prepare_dataset.ipynb

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        "import numpy as np\n",
        "import pandas as pd"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 27977,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27972,\n        \"samples\": [\n          \"im ready gothanks previous person commented last post pushing edge mum work tomorrow thursday friday thats ill it creating set tomorrow week nothing ever goes way life ive ruined childhood trauma effects life result abandoned everybody ive ever loved even change heart still need go current situation thats it im going die soon\",\n          \"atoms ashim posting im anonymous molested  older sister cover secret played liar entire childhood put bad spot emotionally constantly getting trouble parents school bullied rediculed friends harrassed elementary school students teachers put medication rd grade everyday pulled classroom sit principal office would come home crying everyday mistreatment mom finally enough pulled public school transferred private christian school behind education next year retake rd grade students bullied started fight back aggressively also landed principals office th grade transferred back public school made friends bullies bad kids class want bullied anymore tough guy got girlfriend short lived ended writing notes wanted fuck bunch sexual stuff soon found grandma called principal school tried press charges minor gone puberty yet really understand ramifications saying grip parents tightened medication forced throat th grade teacher slapped across face class shut class still remember bead sweat rolling mustache red angry face slapped me made turn back hardened asshole teachers care school point wanted run away several times could never make friends felt like big fucking joke me fist fights recess involved somebody else typically started kids corned back fence friends said fuck friends need them th grade proceeded get girlfriend become sexually active th grade high school began relationship started take turn eventually ended longest friendship entire life devastating ended began get suicidal depression tried overdose medication class caught red handed taking entire bottle pills became violently ill blacked bathroom found carried nurses office school principal called ambulance contacted mom mom devastated pissed crying uncontrollably lied said accident switched meds antidepressants later stopped taking gag reflex overdose went  long term relationship breakups ended cutting arms burning lighter pulled class senior year high school arrested front whole school shooting front school paintball gun night before prank someone would clean charged  different things including terrorism absolutely drive become anything life think going live past  academically barely graduated high school convinced pull shit together go college failed college blew grant money food diamonds girlfriend relationship ended joined marine corps go die country iraq opf fallujah ex girlfriend came see california led wanted get back knew knew company getting deployed iraq fight real full metal jacket mental breakdown weeks deployed threatened kill bunch people landed hospital diagnosis ptsd bipolar major depression fraudulent enlistment charge given discharge honorable conditions plane ticket home tried go back school failed racked another bill year discharge girlfriend broke up friends considered failure everything let love life walk away soon keep depression control jumped relationship first girl would talk gas station convinced looks everything chick nice funny showed attention wanted cock cake care fucked head son already whose father jail decided would pull shit together roll model later started self medicating weed really cop felt great high time friends wifes friends supplying me temper really chilled smoked manic didnt filled uncontrollable anxietydepression rage often super asshole little man syndrome didnt smoke often thought redflag option stop internal pain instead saying loud would impulsively things hurt myself punched structure point shattered wrist several fingers felt better little bit marriage ended year later point interest forming new relationships anyone tired looking new connects weed moved around different states legal dispensaries soon realized weed helping actually making things worse control anymore desperate plea help sister gave place stay moving sister family found work started busting ass make things work could get place entire focus work smoking weed numb pain living often would isolate away everyone else could smoke weed cigarettes really acceptable lifestyle eventually led renting room house full strangers closer work work roommates knowing weed would go room steal weed play instruments work put huge panic major anxiety depression eventually leave situation move back sister brother law take well year started throwing offensive false accusations sexually since interested dating divorce nephews would repeat said friends friends parents soon everyone treated fucked way made things even worse instead giving getting defensive fucking talking shit decided pack shit leave found cheap place live way woods away everyone else keep myself working employer  years started feel little comfortable around coworkers opened little relate mentioned high school paintballing incident normal conversation conflicts employer soon started