition_matrix = transition_matrix / transition_matrix.sum(axis=1, keepdims=True)\n",
    "\n",
    "# Display the transition matrix\n",
    "print(\"Transition Matrix:\")\n",
    "print(transition_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               timestamp  time_diff\n",
      "1975 2023-01-01 00:56:00     4500.0\n",
      "3994 2023-01-01 06:08:00    18720.0\n",
      "625  2023-01-01 07:46:00     5880.0\n",
      "636  2023-01-01 09:08:00     4920.0\n",
      "4255 2023-01-01 13:05:00    14220.0\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "# Convert Timestamp to datetime\n",
    "data['timestamp'] = pd.to_datetime(data['Timestamp'])\n",
    "\n",
    "# Calculate time intervals (in seconds) between engagements\n",
    "data = data.sort_values(by='timestamp')\n",
    "data['time_diff'] = data['timestamp'].diff().dt.total_seconds()\n",
    "\n",
    "# Fill missing time intervals with the median\n",
    "median_time_diff = data['time_diff'].median()\n",
    "data['time_diff'].fillna(median_time_diff, inplace=True)\n",
    "\n",
    "# Inspect time intervals\n",
    "print(data[['timestamp', 'time_diff']].head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulated Event Times: [  2.83563565   4.06038533   5.7687842    5.91039387   6.74259424\n",
      "   7.07064449   7.33055378   7.46519621   7.65816018   8.22051058\n",
      "   8.22378743   8.67408225   9.33194336   9.51074673   9.82199798\n",
      "   9.90679651   9.96939593  10.11775042  10.13007624  10.59295055\n",
      "  11.24947877  11.87524909  12.09657356  12.1822433   12.19765523\n",
      "  12.65373438  13.23922087  13.90080152  14.05849347  14.54440184\n",
      "  15.68476771  16.41107201  16.79159392  18.52769408  20.00691009\n",
      "  20.18379152  21.01730646  21.66509006  22.30946819  25.66129304\n",
      "  26.09643688  26.13910481  26.15982988  26.86693781  28.30570956\n",
      "  28.66435308  29.31113789  31.00438306  31.17545303  31.63022623\n",
      "  32.12520513  32.24406248  32.47499888  32.91575253  33.01558967\n",
      "  33.61384576  34.15250587  34.54863232  35.66919838  35.76033842\n",
      "  35.86117327  36.20920746  36.44418016  36.73908646  37.11543923\n",
      "  38.75596005  39.35278439  40.68241442  41.58539727  41.98631434\n",
      "  42.42918487  42.62898186  42.68525946  43.34043453  44.01080816\n",
      "  44.31812726  45.24373278  45.61944433  45.75481943  46.59230733\n",
      "  47.13007464  47.90368129  48.27194988  49.63353581  50.35617074\n",
      "  51.48463711  51.55066395  52.26808952  52.46942465  52.59859061\n",
      "  52.73158168  52.80599886  52.89168921  52.91217459  53.05136899\n",
      "  53.18399657  54.4412402   55.03949238  55.13349933  55.48812018\n",
      "  55.5332011   55.55883197  55.97974691  56.58369149  56.58484973\n",
      "  56.77802504  57.00124811  57.25604586  57.30993141  57.75417007\n",
      "  58.27979261  58.49333793  58.77212994  59.21703565  59.63213589\n",
      "  59.77143323  60.80952808  60.8403253   61.08846957  61.27929987\n",
      "  62.60831321  62.91989071  64.03815456  64.28581458  64.84523343\n",
      "  65.46575851  65.46824052  65.73337501  66.58339115  66.62761789\n",
      "  67.66379703  67.91139099  68.12126594  68.30411101  68.73548505\n",
      "  68.88277573  69.10467905  69.48790582  69.72834452  69.89303136\n",
      "  70.0339834   71.4802692   71.76576815  72.85483445  73.79071201\n",
      "  75.04160479  75.15280641  75.21854407  75.62240896  75.73414454\n",
      "  76.64360734  76.83183677  76.86771894  77.2134453   77.53396864\n",
      "  78.37028868  79.11043198  79.30417743  79.59059139  80.22705038\n",
      "  80.38187453  81.14670947  81.36644615  82.22971274  82.60674122\n",
      "  83.01785045  83.06173048  83.15148493  83.81455987  84.32398883\n",
      "  84.78655705  85.50867042  88.70104937  89.48326849  90.2041871\n",
      "  90.78499505  91.2483264   91.3687497   92.43706158  92.70622711\n",
      "  93.20047314  94.17641016  94.55828961  94.85358551  95.42418477\n",
      "  95.65506177  96.18936789  97.470931    97.69158661  98.04053166\n",
      "  99.73563092 100.76797814]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Parameters\n",
    "mu = 0.1  # Base intensity\n",
    "alpha = 0.5  # Excitation factor\n",
    "beta = 0.8  # Decay rate\n",
    "T = 100  # Total time for the process\n",
    "\n",
    "# Initialize simulation\n",
    "event_times = []\n",
    "current_time = 0\n",
    "\n",
    "while current_time < T:\n",
    "    # Calculate the current intensity\n",
    "    if len(event_times) == 0:\n",
    "        intensity = mu\n",
    "    else:\n",
    "        intensity = mu + np.sum(alpha * np.exp(-beta * (current_time - np.array(event_times))))\n",
    "    \n",
    "    # Generate the next event time\n",
    "    time_to_next_event = np.random.exponential(scale=1 / intensity)\n",
    "    current_time += time_to_next_event\n",
    "    \n",
    "    # Accept or reject the event based on intensity\n",
    "    if np.random.uniform(0, 1) < (intensity / (mu + alpha)):\n",
    "        event_times.append(current_time)\n",
    "\n",
    "# Convert to numpy array for easier processing\n",
    "event_times = np.array(event_times)\n",
    "\n",