take place coworker told boss paintballing played sociopath wanted job pay concerned thought boss knew well enough believe later sit boss talk future goals told goal make better future plans start business real grown shit like one bit fact retaliated cutting hours bonus pay singled rest crew made work jobs less time complete pressure unbearable redflag plate everyday every hour constantly mind started feel ill overwhelming uncontrollable feelings panic attack quit job health insurance afford cost housing car payments insurance cellphone bill ended going doctor heart would beat chest throwing every morning years wanted know why turns heart enlarged may diabetes stage  hypertension might heart attack stroke cant relieve stress take care myself got way refused take medications instead still self medicating stop weed without losing mind emotionally stopped smoking weedcigs mt dew per doctors request save life anxiety debilitating obsess death cant hold conversation cant share eye contact feel extremely uncomfortable every social situation encounter cant bring back normal feel like normal wanting kill ending existence im running short money bank feel like work people fucking minion worker slave would rather die life completely closed feel though time running out well self control hide painful memories mom traveled  miles past slipped said going kill extremely worried depressed feels like lose child even tho ive said nothing recently actually moved woods far away everyone kill myself nobody would find me cant sleep anymore mind always thinking ways go every tree look at see hanging every gun looks see blood scattered wall body slumped over every tall building see body bouncing pavement every light pole see think truck wrapped around body half way hood windshield go jog try clear mind feel urge step front car semi head hurts cant help punch face burn propane torch relieve pain extremely difficult time analyzing events life absolutely hating people theyve done me hating unable forgive forget move on shitty things ive said done people control myself thought redflag might parents kept here feel hard even around family hour without irritated rude hate everything me starting understand everyone else hates too hurtful things go away cannot help desperately want become ash float away space dot planet insignificant every way mankind nothing offer feel ashamed illness feel like waste space oxygen pray god blow heart chest mom dad would live fact son willingly killed himself\",\n          \"wanna use omegle cus self esteems low ppl always compliment saw ppl saying got doxxed using omegle shit uhh meh oh well\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
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      "source": [
        "df = pd.read_csv('/content/mental_health.csv')\n",
        "df"
      ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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/1zKqVavWZS9suX//fkVERBT5IFwwHfzswuxSFff12b9/vzp06FBk+9kZrrrqqmLnuOKKK4r8PQcHB2vLli0XvW9kZKRGjhypSZMmac6cObr++ut122236f777+fUPQCoBKZNm6Yrr7xSHh4eCgsLU+PGjS94dbXdu3crLS1NNWrUOOf+gkW9z6UsP1ekpKRo3Lhx+vLLL4tkONcaiZf6Weh8Cj43/PO0Ni8vL9WvX79EnysuRXp6uqQLF3v//ve/9fXXX6tXr16qVauWevToobvvvls333zzJT9OcV+fRo0aFfra399fNWvWLLI+Zmnbv3+/GjVqVOR79nyf7y7lewmoKCilAJQJNzc3devWTVOnTtXu3bvVvHlz/fbbb7rtttvUpUsXvffee6pZs6Y8PT01a9YszZ07t8gxzv5t4oU0adJEkrR161b17dv3kjOeb30l4/SC4qmpqeratasCAgL0yiuvqEGDBvLx8dGmTZv03HPPFbms8aXmLW8lfX0u5nxrL/xzEfgCF3u9L+att97Sgw8+qO+++05Lly7VE088ofHjx2vt2rW64oorLi00AMAl/fOXXxdjt9tVo0YNzZkz55z7q1evft77NmzYUB4eHtq6dWuxMl7Kv3N333231qxZo2eeeUatWrWSv7+/7Ha7br755iKfKyTn/WxxMQVXJmzYsOF5x9SoUUNxcXFasmSJfvrpJ/3000+aNWuWHnjggSILgJ9Peb4+5/t8UxYu9zMT4EoopQCUmby8PElnflv2v//9Tz4+PlqyZEmhS/XOmjXrsh7nuuuuU3BwsL744gs9//zzxVrs/EJ++eUXHT9+XN9++626dOni2F5wpZ9LVZyFM+vWravly5fr5MmThX67WHCqYN26dYv12FLxX5+6detq165dRbb/M0PBb+3OvnqPVLLZXAUu9lq1aNFCLVq00Isvvqg1a9bo2muv1YwZM/Tqq6+W+DEBABVPgwYNtHz5cl177bXFLi78/Px04403auXKlTp48KBq165dKplOnDihFStWaNy4cRozZoxj++7du4t1nOJ+rpDyFwSvX7++Y3tOTo7i4+MVFRVVrMe+VAWLsF/s1DovLy/deuutuvXWW2W32/Xvf/9b77//vl566SU1bNiwWM/1UuzevVvdunVzfJ2enq6jR4+qd+/ejm3BwcFFPtvk5OTo6NGjhbYV9+9hy5YtstvthWZLXc7nO6CiYE0pAGUiNzdXS5culZeXl2Nqsru7uywWS6HfNO3bt08LFiy4rMfy8/PTc889px07dui5554752+RPv/8c61fv75Yxy0ob84+Xk5Ojt57771iHadKlSpFPtycT+/evWWz2fTuu+8W2j558mRZLBb16tWrWI8tFf/16d27t9avX6+YmBjH/oyMDM2cOVP16tVzrPPVoEEDSdKqVasc42w2m2bOnFnsjAWqVKkiqWjRZbVaHSVngRYtWsjNza3QaaAAAEj5M5JsNpv+85//FNmXl5d30X+XX375ZRmGoYEDBzp+uXa22NjYS57NU+BcnyskacqUKcU6zvn+rTyXqKgoeXl56e233y70uB999JHS0tKKXPm4NMydO1cffvihOnXqpO7du5933PHjxwt97ebm5rjiX8G/7cV5rpdi5syZys3NdXw9ffp05eXlFfp81aBBg0KfbQru98+ZUsXJ1rt3byUkJBS6wnNeXp7eeecd+fv7q2vXriV5OkCFwEwpAKXip59+cvy2JykpSXPnztXu3bs1atQox9oMffr00aRJk3TzzTfrvvvuU1JSkqZNm6aGDRte0rpCF/LMM89o+/bteuutt/Tzzz/rzjvvVHh4uBISErRgwQKtX79ea9asKdYxO3furODgYA0aNEhPPPGELBaLPvvss2JPnW7Tpo2mT5+uV199VQ0bNlSNGjV04403nnPsrbfeqm7duumFF17Qvn371LJlSy1dulTfffedRowY4SiCiqs4r8+oUaP0xRdfqFevXnriiScUEhKiTz75RPHx8frf//7n+A1f8+bN1bFjR40ePVopKSkKCQnRl19+WaQ8Ko4GDRooKChIM2bMUNWqVVWlShV16NBBf/zxh6Kjo3XXXXfpyiuvVF5enj777DO5u7urX79+JX48AEDF1LVrVz366KMaP3684uLi1KNHD3l6emr37t2aN2+epk6dqjvvvPO89+/cubOmTZumf//732rSpIkGDhyoRo0a6eTJk/rll1/0/fffF3uWbkBAgLp06aIJEyYoNzdXtWrV0tKlS4s9A7tNmzaSpCeeeEI9e/aUu7u7+vfvf86x1atX1+jRozVu3DjdfPPNuu2227Rr1y699957ateuXaELkJTEN998I39/f+Xk5Ojw4cNasmSJVq9erZYtW2revHkXvO/DDz+slJQU3Xjjjbriiiu0f/9+vfPOO2rVqpXjF5qtWrWSu7u73njjDaWlpcnb21s33njjedcKu5icnBx1795dd999t+N1uO6663TbbbcVyvXYY4+pX79+uummm/THH39oyZIlCg0NLXSs4mQbOnSo3n//fT344IOKjY1VvXr19M0332j16tWaMmVKsRfVByoUcy76B6CiKLjE7tk3Hx8fo1WrVsb06dMNu91eaPxHH31kNGrUyPD29jaaNGlizJo1y3FZ4bNJMoYNG1bsPN98843Ro0cPIyQkxPDw8DBq1qxp3HPPPcYvv/xSJPOGDRsK3ffnn38ucmnf1atXGx07djR8fX2NiIgI49lnnzWWLFlSZFzXrl2N5s2bnzNTQkKC0adPH6Nq1aqGpEKXCD6XkydPGk899ZQRERFheHp6Go0aNTImTpxY5LW80GOez6W8PoZhGH///bdx5513GkFBQYaPj4/Rvn17Y+HChUWO9/fffxtRUVGGt7e3ERYWZjz//PPGsmXLLvn1+efloQ0j/7LHzZo1Mzw8PAxJxqxZs4y9e/caDz30kNGgQQPDx8fHCAkJMbp162YsX768WM8fAOBazvdv9j+d698TwzCMmTNnGm3atDF8fX2NqlWrGi1atDCeffZZ48iRI5f0+LGxscZ9993n+Dc5ODjY6N69u/HJJ58YNpvNMAzDiI+PNyQZEydOLHJ/ScbLL7/s+PrQoUPG7bffbgQFBRmBgYHGXXfdZRw5cqTIuILPRseOHStyzLy8PGP48OFG9erVDYvFUuQz1Lm8++67RpMmTQxPT08jLCzMePzxx40TJ04UGnOhx/yngrFnf/a74oorjFtuucX4+OOPjaysrCL3+effUcFnkho1ahheXl5GnTp1jEcffdQ4evRooft98MEHRv369Q13d/dCny/q1q1r9OnT55z56tatawwaNMjxdcH30a+//moMHTrUCA4ONvz9/Y0BAwYYx48fL3Rfm81mPPfcc0ZoaKjh5+dn9OzZ09izZ0+RY14oW9euXYt83ktMTDQGDx5shIaGGl5eXkaLFi2MWbNmFRpTnO8loKKwGAarpQEAAAAAAKB8saYUAAAAAAAAyh2lFAAAAABUUtOmTVO9evXk4+OjDh06FPvCMABwOSilAAAAAKAS+uqrrzRy5Ei9/PLL2rRpk1q2bKmePXsqKSnJ7GgAKgnWlAIAAACASqhDhw5q166d3n33XUmS3W5X7dq1NXz4cI0aNcrkdAAqA2ZKAQAAAEAlk5OTo9jYWEVFRTm2ubm5KSoqSjExMSYmA1CZUEoBAAAAQCWTnJwsm82msLCwQtvDwsKUkJBgUioAlY2H2QEqCrvdriNHjqhq1aqyWCxmxwEAABWUYRg6efKkIiIi5ObG7xcBAIDropQqJUeOHFHt2rXNjgEAACqJgwcP6oorrjA7BgAXFRoaKnd3dyUmJhbanpiYqPDwcJNSAahsKKVKSdWqVSXlf0AMCAgwOQ0AAKiorFarateu7fjsAQAl4eXlpTZt2mjFihXq27evpPyzP1asWKHo6GhzwwGoNCilSknBKXsBAQGUUgAAoMyxXACAyzVy5EgNGjRIbdu2Vfv27TVlyhRlZGRo8ODBZkcDUElQSgEAAABAJXTPPffo2LFjGjNmjBISEtSqVSstXry4yOLnAFBWLIZhGGaHqAisVqsCAwOVlpbGTCkAAFBm+MwBAAAqCi7ZAgAAAAAAgHLH6XsAAAAAAAClzGazKTc31+wYZcLT01Pu7u6XfRxKKQAAAAAAgFJiGIYSEhKUmppqdpQyFRQUpPDw8Mu6+AqlFAAAAAAAQCkpKKRq1KghPz+/CnfFXMMwlJmZqaSkJElSzZo1S3wsSikAAAAAAIBSYLPZHIVUtWrVzI5TZnx9fSVJSUlJqlGjRolP5WOhcwAAAAAAgFJQsIaUn5+fyUnKXsFzvJx1syilAAAAAAAASlFFO2XvXErjOVJKAQAAAAAAoNxRSgEAAAAAAKDcsdA5AAAAAABAGas36sdyfbx9r/cp0f2mTZumiRMnKiEhQS1bttQ777yj9u3bl3K6fMyUAgAAAAAAgL766iuNHDlSL7/8sjZt2qSWLVuqZ8+eSkpKKpPHo5QCAAAAAACAJk2apEceeUSDBw9Ws2bNNGPGDPn5+enjjz8uk8ejlAIAAAAAAKjkcnJyFBsbq6ioKMc2Nzc3RUVFKSYmpkwekzWlAAAAAAAOhmEox2aX3S7ZDeP0TXJ3s8jbw02e7sxtACqi5ORk2Ww2hYWFFdoeFhamnTt3lsljmlpKrVq1ShMnTlRsbKyOHj2q+fPnq2/fvucc+9hjj+n999/X5MmTNWLECMf2lJQUDR8+XD/88IPc3NzUr18/TZ06Vf7+/o4xW7Zs0bBhw7RhwwZVr15dw4cP17PPPlvo+PPmzdNLL72kffv2qVGjRnrjjTfUu3fvsnjaAAAAAFCmTmbl6khqlo6mndKJzBylZeYq7VSe0k7lOm7Ws/77VK5NOXl25drsyrMbFzx2QTnl4+kubw+3Qv9dxdtDwVW8VK2Kl0KqeKmav/eZ/z79Z7Cfl9zcLOX0SgBwZqaWUhkZGWrZsqUeeugh3XHHHecdN3/+fK1du1YRERFF9g0YMEBHjx7VsmXLlJubq8GDB2vo0KGaO3euJMlqtapHjx6KiorSjBkztHXrVj300EMKCgrS0KFDJUlr1qzRvffeq/Hjx+uWW27R3Llz1bdvX23atElXXXVV2Tz5SuD1zclmR4CTGtU61OwIAAAALi0lI0d/H0vX4ROndDj1lI44blk6knpKJ7PzyuyxbXZDmTk2ZebYSnR/DzeLIoJ8VSfET7VDfFU7xE91zroF+XmVcmIAlyI0NFTu7u5KTEwstD0xMVHh4eFl8pimllK9evVSr169Ljjm8OHDGj58uJYsWaI+fQpfznDHjh1avHixNmzYoLZt20qS3nnnHfXu3VtvvvmmIiIiNGfOHOXk5Ojjjz+Wl5eXmjdvrri4OE2aNMlRSk2dOlU333yznnnmGUnSf/7zHy1btkzvvvuuZsyYUQbPHAAAAAAu7kRGjv5KPKm/ktK1J/Gk/kpM1+6kk0pOzzE7Wonl2Q0dSMnUgZTMc+4P8PFQ3WpVdGVYVTWtWVXNagaoac0ABVehrALKkpeXl9q0aaMVK1Y4zmKz2+1asWKFoqOjy+QxnXpNKbvdroEDB+qZZ55R8+bNi+yPiYlRUFCQo5CSpKioKLm5uWndunW6/fbbFRMToy5dusjL68wbWM+ePfXGG2/oxIkTCg4OVkxMjEaOHFno2D179tSCBQvK7LkBAAAAwNmSTmZp84FUbT6Qqq2HU7UrIV3J6dlmxyp31qw8bT2cpq2H0wptDw/wUbOIADWtWVVNawaoWc0ARYZWkcXCqYBAaRk5cqQGDRqktm3bqn379poyZYoyMjI0ePDgMnk8py6l3njjDXl4eOiJJ5445/6EhATVqFGj0DYPDw+FhIQoISHBMSYyMrLQmIJFuxISEhQcHKyEhIRzLuRVcIxzyc7OVnb2mX8grFbrpT8xAAAAAJVadp5N2w5btfnACW0+mKq4A6k6nHrK7FhOLcGapQRrllbuTHJsC/bzVJu6wWpXL0Rt64WoRa1AeXmwEDtQUvfcc4+OHTumMWPGKCEhQa1atdLixYuLdCalxWlLqdjYWE2dOlWbNm1yyuZ7/PjxGjdunNkxAAAAALiAnDy7Nu5P0e+7k7Xm7+P684hVOTa72bFc3onMXC3fkaTlO/KLKh9PN119RZDa1QtW23ohalM3WAE+nianBPLte73PxQc5gejo6DI7Xe+fnLaU+u2335SUlKQ6deo4ttlsNj399NOaMmWK9u3bp/DwcCUlJRW6X15enlJSUhyLcIWHh59zka6CfRcac6GFvEaPHl3olD+r1aratWuX4JkCAAAAqIj+SjypVX8d0+97krU+PqXEC4Pj0mXl2rU+PkXr41Mk/S13N4ta1Q5St8bVdUPjGrqqVqDZEQGcxWlLqYEDByoqKqrQtp49e2rgwIGOcxk7deqk1NRUxcbGqk2bNpKklStXym63q0OHDo4xL7zwgnJzc+Xpmd+QL1u2TI0bN1ZwcLBjzIoVKzRixAjHYy1btkydOnU6bz5vb295e3uX2vMFAAAA4NqsWbn6eWeSVv2VrN/3HFOitfKtB+VsbHZDsftPKHb/Cb259C/VqOqtrldWV7cmNXR9o1BVZRYVYCpTS6n09HTt2bPH8XV8fLzi4uIUEhKiOnXqqFq1aoXGe3p6Kjw8XI0bN5YkNW3aVDfffLMeeeQRzZgxQ7m5uYqOjlb//v0VEREhSbrvvvs0btw4DRkyRM8995y2bdumqVOnavLkyY7jPvnkk+rataveeust9enTR19++aU2btyomTNnlsOrAAAAAMBVncjI0dI/E/TTtgSt2XOcU/KcXNLJbM2LPaR5sYfk6W7RNXWC1b1pDfVuUVNXBPuZHQ+odEwtpTZu3Khu3bo5vi44HW7QoEGaPXv2JR1jzpw5io6OVvfu3eXm5qZ+/frp7bffduwPDAzU0qVLNWzYMLVp00ahoaEaM2aMhg4d6hjTuXNnzZ07Vy+++KKef/55NWrUSAsWLNBVV11VOk8UAHBJXt+cbHYEOKlRrUPNjgAADkkns7Rke6IWbzuqdXtTlGc3zI6EEsi1GVoXn6J18Sn676Kdal0nSLdeHaFbrq6pGgE+ZscDKgWLYRi8g5YCq9WqwMBApaWlKSAgwOw4ToEfLnE+/HCJ8+F9A+fD+8YZfOYAzHEiI0ffxR3Wj1uPKnb/CdFDVVxuFqldvRDd2jJCvVvUVEgVL7MjwYVkZWUpPj5ekZGR8vGp2OVmaTxXp11TCgAAAADMZLMbWvXXMc2LPajlfyZxal4lYTfkmEE19vvt6twwVH1b5RdUPp7uZscDKhRKKQAAAAA4S3xyhr7eeFDfbjrEYuWVXN7pYnLVX8f08vfb9a9WEerfrg5X8QNKCaUUAAAAgEovMydPC/84qq83HtTG/SfMjgMndDIrT5+vPaDP1x5Q84gADehQV31bR8jPix+rgZLi/x4AAAAAldbBlEx9smafvt54UNasPLPjwEVsP2LV8/O3avyiHerbupbu71hXjcOrmh0LcDmUUgAAAAAqnTV/J+vj3/dp5c5EFi1HiZ3MztNna/frs7X7dV3DUA3tUl9drqxudizAZVBKAQAAAKgU8mx2LdxyVB/8tlfbj1jNjoMK5vc9yfp9T7Ka1QzQ0C71dcvVNeXh7mZ2LDiTseW8FtnYtGINX7VqlSZOnKjY2FgdPXpU8+fPV9++fcsm22n8HwIAAACgQsvMydMHq/bq+gk/a8RXcRRSKFN/HrVqxFdx6jrxF334215lZHNaKFxDRkaGWrZsqWnTppXbYzJTCgAAAECFlJmTp09j9uuDVXt1PCPH7DioZA6nntKrP+7Q2yt