    "# Display the simulated event times\n",
    "print(\"Simulated Event Times:\", event_times)\n",
    "\n",
    "# Plot the event intensity over time\n",
    "time_grid = np.linspace(0, T, 5000)\n",
    "intensities = [mu + np.sum(alpha * np.exp(-beta * (t - event_times[event_times < t]))) for t in time_grid]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(time_grid, intensities, label='Intensity Function')\n",
    "plt.scatter(event_times, [0] * len(event_times), color='red', label='Event Times')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Intensity')\n",
    "plt.title('Hawkes Process Intensity')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated Poisson Rate (λ): 1.4290818559651675\n"
     ]
    }
   ],
   "source": [
    "# Calculate total engagement (likes, retweets, replies) per unit time\n",
    "time_duration = (data['timestamp'].max() - data['timestamp'].min()).total_seconds()  # Total time span in seconds\n",
    "total_events = data[['likes', 'retweets', 'replies']].sum().sum()  # Total engagement events\n",
    "lambda_rate = total_events / time_duration  # Engagement rate per second\n",
    "\n",
    "print(\"Estimated Poisson Rate (λ):\", lambda_rate)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulated Engagement Events: [1 4 2 2 5 2 3 3 1 2]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import poisson\n",
    "\n",
    "# Simulate engagement events\n",
    "simulated_events = poisson.rvs(mu=lambda_rate, size=5000)\n",
    "\n",
    "# Inspect simulated events\n",
    "print(\"Simulated Engagement Events:\", simulated_events[:10])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated Parameters:\n",
      "Baseline Intensity (μ): 1.1262246495040338\n",
      "Excitation Factor (α): 0.13927469987262026\n",
      "Decay Rate (β): 0.32752872857992016\n"
     ]
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "def hawkes_log_likelihood(params, event_times, T):\n",
    "    mu, alpha, beta = params\n",
    "    log_likelihood = 0\n",
    "\n",
    "    # Compute log-likelihood for observed events\n",
    "    for i, t in enumerate(event_times):\n",
    "        intensity = mu + np.sum(alpha * np.exp(-beta * (t - event_times[:i])))\n",
    "        log_likelihood += np.log(intensity)\n",
    "\n",
    "    # Compute integral term (Riemann sum approximation)\n",
    "    time_grid = np.linspace(0, T, 1000)\n",
    "    intensity_grid = [\n",
    "        mu + np.sum(alpha * np.exp(-beta * (t - event_times[event_times < t]))) for t in time_grid\n",
    "    ]\n",
    "    integral = np.sum(intensity_grid) * (T / len(time_grid))\n",
    "\n",
    "    return -(log_likelihood - integral)  # Negative because we minimize\n",
    "\n",
    "# Initial guesses for mu, alpha, beta\n",
    "initial_params = [0.1, 0.5, 0.3]\n",
    "\n",
    "# Optimize parameters\n",
    "result = minimize(\n",
    "    hawkes_log_likelihood,\n",
    "    initial_params,\n",
    "    args=(event_times, T),\n",
    "    bounds=[(0, None), (0, None), (0, None)]\n",
    ")\n",
    "\n",
    "# Output the estimated parameters\n",
    "mu_est, alpha_est, beta_est = result.x\n",
    "print(\"Estimated Parameters:\")\n",
    "print(f\"Baseline Intensity (μ): {mu_est}\")\n",
    "print(f\"Excitation Factor (α): {alpha_est}\")\n",
    "print(f\"Decay Rate (β): {beta_est}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      numeric_timestamp  hawkes_intensity\n",
      "1975                0.0          1.126225\n",
      "3994            18720.0          1.126225\n",
      "625             24600.0          1.126225\n",
      "636             29520.0          1.126225\n",
      "4255            43740.0          1.126225\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Assuming your data has a 'timestamp' column\n",
    "# Convert timestamps to seconds since the start of observation\n",
    "data['numeric_timestamp'] = (data['timestamp'] - data['timestamp'].min()).dt.total_seconds()\n",
    "event_times_numeric = event_times - event_times.min()  # Align the scale\n",
    "\n",
    "# Compute temporal decay (intensity) for each tweet\n",
    "def compute_intensity(event_times, mu, alpha, beta, t):\n",
    "    # Ensure that t and event_times are numeric\n",
    "    past_events = event_times[event_times < t]\n",
    "    intensity = mu + np.sum(alpha * np.exp(-beta * (t - past_events)))\n",
    "    return intensity\n",
    "\n",
    "# Apply to dataset using the numeric timestamp\n",
    "data['hawkes_intensity'] = data['numeric_timestamp'].apply(\n",
    "    lambda t: compute_intensity(event_times_numeric, mu_est, alpha_est, beta_est, t)\n",
    ")\n",
    "\n",
    "# Preview dataset with Hawkes intensity\n",
    "print(data[['numeric_timestamp', 'hawkes_intensity']].head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.682\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Define features and target\n",
    "X = data[['likes', 'retweets', 'replies', 'hawkes_intensity']]\n",
    "y = (data['virality_score'] > 0.7).astype(int)  # Virality threshold\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Train Random Forest Classifier\n",
    "model = RandomForestClassifier(random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n"
   ]
  }
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