26/6OdTX42khVr+ptdizgvHr16qVevXqV62NSSgEAAACoUDJz8vTJmv368DfKKJjPmpWn9375W7NW79OgzvX0WNf6CvLzMjsW4BQopQAAAABUCAVl1Ae/7VUKZRSczKlcm2b8+rfmrNuvh6+rryHXR8rfmx/JUbnxfwAAAAAAl5aVa9PsNfs0cxVlFJzfyaw8TV7+lz6J2afHutbXA53qycfT3exYgCkopQAAAAC4JMMwtCDusCYu3qUjaVlmxwGKJSUjR/9dtFMf/R6v6G4N1b99HXlytT5UMpRSAAAAAFzOhn0penXhn/rjUPEueQ44m0Rrtl76brs+/D1eL/Ruqh7Nw82OBJQbSikAAAAALuNgSqbG/7RDi7YmmB0FKFX7j2dq6Gex6nJldY25pZka1vA3OxIqmfT0dO3Zs8fxdXx8vOLi4hQSEqI6deqUyWNSSgEAAABwetasXE1buUez1uxTTp7d7DhAmVn11zH1mrpKD3SqpxFRjVTVx9PsSKgkNm7cqG7dujm+HjlypCRp0KBBmj17dpk8JqUUAAAAAKf2TewhjV+0Q8dZxByVRK7N0Ee/x+u7uMN6pmdj3d22tiwWi9mxcLnGOvfpxjfccIMMwyjXx2QVNQAAAABOaV9yhgZ8uFb/N+8PCilUSsnpOXruf1vVd9pqxR1MNTsOUOoopQAAAAA4lVybXdN+3qOeU1Zp9Z7jZscBTPfHoTTd8d5q/WfhnzqVYzM7DlBqOH0PAAAAgNOI3X9Cz3+7VbsST5odBXAqdkP66Pd4Ld+RqPF3tFDnBqFmRwIuG6UUAAAAANOdzMrVG4t3au66A7KX75ImgEvZfzxTAz5cp/7t6uj53k1YCB0ujVIKAAAAgKl+352s/5v3hxKsWWZHAVyCYUhfrD+gX3Yl6b+3t1C3JjXMjgSUCGtKAQAAADBFdp5Nr/zwpwZ+vI5CCiiBo2lZGjx7g576Kk6pmVwMwJnY7XazI5S50niOzJQCAAAAUO52HLVqxJdxrB0FlIL5mw9r7d7jmnJPK3WoX83sOJWal5eX3NzcdOTIEVWvXl1eXl6yWCxmxypVhmEoJydHx44dk5ubm7y8vEp8LEopAAAAAOXGMAx98Ntevbn0L+XkVfyZBEB5OZqWpXs/WKvobg31ZNSVcnerWEWIq3Bzc1NkZKSOHj2qI0eOmB2nTPn5+alOnTpycyv5SXiUUgAAAADKxdG0U3r66z+05u/jZkcBKiS7Ib29co/W/H1cU+9trVpBvmZHqpS8vLxUp04d5eXlyWazmR2nTLi7u8vDw+OyZ4FRSgEAAAAoc4u3HdVz/9uqtFO5ZkcBKryN+0+o15RVer3f1erdoqbZcSoli8UiT09PeXpydcQLYaFzAAAAAGXGZjf030U79NjnmyikgHJkzcrTv+ds0qj/bdGpnIo5Wweuj1IKAAAAQJlITs/WgA/XauaqvWZHASqtLzcc1G3v/q745AyzowBFUEoBAAAAKHWx+0/olrd/19q9KWZHASq93Unp+te7v+vnXUlmRwEKoZQCAAAAUKpmr45X/5kxSrBmmR0FwGnWrDwNmb1B037eY3YUwIGFzgEAAACUilM5No36dou+i6vYl0EHXJXdkCYu2aU/j1g18a6r5edFJQBz8R0IAAAA4LIlpGVp8OwN2nHUanYUABfx49aj+vtYumYObKs61fzMjoNKjNP3AAAAAFyW7UfS1HfaagopwIXsTDip26b9rt92HzM7CioxSikAAAAAJbZyZ6LunsH6UYArSs3M1YOzNujL9QfMjoJKilIKAAAAQIl8GrNPj3waq4wcm9lRAJSQzW5o1LdbNXnZX2ZHQSXEmlIAAAAAisVuN/Taoh366Pd4s6MAKCVTV+xWojVLr93eQu5uFrPjoJKglAIAAABwyU7l2PTkl5u19M9Es6MAKGVfbjiopJPZmnbfNfL1cjc7DioBTt8DAAAAcEnSTuXqvg/XUkgBFdjKnUnq/8FaHU/PNjsKKgFKKQAAAAAXdTw9W/fOXKvNB1LNjgKgjP1xMFX9pq/R/uMZZkdBBUcpBQAAAOCCEq1ZumfmWv151Gp2FADlZN/xTPWbHqOdCfx/j7JDKQUAAADgvA6mZOquGTHak5RudhQA5Sz59AzJbYfTzI6CCopSCgAAAMA5/X0sXXe/H6MDKZlmRwFgkhOZubrvg7XafOCE2VFQAVFKAQAAACjizyNW3fN+jI6mZZkdBYDJrFl5GvjRem3cl2J2FFQwlFIAAAAACtlyKFX3frBWyek5ZkcB4CTSs/M06GOKKZQuU0upVatW6dZbb1VERIQsFosWLFjg2Jebm6vnnntOLVq0UJUqVRQREaEHHnhAR44cKXSMlJQUDRgwQAEBAQoKCtKQIUOUnl74fPctW7bo+uuvl4+Pj2rXrq0JEyYUyTJv3jw1adJEPj4+atGihRYtWlQmzxkAAABwZn8eseqBj9cr7VSu2VEAOJmMHBvFFEqVqaVURkaGWrZsqWnTphXZl5mZqU2bNumll17Spk2b9O2332rXrl267bbbCo0bMGCAtm/frmXLlmnhwoVatWqVhg4d6thvtVrVo0cP1a1bV7GxsZo4caLGjh2rmTNnOsasWbNG9957r4YMGaLNmzerb9++6tu3r7Zt21Z2Tx4AAABwMrsTT2rgR+uUmkkhBeDcKKZQmiyGYRhmh5Aki8Wi+fPnq2/fvucds2HDBrVv31779+9XnTp1tGPHDjVr1kwbNmxQ27ZtJUmLFy9W7969dejQIUVERGj69Ol64YUXlJCQIC8vL0nSqFGjtGDBAu3cuVOSdM899ygjI0MLFy50PFbHjh3VqlUrzZgx45LyW61WBQYGKi0tTQEBASV8FSqW1zcnmx0BTmpU61CzI8BJ8b6B8+F94ww+c6Cs7EvO0N3vxyjpZLbZUQC4gAAfD331aCc1rcm/RSg5l1pTKi0tTRaLRUFBQZKkmJgYBQUFOQopSYqKipKbm5vWrVvnGNOlSxdHISVJPXv21K5du3TixAnHmKioqEKP1bNnT8XExJTxMwIAAADMdzTtlAZ8uI5CCsAls2bl6YGP1+vAca7OiZJzmVIqKytLzz33nO69917HbwUTEhJUo0aNQuM8PDwUEhKihIQEx5iwsLBCYwq+vtiYgv3nkp2dLavVWugGAAAAuJrj6dm6/8N1Opx6yuwoAFzMsZPZGvjxOiWd5CqdKBmXKKVyc3N19913yzAMTZ8+3ew4kqTx48crMDDQcatdu7bZkQAAAIBiOZmVq0Gz1uvvYxlmRwHgovYfz9SgjzfImsVadCg+py+lCgqp/fv3a9myZYXWTggPD1dSUlKh8Xl5eUpJSVF4eLhjTGJiYqExBV9fbEzB/nMZPXq00tLSHLeDBw+W/EkCAAAA5Swnz65HPt2obYeZ8Q/g8uw4atXDszcqK9dmdhS4GKcupQoKqd27d2v58uWqVq1aof2dOnVSamqqYmNjHdtWrlwpu92uDh06OMasWrVKublnWttly5apcePGCg4OdoxZsWJFoWMvW7ZMnTp1Om82b29vBQQEFLoBAAAArmLU/7Zo7V6ungWgdKzfl6JhczYpz2Y3OwpciKmlVHp6uuLi4hQXFydJio+PV1xcnA4cOKDc3Fzdeeed2rhxo+bMmSObzaaEhAQlJCQoJydHktS0aVPdfPPNeuSRR7R+/XqtXr1a0dHR6t+/vyIiIiRJ9913n7y8vDRkyBBt375dX331laZOnaqRI0c6cjz55JNavHix3nrrLe3cuVNjx47Vxo0bFR0dXe6vCQAAAFDWpiz/S99uPmx2DAAVzIqdSRr17VazY8CFmFpKbdy4Ua1bt1br1q0lSSNHjlTr1q01ZswYHT58WN9//70OHTqkVq1aqWbNmo7bmjVrHMeYM2eOmjRpou7du6t379667rrrNHPmTMf+wMBALV26VPHx8WrTpo2efvppjRkzRkOHDnWM6dy5s+bOnauZM2eqZcuW+uabb7RgwQJdddVV5fdiAAAAAOVgwebDmrJ8t9kxAFRQ38Qe0vRf/jY7BlyExTAMw+wQFYHValVgYKDS0tI4le+01zcnmx0BTmpU61CzI8BJ8b6B8+F94ww+c+ByrNt7XAM/Wq8cTq8BUIbcLNL7A9vqpmZhFx+MSs2p15QCAAAAUDr2HkvXo5/HUkgBKHN2Qxrx5WbtOMqFFHBhlFIAAABABZeSkaPBszcoNZNLtgMoHxk5Nj38yUYlp2ebHQVOzMPsAAAAAADKTp7Nrsc+j9X+45lmR0EZS/19jtJWf1Fom0fIFar1yAxJkpGXo5SVHylzxyoZtlz5Rl6jkB6Py71K8HmPmfzjZGVsK3ylcp/IaxR29yunj5mr44vfVubutXKvEqyQHv+Wb71WjrFp6/4nm/WYQm56rJSeJVzJ4dRTeuyzWM19pKO8PJgTg6IopQAAAIAK7PWfdmp9fIrZMVBOPEPrKOye185scDtTBKSs+ECn/t6o0L6j5OZdRSnLpuvY/P8q/P6JFzymT2QbhfYecWaDh6fjP0/+sVg5CXsUfv+bOrU3Vsk/TNQV0Z/LYrEoNzVB6X8sUc1BU0rp2cEVbdx/QqO/3aq37m5pdhQ4IapKAAAAoIL6cctRffh7vNkxUJ7c3OXuH3zm5hcoSbJnZyh9yzIF3zhEvnVbyju8oUJ7j1D24R3KPrzzgoe0eHgWPqaPv2Nf7vGD8m3YQV7V66rqNX1kz0yT/VT+OkIpS99T8A0Pys3br+yeL1zC/zYd0ger9podA06ImVIAAABABbQnKV3PfvOH2TFQzvJOHNGhaQ/I4u4pr1pNFNx1kDwCaig7YY9kzyt0ap1ntdpyD6iu7CM75V2ryXmPmXVgqw6+M0BuPv7yqXO1groMlLtv/tU/vWpEKmPbz7LnZisrfpPc/UPk5hug9O0/y+LhJb8rO5f1U4aLeGPxTrWuE6S29ULMjgInQikFAAAAVDAZ2Xl67PNYZeTYzI6CcuRds7Gq9X5KniG1ZEtPUdrqL5Qw5zlFPDRN9owTkruH3M6a5SRJ7lWCZMs4cd5j+kZeI78rO8sjKEx5J44qddWnSpr3ssLvf1MWN3f5t7hJOUn7dOSjf8vdN0Ch/3pO9qx0pf0+R2H3jteJVZ8pc8cqeQSFq1rvJ+VRNbSsXwY4qTy7oeFfbNaPT1yvkCpeZseBk6CUAgAAACqYZ/+3RXuS0s2OgXLm26DtmS9qRMo7orEOTX9IGTt/l5tnyUqAKs26Ov7bq3o9edaI1JH3H1bWga3yrddKFncPVevxeKH7JP84RVXb3KqcxL06tTtGNQe/I+u6/+nE8pmqfvvzJcqBiuFoWpae+ipOswe3k8ViMTsOnABrSgEAAAAVyEe/x+vHLUfNjgEn4ObjL8+QWspLPSK3KsGSLU/2rMJlpS0j9YJX3/snz6BwufkGKC/13N9jWfu3KPf4flW95hZlHdgi3/pt5eblI78m1ynrwNbLej6oGH7965je++Vvs2PASVBKAQAAABVE7P4TGr9oh9kx4CTsOaeUl3pU7lVC5B3eUHLz0Kn9Z9YZyz1+SDbrMXlHnH89qX/KsybLfuqk3KsUXRfIyMtRyrLpqtYzWhY3d8mwy7CfPoXUbpNh2C/7OaFimLTsL64KCkmUUgAAAECFkJ6dp6e+ilOe3TA7CkxyYuVHyjqwVXlpico6tEPHvn1NsripSrOucvOuIv+rb9KJlR8qa/8WZSfs0fFFU+Qd0aTQIueHP3hMmX+tkZRfap34+WNlH96pvLREndoXp2Pf/kcewTXlG3lNkcdPXfOlfOu3lVdYA0mSd61myvxrjXKS4nVy00L51GpaPi8EnJ7Nbmj4F5t0PD3b7CgwGWtKAQAAABXAuO+360BKptkxYKK8k8lK/mGibKescvcNlPcVzRQ+8C25+wVKkkK6P6IUi5uOLfivDFuufCKvUbWb/l34GCmHZM8+/X1kcVNOUrzSt62QPStD7v4h8o1sraDr75fFw7PQ/XKO7VPmzt9U88F3HNv8mlyrrINblTDnOXlWq6XQW58p2xcALiXRmq0RX8Xp04fas75UJWYxDINfpZQCq9WqwMBApaWlKSAgwOw4TuH1zclmR4CTGtWaq67g3HjfwPnwvnEGnzlwLou3Jeixz2PNjgEAxfbSLc005LpIs2PAJJy+BwAAALiwJGuWRn+7xewYAFAiE5fs1N5jXC20sqKUAgAAAFzYM99s0YnMXLNjAECJZOXa9fS8P2RjPbxKiVIKAAAAcFGfxuzTr38dMzsGAFyWzQdS9f6qv82OARNQSgEAAAAuaE9Suv67aIfZMQCgVExZtlu7Ek6aHQPljFIKAAAAcDF2u6H/m/eHsnLtZkcBgFKRY7Nr5NdxyrXxvlaZUEoBAAAALubzdfsVdzDV7BgAUKq2H7HqnZV7zI6BckQpBQAAALiQRGuWJi7eZXYMACgT7/28R9sOp5kdA+WEUgoAAABwIWO+26aT2XlmxwCAMpFnN/Tigm0yDK7GVxlQSgEAAAAuYun2BC3Znmh2DAAoU3EHU/XF+oNmx0A5oJQCAAAAXEB6dp5e/n672TEAoFxMWLJTKRk5ZsdAGaOUAgAAAFzAm0t26WhaltkxAKBcpGbm6o2fdpodA2WMUgoAAABwcn8cTNWnMfvMjgEA5err2IOK3X/C7BgoQ5RSAAAAgBMzDEMvf79ddtb8BVDJGIb04oJtsvEGWGFRSgEAAABO7Ps/jijuYKrZMQDAFDuOWvXJmn1mx0AZoZQCAAAAnFRWrk0TFu8yOwYAmGrysr+UnJ5tdgyUAUopAAAAwEl9vDpeh1NPmR0DAEx1MjtP76zYbXYMlAFKKQAAAMAJJadna/rPf5sdAwCcwtz1B7QvOcPsGChllFIAAACAE5q07C+dzM4zOwYAOIVcm6GJSzmduaKhlAIAAACczF+JJ/XVhoNmxwAAp7Jo61FtOZRqdgyUIkopAAAAwMm89uMOLoEOAP9gGNL4RTvNjoFSRCkFAAAAOJG1e4/r17+OmR0DAJxSzN7j+nlXktkxUEoopQAAAAAnMnU5V5gCgAt546edsjObtEKglAIAAACcxLq9xxWz97jZMQDAqe1MOKnv/jhsdgyUAkopAAAAwElMXcEsKQC4FO+u3MNsqQqAUgoAAABwAuvjU7Tmb2ZJAcCl+PtYhhZvTzA7Bi4TpRQAAADgBKau+MvsCADgUqb9vMfsCLhMlFIAAACAyTbsS9HqPcySAoDi2H7EypX4XBylFAAAAGCyKcuZJQUAJTFtJbOlXBmlFAAAAGCiuIOpzJICgBLauP+E1nHVUpdFKQUAAACY6KPf482OAAAu7V3WlnJZlFIAAACASRLSsvTT1qNmxwAAl/bb7mRtOZRqdgyUAKUUAAAAYJJPYvYpz26YHQMAXN6HvzHr1BWZWkqtWrVKt956qyIiImSxWLRgwYJC+w3D0JgxY1SzZk35+voqKipKu3fvLjQmJSVFAwYMUEBAgIKCgjRkyBClp6cXGrNlyxZdf/318vHxUe3atTVhwoQiWebNm6cmTZrIx8dHLVq00KJFi0r9+QIAAAAFsnJt+mL9AbNjAECF8NO2o0qyZpkdA8VkaimVkZGhli1batq0aefcP2HCBL399tuaMWOG1q1bpypVqqhnz57KyjrzjTZgwABt375dy5Yt08KFC7Vq1SoNHTrUsd9qtapHjx6qW7euYmNjNXHiRI0dO1YzZ850jFmzZo3uvfdeDRkyRJs3b1bfvn3Vt29fbdu2reyePAAAACq1/206pNTMXLNjAECFkGsz9Pna/WbHQDFZDMNwivnCFotF8+fPV9++fSXlz5KKiIjQ008/rf/7v/+TJKWlpSksLEyzZ89W//79tWPHDjVr1kwbNmxQ27ZtJUmLFy9W7969dejQIUVERGj69Ol64YUXlJCQIC8vL0nSqFGjtGDBAu3cuVOSdM899ygjI0MLFy505OnYsaNatWqlGTNmXFJ+q9WqwMBApaWlKSAgoLReFpf2+uZksyPASY1qHWp2BDgp3jdwPrxvnMFnjorBMAzdNHmV9iSlX3wwAOCShPp7ac2o7vLyYKUiV+G0f1Px8fFKSEhQVFSUY1tgYKA6dOigmJgYSVJMTIyCgoIchZQkRUVFyc3NTevWrXOM6dKli6OQkqSePXtq165dOnHihGPM2Y9TMKbgcc4lOztbVqu10A0AAAC4FL/+dYxCCgBKWXJ6jn7cesTsGCgGpy2lEhISJElhYWGFtoeFhTn2JSQkqEaNGoX2e3h4KCQkpNCYcx3j7Mc435iC/ecyfvx4BQYGOm61a9cu7lMEAABAJTVr9T6zIwBAhfT5WtbqcyVOW0o5u9GjRystLc1xO3jwoNmRAAAA4AIOncjUqt3HzI4BABVS7P4T2pnAmUyuwmlLqfDwcElSYmJioe2JiYmOfeHh4UpKSiq0Py8vTykpKYXGnOsYZz/G+cYU7D8Xb29vBQQEFLoBAAAAF/P1xkNyjlVdAaBiYsFz1+G0pVRkZKTCw8O1YsUKxzar1ap169apU6dOkqROnTopNTVVsbGxjjErV66U3W5Xhw4dHGNWrVql3NwzVzZZtmyZGjdurODgYMeYsx+nYEzB4wAAAAClwW439M1GZtgDQFlasPmITuXYzI6BS2BqKZWenq64uDjFxcVJyl/cPC4uTgcOHJDFYtGIESP06quv6vvvv9fWrVv1wAMPKCIiwnGFvqZNm+rmm2/WI488ovXr12v16tWKjo5W//79FRERIUm677775OXlpSFDhmj79u366quvNHXqVI0cOdKR48knn9TixYv11ltvaefOnRo7dqw2btyo6Ojo8n5JAAAAUIGt2n1MR9KyzI4BABVaenaelv55/jWi4TxMLaU2btyo1q1bq3Xr1pKkkSNHqnXr1hozZowk6dlnn9Xw4cM1dOhQtWvXTunp6Vq8eLF8fHwcx5gzZ46aNGmi7t27q3fv3rruuus0c+ZMx/7AwEAtXbpU8fHxatOmjZ5++mmNGTNGQ4cOdYzp3Lmz5s6dq5kzZ6ply5b65ptvtGDBAl111VXl9EoAAACgMpgXe8jsCABQKXy76bDZEXAJLIbBGe2lwWq1KjAwUGlpaawvddrrm5PNjgAnNap1qNkR4KR438D58L5xBp85XFfaqVy1e225cvLsZkcBgArP3c2itaO7q3pVb7Oj4AKcdk0pAAAAoCJZuOUIhRQAlBOb3dD3fxwxOwYuglIKAAAAKAf/49Q9AChX8zfzvuvsKKUAAACAMnbgeKY2HUg1OwYAVCrbDlu1O/Gk2TFwAZRSAAAAQBn7adtRsyMAQKU0fzMLnjszSikAAACgjP20jUuTA4AZvos7Iq7v5rwopQAAAIAydDTtlP44lGp2DAColA6nntKmAyfMjoHzoJQCAAAAytDibQnil/QAYJ5lfyaZHQHnQSkFAAAAlCFO3QMAcy37k/dhZ0UpBQAAAJSRYyeztXFfitkxAKBS+/tYhuKTM8yOgXOglAIAAADKyNI/E2Tn1D0AMB2zpZwTpRQAAABQRhZz6h4AOIXlrCvllCilAAAAgDKQnp2ntXuPmx0DACAp9sAJpWTkmB0D/0ApBQAAAJSBtX8fV66Nc/cAwBnY7IZW7mS2lLOhlAIAAADKwO97ks2OAAA4C+tKOR9KKQAAAKAM/Lb7mNkRAABnWbPnuGxcfcKpUEoBAAAApexo2in9fYzLjwOAMzmZnadth9PMjoGzUEoBAAAApey3vzh1DwCcUQwXoHAqlFIAAABAKfuN9aQAwCnF/E0p5UwopQAAAIBSZBiG1lBKAYBT2rgvRXk2u9kxcBqlFAAAAFCKth+x6nhGjtkxAADnkJFj0x+HWFfKWVBKAQAAAKVofXyK2REAABewlnWlnAalFAAAAFCKNh04YXYEAMAFsK6U86CUAgAAAEpR3MFUsyMAAC4gdv8J5eSxrpQzoJQCAAAASsmxk9k6dOKU2TEAABdwKtemnQlWs2NAlFIAAABAqdnMqXsA4BK2sNi5U6CUAgAAAEoJp+4BgGvYdphSyhlQSgEAAAClZPOBVLMjAAAuATOlnAOlFAAAAFAK7HZDWw6lmh0DAHAJdiedVHaezewYlR6lFAAAAFAKdiWeVEYOP+AAgCvItRnaefSk2TEqPUopAAAAoBRsP8KVnADAlWxhXSnTUUoBAAAApWB3Ir9xBwBXso11pUxHKQUAAACUgt1J6WZHAAAUAzOlzEcpBQAAAJSCv5gpBQAu5e+kdNnthtkxKjVKKQAAAOAyZebk6XDqKbNjAACKIcdm16ETvHebiVIKAAAAuEx7ktJl8Mt2AHA5e5M59dpMlFIAAADAZdqdyA81AOCK9h7LMDtCpUYpBQAAAFymv5JYTwoAXFF8MqWUmSilAAAAgMu0h5lSAOCSOH3PXJRSAAAAwGWKP85v2gHAFcVz+p6pKKUAAACAy3SEK+8BgEs6as3SqRyb2TEqLUopAAAA4DKkZOQoK9dudgwAQAkYButKmYlSCgAAALgMzJICANd26ESm2REqrRKVUps2bdLWrVsdX3/33Xfq27evnn/+eeXk5JRaOAAAAMDZUUoBgGtLPJltdoRKq0Sl1KOPPqq//vpLkrR37171799ffn5+mjdvnp599tlSDQgAAAA4M0opAHBtx6xZZkeotEpUSv31119q1aqVJGnevHnq0qWL5s6dq9mzZ+t///tfqYWz2Wx66aWXFBkZKV9fXzVo0ED/+c9/ZBiGY4xhGBozZoxq1qwpX19fRUVFaffu3YWOk5KSogEDBiggIEBBQUEaMmSI0tMLX/Zxy5Ytuv766+Xj46PatWtrwoQJpfY8AAAAUHEdSeOHGQBwZYlWZkqZpUSllGEYstvzF3Ncvny5evfuLUmqXbu2kpOTSy3cG2+8oenTp+vdd9/Vjh079MYbb2jChAl65513HGMmTJigt99+WzNmzNC6detUpUoV9ezZU1lZZz4cDBgwQNu3b9eyZcu0cOFCrVq1SkOHDnXst1qt6tGjh+rWravY2FhNnDhRY8eO1cyZM0vtuQAAAKBiYqYUALi2pJP8csEsHiW5U9u2bfXqq68qKipKv/76q6ZPny5Jio+PV1hYWKmFW7Nmjf71r3+pT58+kqR69erpiy++0Pr16yXll2NTpkzRiy++qH/961+SpE8//VRhYWFasGCB+vfvrx07dmjx4sXasGGD2rZtK0l655131Lt3b7355puKiIjQnDlzlJOTo48//lheXl5q3ry54uLiNGnSpELlFQAAAPBPlFIA4NqYKWWeEs2Umjx5sjZt2qTo6Gi98MILatiwoSTpm2++UefOnUstXOfOnbVixQrH+lV//PGHfv/9d/Xq1UtSfgmWkJCgqKgox30CAwPVoUMHxcTESJJiYmIUFBTkKKQkKSoqSm5ublq3bp1jTJcuXeTl5eUY07NnT+3atUsnTpw4Z7bs7GxZrdZCNwAAAFQ+/DADAK6NmVLmKdFMqZYtWxa6+l6BiRMnysOjRIc8p1GjRslqtapJkyZyd3eXzWbTa6+9pgEDBkiSEhISJKnI7KywsDDHvoSEBNWoUaPQfg8PD4WEhBQaExkZWeQYBfuCg4OLZBs/frzGjRtXCs8SAAAArsx6KtfsCACAy3A8I0d5Nrs83Es0bweXoUSveP369XX8+PEi27OysnTllVdedqgCX3/9tebMmaO5c+dq06ZN+uSTT/Tmm2/qk08+KbXHKKnRo0crLS3NcTt48KDZkQAAAFDObHZD6Tl5ZscAAFwGw5COpTPr1Qwlmta0b98+2Wy2Ituzs7N16NChyw5V4JlnntGoUaPUv39/SVKLFi20f/9+jR8/XoMGDVJ4eLgkKTExUTVr1nTcLzEx0XF1wPDwcCUlJRU6bl5enlJSUhz3Dw8PV2JiYqExBV8XjPknb29veXt7X/6TBAAAgMuynsrVWReGBgC4qOPpOaoZ6Gt2jEqnWKXU999/7/jvJUuWKDAw0PG1zWbTihUripwGdzkyMzPl5lZ4Mpe7u7vjyn+RkZEKDw/XihUrHCWU1WrVunXr9Pjjj0uSOnXqpNTUVMXGxqpNmzaSpJUrV8put6tDhw6OMS+88IJyc3Pl6ekpSVq2bJkaN258zlP3AAAAAElK49Q9AKgQMrKZ9WqGYpVSffv2lSRZLBYNGjSo0D5PT0/Vq1dPb731VqmFu/XWW/Xaa6+pTp06at68uTZv3qxJkybpoYcecuQYMWKEXn31VTVq1EiRkZF66aWXFBER4cjatGlT3XzzzXrkkUc0Y8YM5ebmKjo6Wv3791dERIQk6b777tO4ceM0ZMgQPffcc9q2bZumTp2qyZMnl9pzAQAAQMVDKQUAFUMGp2Kbolil1NkzlDZs2KDQ0NAyCVXgnXfe0UsvvaR///vfSkpKUkREhB599FGNGTPGMebZZ59VRkaGhg4dqtTUVF133XVavHixfHx8HGPmzJmj6Ohode/eXW5uburXr5/efvttx/7AwEAtXbpUw4YNU5s2bRQaGqoxY8Zo6NChZfr8AAAA4NqsWZRSAFARpGcXXaIIZc9iGJwFXxqsVqsCAwOVlpamgIAAs+M4hdc3J5sdAU5qVOuyLbThunjfwPnwvnEGnzmcy8ItRxQ9d7PZMQAAl+n1O1qof/s6ZseodEq00LkkrVixQitWrFBSUpJjBlWBjz/++LKDAQAAAM6O0/cAoGJIZ00pU5SolBo3bpxeeeUVtW3bVjVr1pTFYintXAAAAIDTO5nFDzEAUBFkcPqeKUpUSs2YMUOzZ8/WwIEDSzsPAAAA4DJy8uwXHwQAcHqZLHRuCreS3CknJ0edO3cu7SwAAACAS8mzszwrAFQEnL5njhKVUg8//LDmzp1b2lkAAAAAl2KzM1MKACqCUzmcvmeGEp2+l5WVpZkzZ2r58uW6+uqr5enpWWj/pEmTSiUcAAAA4MyYKQUAFQPv5+YoUSm1ZcsWtWrVSpK0bdu2QvtY9BwAAACVhc3GDzEAUBHYDd7PzVCiUurnn38u7RwAAACAy+E36wBQMdBJmaNEa0oBAAAAkGyUUgBQITBTyhwlminVrVu3C56mt3LlyhIHAgAAAFwFM6VgJn+PPAV62BTgYVNVj1xVdbepqkee/N1yVcXdJn/3XPlZcuXrlis/t1z5KFc+llz5KlfeypaXcuWtHHka2fIycuRpz5aHPVvu9hy527PlbsuSmy1bFnuOLAaL+qNiy/PrKamN2TEqnRKVUgXrSRXIzc1VXFyctm3bpkGDBpVGLgAAAMDp2SmlIMnbza5AT5sC3PMU4GlTVff8UqiqR56quOWqinueqljyiyE/S5583XLkYzldEilHXsqRt3LyiyEjW55GrjztWWcKIlu23AoKIluWLHlZUl62LDr9/Zd3+gagxLzy0s2OUCmVqJSaPHnyObePHTtW6en8RQIAAKBycHPjIj/OxGIxFOBhU6BHngI88lTVw6aqp2cM+bvbVMU9R36Wgj/z5GvJla8lvyDyVq58LNnyNnLkpdz8csieI08jRx72bHnYs+Ruz3GUQ2552bLYTkm5WbIYpy8lb5eUbepLAKCkLKxuZIYSlVLnc//996t9+/Z68803S/OwAAAAgFPycqeUOh8/9/zTygI8bKrqnqeqHvk3/9OziPxPn1Lm55YnX0uOfC158rHknD7FLOd0OVQwe+j0qWUFBZEtS272HLnbsmSxZcktL1vKy5LFdroRMiTlnr4BwKWglDJFqZZSMTEx8vHxKc1DAgAAAE7Ly8P5f4hxt9gd6w4FeOadLohs8nfPP7Ws4M/8cihXvm458lVBQZQj79PrDnkZ2fJUrjzt2Y6CqGDdIXdb/gyi/FPLThdEBbOHbKdvOWa+CgBwERZ3sxNUSiUqpe64445CXxuGoaNHj2rjxo166aWXSiUYAAAA4Ow83YtXSlVxnFpmU4BHnvxPzyDyd8tTFfdcVXGz5RdElhz5uOXl/3l67SHvgnWHzp49ZGSfPrUsf90hd1uW3OzZcsvLL4iUly2L7XQbZIhiCADOx9PX7ASVUolKqcDAwEJfu7m5qXHjxnrllVfUo0ePUgkGAAAAOLv+Ads0oME3Z607dLogsp9ed8iWLUve6YWpbdlnrmDGwtQA4Fy8q5qdoFIqUSk1a9as0s4BAAAAuJw6xhHp8E9mxwAAXC4vf7MTVEqXtaZUbGysduzYIUlq3ry5WrduXSqhAAAAAJfg5Wd2AgBAafCqYnaCSqlEpVRSUpL69++vX375RUFBQZKk1NRUdevWTV9++aWqV69emhkBAAAA5+TJDzEAUCF4M1PKDCW6XMjw4cN18uRJbd++XSkpKUpJSdG2bdtktVr1xBNPlHZGAAAAwDkxUwoAKgYv1pQyQ4lmSi1evFjLly9X06ZNHduaNWumadOmsdA5AAAAKg+fILMTAABKA6fvmaJEM6Xsdrs8PT2LbPf09JTdbr/sUAAAAIBLqBJqdgIAQGng9D1TlKiUuvHGG/Xkk0/qyJEjjm2HDx/WU089pe7du5daOAAAAMCp+VFKAUCFwNX3TFGiUurdd9+V1WpVvXr11KBBAzVo0ECRkZGyWq165513SjsjAAAA4Jz8qkmymJ0CAHC5mPlqihKtKVW7dm1t2rRJy5cv186dOyVJTZs2VVRUVKmGAwAAAJyau4fkGySdOmF2EgDA5fAPNztBpVSsmVIrV65Us2bNZLVaZbFYdNNNN2n48OEaPny42rVrp+bNm+u3334rq6wAAACA8+EUPgBwbb4hkoeX2SkqpWKVUlOmTNEjjzyigICAIvsCAwP16KOPatKkSaUWDgAAAHB6nPIBAK6tKrOkzFKsUuqPP/7QzTfffN79PXr0UGxs7GWHAgAAAFyGXzWzEwAALod/mNkJKq1ilVKJiYny9PQ8734PDw8dO3bsskMBAAAALqNKdbMTAAAuBzOlTFOsUqpWrVratm3befdv2bJFNWvWvOxQAAAAgMsIiDA7AQDgcjBTyjTFKqV69+6tl156SVlZWUX2nTp1Si+//LJuueWWUgsHAAAAOL2gumYnAABcDmZKmcajOINffPFFffvtt7ryyisVHR2txo0bS5J27typadOmyWaz6YUXXiiToAAAAIBTCq5ndgIAwOWglDJNsUqpsLAwrVmzRo8//rhGjx4twzAkSRaLRT179tS0adMUFsa0NwAAAFQilFIA4NqY8WqaYpVSklS3bl0tWrRIJ06c0J49e2QYhho1aqTg4OCyyAcAAAA4t6phkqeflJtpdhIAQElUa2h2gkqr2KVUgeDgYLVr1640swAAAACuKaiudGyH2SkAAMVVpYbkE2B2ikqrWAudAwAAADgHTuEDANfELClTUUoBAAAAl4tSCgBcU7X6Zieo1CilAAAAgMtFKQUAromZUqailAIAAAAuV2gjsxMAAEqCUspUlFIAAADA5QpvYXYCAEBJUEqZilIKAAAAuFz+NfKv4AQAcB0WNyk40uwUlRqlFAAAAFAawpqbnQAAUBzVGkmePmanqNQopQAAAIDSEH6V2QkAAMUR0crsBJUepRQAAABQGsJYVwoAXErNVmYnqPScvpQ6fPiw7r//flWrVk2+vr5q0aKFNm7c6NhvGIbGjBmjmjVrytfXV1FRUdq9e3ehY6SkpGjAgAEKCAhQUFCQhgwZovT09EJjtmzZouuvv14+Pj6qXbu2JkyYUC7PDwAAABUEM6UAwLXUbGl2gkrPqUupEydO6Nprr5Wnp6d++ukn/fnnn3rrrbcUHBzsGDNhwgS9/fbbmjFjhtatW6cqVaqoZ8+eysrKcowZMGCAtm/frmXLlmnhwoVatWqVhg4d6thvtVrVo0cP1a1bV7GxsZo4caLGjh2rmTNnluvzBQAAgAsLvVJy9zI7BQDgklikmlebHaLS8zA7wIW88cYbql27tmbNmuXYFhl5ZmV8wzA0ZcoUvfjii/rXv/4lSfr0008VFhamBQsWqH///tqxY4cWL16sDRs2qG3btpKkd955R71799abb76piIgIzZkzRzk5Ofr444/l5eWl5s2bKy4uTpMmTSpUXgEAAADn5e4phTaWEreanQQAcDHVGkreVc1OUek59Uyp77//Xm3bttVdd92lGjVqqHXr1vrggw8c++Pj45WQkKCoqCjHtsDAQHXo0EExMTGSpJiYGAUFBTkKKUmKioqSm5ub1q1b5xjTpUsXeXmd+c1Wz549tWvXLp04ceKc2bKzs2W1WgvdAAAAUMnVusbsBACAS8Ei507BqUupvXv3avr06WrUqJGWLFmixx9/XE888YQ++eQTSVJCQoIkKSwsrND9wsLCHPsSEhJUo0aNQvs9PDwUEhJSaMy5jnH2Y/zT+PHjFRgY6LjVrl37Mp8tAAAAXF6dTmYnAABcCtaTcgpOXUrZ7XZdc801+u9//6vWrVtr6NCheuSRRzRjxgyzo2n06NFKS0tz3A4ePGh2JAAAAJitTgezEwAALkVEa7MTQE5eStWsWVPNmjUrtK1p06Y6cOCAJCk8PFySlJiYWGhMYmKiY194eLiSkpIK7c/Ly1NKSkqhMec6xtmP8U/e3t4KCAgodAMAAEAlF1Jf8j/350cAgJNw95ZqtTE7BeTkpdS1116rXbt2Fdr2119/qW7dupLyFz0PDw/XihUrHPutVqvWrVunTp3yp0536tRJqampio2NdYxZuXKl7Ha7OnTo4BizatUq5ebmOsYsW7ZMjRs3LnSlPwAAAOCimC0FAM6tVhvJ09fsFJCTl1JPPfWU1q5dq//+97/as2eP5s6dq5kzZ2rYsGGSJIvFohEjRujVV1/V999/r61bt+qBBx5QRESE+vbtKyl/ZtXNN9+sRx55ROvXr9fq1asVHR2t/v37KyIiQpJ03333ycvLS0OGDNH27dv11VdfaerUqRo5cqRZTx0AAACuqnZHsxMAAC6k3nVmJ8BpHmYHuJB27dpp/vz5Gj16tF555RVFRkZqypQpGjBggGPMs88+q4yMDA0dOlSpqam67rrrtHjxYvn4+DjGzJkzR9HR0erevbvc3NzUr18/vf322479gYGBWrp0qYYNG6Y2bdooNDRUY8aM0dChQ8v1+QIAAKACqEMpBQBOrd61ZifAaRbDMAyzQ1QEVqtVgYGBSktLY32p017fnGx2BDipUa1DzY4AJ8X7Bs6H940z+MzhAmx50ut1pNwMs5MAAP7J3UsadYDT95yEU5++BwAAALgcdw/pirZmpwAAnEvENRRSToRSCgAAAChtDW40OwEA4FxYT8qpUEoBAAAApa1hlNkJAADnQinlVCilAAAAgNIWfpVUNcLsFACAs3n4SLU7mJ0CZ6GUAgAAAMpCw+5mJwAAnC2yi+TlZ3YKnIVSCgAAACgLjW4yOwEA4GxX3mx2AvwDpRQAAABQFurfILl5mJ0CAFCAUsrpUEoBAAAAZcEnkLVLAMBZhF8tBdYyOwX+gVIKAAAAKCtchQ8AnAOzpJwSpRQAAABQVvghCACcQ2Pej50RpRQAAABQVsKaSaGNzU4BAJWbf7gUcY3ZKXAOlFIAAABAWWre1+wEAFC5XdlDsljMToFzoJQCAAAAylLzO8xOAACVW7O+ZifAeVBKAQAAAGWpRhOpelOzUwBA5VSlhlT/BrNT4DwopQAAAICydhWzpQDAFFfdIbm5m50C50EpBQAAAJS15rebnQAAKqcWd5mdABdAKQUAAACUtdBGUlgLs1MAQOUSUl+6oq3ZKXABlFIAAABAebiK2VIAUK6uutPsBLgISikAAACgPFx9j2Th4zcAlJur7zY7AS6CfxUBAACA8hB4hdSgu9kpAKByqNky/9RpODVKKQAAAKC8XPOA2QkAoHK4ur/ZCXAJKKUAAACA8tK4l1SlhtkpAKBi8/CVWt1rdgpcAkopAAAAoLy4e/KDEgCUtavukHyDzU6BS0ApBQAAAJSnawaZnQAAKra2Q8xOgEtEKQUAAACUp2oNpLrXmZ0CACqmmi2lK9qYnQKXiFIKAAAAKG8seA4AZaPtQ2YnQDFQSgEAAADlrdm/JL9Qs1MAQMXiHSC1uMvsFCgGSikAAACgvHn6SO1Y8wQASlXL/pJXFbNToBgopQAAAAAztHtE8vAxOwUAVBycuudyKKUAAAAAM/hXl66+2+wUAFAxNLxJqtHU7BQoJkopAAAAwCydoiVZzE4BAK7vuhFmJ0AJUEoBAAAAZqneWGp0k9kpAMC1XdFOqned2SlQApRSAAAAgJk6RZudAABc27UjzE6AEqKUAgAAAMxUv6sU3sLsFADgmkIbS036mJ0CJUQpBQAAAJiN3/IDQMlc+4RkYW0+V0UpBQAAAJit+R35v+0HAFy6gFrS1feYnQKXgVIKAAAAMJubm3TDc2anAADX0vHfkrun2SlwGSilAAAAAGfQ/A6pRjOzUwCAa/APk9o+ZHYKXCZKKQAAAMAZWCzSDaPNTgEArqHLM5KXn9kpcJkopQAAAABn0fRWKfxqs1MAgHMLrie1edDsFCgFlFIAAACAs2C2FABc3A3Ps5ZUBUEpBQAAADiTJr2liNZmpwAA51SjmdTiLrNToJRQSgEAAADO5sYXzU4AAM7pxhfzr1iKCoG/SQAAAMDZNIzKvwEAzriindSkj9kpUIpcqpR6/fXXZbFYNGLECMe2rKwsDRs2TNWqVZO/v7/69eunxMTEQvc7cOCA+vTpIz8/P9WoUUPPPPOM8vLyCo355ZdfdM0118jb21sNGzbU7Nmzy+EZAQAAAOfR87+Sm4fZKQDAeXQfY3YClDKXKaU2bNig999/X1dfXfhqJE899ZR++OEHzZs3T7/++quOHDmiO+64w7HfZrOpT58+ysnJ0Zo1a/TJJ59o9uzZGjPmzDdzfHy8+vTpo27duikuLk4jRozQww8/rCVLlpTb8wMAAAAKqd5YajvE7BQA4Bya3CJFdjE7BUqZS5RS6enpGjBggD744AMFBwc7tqelpemjjz7SpEmTdOONN6pNmzaaNWuW1qxZo7Vr10qSli5dqj///FOff/65WrVqpV69euk///mPpk2bppycHEnSjBkzFBkZqbfeektNmzZVdHS07rzzTk2ePNmU5wsAAABIkm4YJfkGX3wcAFRkHj5Sz9fMToEy4BKl1LBhw9SnTx9FRRU+rz42Nla5ubmFtjdp0kR16tRRTEyMJCkmJkYtWrRQWFiYY0zPnj1ltVq1fft2x5h/Hrtnz56OYwAAAACm8AuRuo4yOwUAmOvaJ6XgemanQBlw+pPUv/zyS23atEkbNmwosi8hIUFeXl4KCgoqtD0sLEwJCQmOMWcXUgX7C/ZdaIzVatWpU6fk6+tb5LGzs7OVnZ3t+NpqtRb/yQEAAAAX0+5haeNHUvJfZicBgPIXWEe67imzU6CMOPVMqYMHD+rJJ5/UnDlz5OPjY3acQsaPH6/AwEDHrXbt2mZHAgAAQEXk7iH14LQVAJVUz9ckz6ITRVAxOHUpFRsbq6SkJF1zzTXy8PCQh4eHfv31V7399tvy8PBQWFiYcnJylJqaWuh+iYmJCg8PlySFh4cXuRpfwdcXGxMQEHDOWVKSNHr0aKWlpTluBw8eLI2nDAAAABR1ZQ/pyl5mpwCA8lX/BqnZbWanQBly6lKqe/fu2rp1q+Li4hy3tm3basCAAY7/9vT01IoVKxz32bVrlw4cOKBOnTpJkjp16qStW7cqKSnJMWbZsmUKCAhQs2bNHGPOPkbBmIJjnIu3t7cCAgIK3QAAAIAy0+dNyauq2SkAoHy4eUq9JpidAmXMqdeUqlq1qq666qpC26pUqaJq1ao5tg8ZMkQjR45USEiIAgICNHz4cHXq1EkdO3aUJPXo0UPNmjXTwIEDNWHCBCUkJOjFF1/UsGHD5O3tLUl67LHH9O677+rZZ5/VQw89pJUrV+rrr7/Wjz/+WL5PGAAAADifwCuk7i9JPz1rdhIAKHsdHpWqNzY7BcqYU8+UuhSTJ0/WLbfcon79+qlLly4KDw/Xt99+69jv7u6uhQsXyt3dXZ06ddL999+vBx54QK+88opjTGRkpH788UctW7ZMLVu21FtvvaUPP/xQPXv2NOMpAQAAAOfW7hGpVluzUwBA2QqOlLq9YHYKlAOLYRiG2SEqAqvVqsDAQKWlpXEq32mvb042OwKc1KjWoWZHgJPifQPnw/vGGXzmgBK3S+93ley5ZicBgDJgkR5cKNW7zuwgKAcuP1MKAAAAqFTCmkvXPmF2CgAoG20fopCqRCilAAAAAFfT5VkppIHZKQCgdAXWkW565eLjUGFQSgEAAACuxtNHunWqJIvZSQCg9Nw2VfL2NzsFyhGlFAAAAOCKIq/PvzoVAFQEre+XGtxodgqUM0opAAAAwFVFjZNqNDM7BQBcnqo1pZ7/NTsFTEApBQAAALgqTx+p34eSu7fZSeDkXv89W5ZxVo1YnOXY9neKXbd/lanqE08qYLxVd8/LVGK6/YLHGftLlizjrIVuTd5NLzRm5JIshbxhVe3JJzVnS+GrRM7bnqtbv8gsvSeGCsAi/etdySfQ7CAwgYfZAQAAAABchrDmUtTL0pLnzU4CJ7XhsE3vx+bo6rAzcxIycgz1+DxDLcPctfIBP0nSSz9n69YvMrX24Spys5x/vbLm1d20/PR9JMnjrKkOP+zK1dytuVo6sIp2H7froe9PqWdDd4X6uSkty9ALK7ML3RdQ52ipYZTZKWASZkoBAAAArq7jv6X63cxOASeUnmNowLen9MGtvgr2OVM0rT5o075UQ7P7+qpFmLtahLnrk76+2njErpXxtgse08NNCvd3c9xC/c78WLkj2a4b6rmrbYS77m3hqQBvi+JPGJKkZ5dl6fG2nqoTyI+hOC3iGqn7y2angIl4NwAAAABcncUi9Z0u+YaYnQROZtiiLPVp5KGo+oVPksnOM2SR5O1+ZpuPh+RmkX4/kHfBY+5OsSvirZOqP/WkBnybqQNpZ075axnmro1HbDpxylDsEZtO5RpqGOKm3w/kaVOCTU908CrNpwdX5lVVuvMjyd3T7CQwEaUUAAAAUBEE1JRunWp2CjiRL7flatNRm8ZHFV1zrOMV7qriJT23PFuZuYYycgz939Is2Qzp6EnjvMfsUMtds//lq8X3+2l6H1/FnzB0/awMnczOv0/Phh66/2pPtfsgXQ9+d0qf9PVVFS/p8R+zNKOPr6ZvzFXjd9N17ccZ2p504RlZqOBumSyF1Dc7BUxGKQUAAABUFM1uk9o+ZHYKOIGDaXY9uThLc+7wlY9H0fWhqldx07y7/PTDX7ny/+9JBb5+UqnZ0jU13eR2/uWk1KuRp+5q7qmrw9zVs6GHFg3wU2qWoa+3n1nQfOwNPtrzRFVtfdxftzf11PjfchQV6SFPd+nVVdn6fbCfHm7tqQcWnCqLpw5X0GqAdPVdZqeAE2ChcwAAAKAiufkN6egf0uFYs5PARLFHbUrKMHTN+xmObTZDWrXfpnfX5yj7xarq0cBDfz9RVcmZdnm4WRTkY1H4mydVv/mlz10I8rHoympu2pNy7qv27Uy26fOtudr8aBV9vDlHXeq6q3oVN93d3FMPfZ+lk9mGqnpfoAVDxVOtodR7otkp4CQopQAAAICKxMNLuvtT6f2uUmay2Wlgku6RHtr6eJVC2wZ/d0pNQt313LVecj9rOlTBQuUr4/OUlGHotsaX/mNieo6hv1PsGnh10WLJMAw9ujBLk3p4y9/LIptdyj3dXRX8aTv/mYKoiDx8pTtnSV5VLj4WlQKn7wEAAAAVTeAV0p0fSxb3i49FhVTV26KrargXulXxtKiab/52SZq1OUdrD+Xp7xS7Pt+So7vmndJTHb3UOPTM9033TzP07vocx9f/tzRLv+7L075Uu9YczNPtX2XK3c2ie68qulj1h5tyVd3Polsb5++7to6HVsbnae2hPE2OyVaz6m4K8mGWVKVy2ztSzavNTgEnwkwpAAAAoCKq31Xq/pK0fKzZSeCkdh23a/SKbKWcMlQvyE0vXO+lpzoWvjre3yl2JWeeOTXvkNWue/93SsdPGaruZ9F1ddy1dkgVVa9SeL5DYrpdr/2WrTVDzsyIaV/LXU938lafuadUo4pFn/T1LdsnCOfSKZp1pFCExTAMJkyWAqvVqsDAQKWlpSkgIMDsOE7h9c1MF8e5jWodanYEOCneN3A+vG+cwWcOFNtX90s7fjA7BYDKrMGN0oBvJDdmb6IwTt8DAAAAKrK+06VqjcxOAaCyCo7MP52YQgrnQCkFAAAAVGTeVaX+cySfQLOTAKhsvPyl/nMl32Czk8BJUUoBAAAAFV31xtLdn0luRRejBoCyYcmfqRnWzOwgcGKUUgAAAEBlUL+rdOtUs1MAqCy6Pic1u83sFHBylFIAAABAZdF6gNTlGbNTAKjoWt0vdRttdgq4AEopAAAAoDK58UWpBZdlB1BGGt7ErExcMkopAAAAoLL51zSp7rVmpwBQ0US0lu7+RHL3MDsJXASlFAAAAFDZeHhL93wuVWtodhIAFUVwpHTfPMmritlJ4EIopQAAAIDKyC9EGvCNVLWm2UkAuLoq1aWB30r+1c1OAhdDKQUAAABUViGR0gPfSX6hZicB4Ko8q0j3fSWF1Dc7CVwQpRQAAABQmVVvLA2cL/kEmp0EgKtx95Lu/lSq1cbsJHBRlFIAAABAZVfzamnA/yQvf7OTAHAVbh7SXbOlRlFmJ4ELo5QCAAAAINVuJ937peTha3YSAM7O4i7d8YHUpI/ZSeDiKKUAAAAA5Iu8Xrrns/xTcgDgXCxu0r+mSVfdYXYSVACUUgAAAADOaHST1O+j/FNzAOBsBYVUq3vNToIKglIKAAAAQGHNbpPunCW5eZqdBICzcBRS95mdBBUIpRQAAACAoprdJt3zueTubXYSAGajkEIZoZQCAAAAcG6Nb5buY/FzoFJz98qfOUkhhTJAKQUAAADg/BrcKA38VvIOMDsJgPLm5S/d97XUvK/ZSVBBUUoBAAAAuLC6naVBP0h+oWYnAVBefEOkB76XGnQzOwkqMEopAAAAABcX0Up6aLEUcIXZSQCUtYBa+f+/X9HG7CSo4CilAAAAAFya0EbSkCVSjeZmJwFQVqo1kh5aIlVvbHYSVAKUUgAAAAAuXeAV+TMoGnQ3OwmA0lazVf7/30G1zU6CSoJSCgAAAEDx+ATkL37c5kGzkwAoLY17Sw/+KFVh7TiUH0opAAAAAMXn7iHdOlW66RVJFrPTALgc1z4p3TNH8vY3OwkqGQ+zAwAAAABwYdc+KQXVleY/KuVlmZ0GQHG4e0m3TJFaDzA7CSopZkoBAAAAuDzN+0qDFkpVqpudBMCl8guVHvieQgqmopQCAAAAcPlqt5MeWSlFtDY7CYCLqdEs///Xup3MToJKzqlLqfHjx6tdu3aqWrWqatSoob59+2rXrl2FxmRlZWnYsGGqVq2a/P391a9fPyUmJhYac+DAAfXp00d+fn6qUaOGnnnmGeXl5RUa88svv+iaa66Rt7e3GjZsqNmzZ5f10wMAAAAqlqA6+ZeSv+YBs5MAOJ9GPaUhS6XgumYnAZy7lPr11181bNgwrV27VsuWLVNubq569OihjIwMx5innnpKP/zwg+bNm6dff/1VR44c0R133OHYb7PZ1KdPH+Xk5GjNmjX65JNPNHv2bI0ZM8YxJj4+Xn369FG3bt0UFxenESNG6OGHH9aSJUvK9fkCAAAALs/DW7rtHem2dyUPH7PTAChgcZdufFG67yvJu6rZaQBJksUwDMPsEJfq2LFjqlGjhn799Vd16dJFaWlpql69uubOnas777xTkrRz5041bdpUMTEx6tixo3766SfdcsstOnLkiMLCwiRJM2bM0HPPPadjx47Jy8tLzz33nH788Udt27bN8Vj9+/dXamqqFi9efEnZrFarAgMDlZaWpoCAgNJ/8i7o9c3JZkeAkxrVmsvM4tx438D58L5xBp854FKOxElfPyCl7jc7CVC5+YdL/T6UIq83OwlQiFPPlPqntLQ0SVJISIgkKTY2Vrm5uYqKinKMadKkierUqaOYmBhJUkxMjFq0aOEopCSpZ8+eslqt2r59u2PM2ccoGFNwDAAAAAAlENFKGvqL1PAms5MAlVdkF+mx3yik4JRcppSy2+0aMWKErr32Wl111VWSpISEBHl5eSkoKKjQ2LCwMCUkJDjGnF1IFewv2HehMVarVadOnTpnnuzsbFmt1kI3AAAAAP/gFyLd97V0w+j804cAlA+Lm9R1lDTwO8m/htlpgHNymVJq2LBh2rZtm7788kuzo0jKX4Q9MDDQcatdu7bZkQAAAADn5OYm3TBKGrxICmJxZaDMVaku3f+t1G10/v9/gJNyie/O6OhoLVy4UD///LOuuOIKx/bw8HDl5OQoNTW10PjExESFh4c7xvzzanwFX19sTEBAgHx9fc+ZafTo0UpLS3PcDh48eFnPEQAAAKjw6nSUHvtdanmv2UmAiqtBd+nR36QG3cxOAlyUU5dShmEoOjpa8+fP18qVKxUZGVlof5s2beTp6akVK1Y4tu3atUsHDhxQp06dJEmdOnXS1q1blZSU5BizbNkyBQQEqFmzZo4xZx+jYEzBMc7F29tbAQEBhW4AAAAALsInQLp9hnTXbMk32Ow0QMXh5S/dMlka+K0UUNPsNMAl8TA7wIUMGzZMc+fO1XfffaeqVas61oAKDAyUr6+vAgMDNWTIEI0cOVIhISEKCAjQ8OHD1alTJ3Xs2FGS1KNHDzVr1kwDBw7UhAkTlJCQoBdffFHDhg2Tt7e3JOmxxx7Tu+++q2effVYPPfSQVq5cqa+//lo//vijac8dAAAAqNCa3y7V7iDNf0yK/9XsNIBrq9NZ6vueFBJ58bGAE3HqmVLTp09XWlqabrjhBtWsWdNx++qrrxxjJk+erFtuuUX9+vVTly5dFB4erm+//dax393dXQsXLpS7u7s6deqk+++/Xw888IBeeeUVx5jIyEj9+OOPWrZsmVq2bKm33npLH374oXr27FmuzxcAAACoVAIipAe+k3q8Jrl7m50GcD0ePlKPV6UHf6SQgkuyGIZhmB2iIrBarQoMDFRaWhqn8p32+uZksyPASY1qHWp2BDgp3jdwPrxvnMFnDlRYyXukH56U9v9udhLANdRsJd3+vlSjidlJgBJz6plSAAAAACqJ0IbSgwulW9+WfILMTgM4Lw9f6cYXpYdXUEjB5VFKAQAAAHAOFovUZpAUvUFqfofZaQDn06iHNGyt1OUZyd2pl4gGLgmlFAAAAADn4l9DumuWdN88KbCO2WkA8wXUku7+TBowTwquZ3YaoNRQSgEAAABwTleenhXSKVpyY1YIKiE3j/zv/2HrpWa3mZ0GKHWUUgAAAACcl1cVqedr0uMxUiOujo1KpHYH6dFV+d//3v5mpwHKBKUUAAAAAOdX/UppwNfS/d9K1ZuanQYoO4F1pDs+kB5aIoU1NzsNUKaYAwsAAADAdTTsLtW/QYqdJf38XynzuNmJgNLhEyRd/7TU4VHJw9vsNEC5oJQCAAAA4Frc3KV2D0st7pJWTZTWvS/ZcsxOBZSMu7fU/hGpy/9JvsFmpwHKFafvAQAAAHBNPoFSj1el6A1SqwGSxd3sREAxWPKL1egN+etGUUihEqKUAgAAAODagutJfd/L/+G+5b2UU3B+jXpIQ3+W+n0oBdc1Ow1gGkopAAAAABVDtQbS7TOkYeulFndLFn7cgTOxSE1ukYb+Kg2YJ0W0NjsQYDrepQEAAABULKENpX4fSP9eJ111J+UUzGVxk67qJz2+Ruo/R4poZXYiwGmw0DkAAACAiqn6ldKdH0ldn5Vi3pW2fC3lZZmdCpWFm0f+mlHXPy2FNjI7DeCUKKUAAAAAVGzVG0u3vSPdOEba8GH+LTPZ7FSoqLyqSq3ulTr+WwqJNDsN4NQopQAAAABUDv7VpW6jpeuekrZ8KcW8JyXvMjsVKoqQ+lL7oflXgvQJMDsN4BIopQAAAABULp4+UpsHpWsGSXuW55/at/dXSYbZyeByLFKDblKHx/KvqGexmB0IcCmUUgAAAAAqJ4tFanRT/u3439Lmz6S4L6T0BLOTwdl5+Ust+0vtH81fuwxAiVBKAQAAAEC1BlLUWKnbi9LupdKmT/P/NGxmJ4PTsEiR1+efntf0VsmritmBAJdHKQUAAAAABdw9pCa982/Wo1LcHGnz59KJeLOTwSwh9aWW9+XPjAqqbXYaoEKhlAIAAACAcwmoKXX5P+n6p6UDa6Xt86U/v+P0vsrAO1Bq3ldqdZ9Up6PZaYAKi1IKAAAAAC7EYpHqdsq/3fy6dCAmv6Da8b2Unmh2OpQW3xCpca/8U/Pqd8tfEB9AmaKUAgAAAIBL5eYm1bs2/9ZrgrR/9emC6gcpI8nsdCiuqhFSkz75RVS96yQ3d7MTAZUKpRQAAAAAlISbW/7C15HXS73flI5slvYsz78djmWRdGcV2lhqfLPU9DapVpv8mXAATEEpBQAAAACXy81NuqJN/u2G56TMFGnvz9KeFfk31qEyj3+YVP+GM7eACJMDAShAKQUAAAAApc0vRLqqX/5NkhK2Sn+vlA6skw6ulTKPm5uvIvPyl+pee6aECmtmdiIA50EpBQAAAABlLbxF/u3a018n78kvpw6slQ6uk5L/MjWeSwtpIF3RVqrVNn+mWvjVkrun2akAXAJKKQAAAAAob6EN82+t78//OjMlv5w6HCslbJMSt0tpB8zN6Ix8Q/LXgSoooWpdkz8rDYBLopQCAAAAALP5hUiNe+XfCmSlSYl/SomnS6rEbVLSDikn3byc5cUnSKrRVKreWKre5PSfTaWAmmYnA1CKKKUAAAAAwBn5BEp1O+XfChiGZD0indgnpe7P//PE/jNfn0yQZJiTtzgsbvkLkAdekX8LqCUF15NCr8wvoaqGmZ0QQDmglAIAAAAAV2GxSIG18m+OBarOkpslpR6QTh6VMpOljOP5f2YelzLO/jNZyj4p5WWVVjDJO0DyDcwv03yCJN+gM3/6hhQuoAIiWPcJAKUUAAAAAFQYnj5S9Svzb5fCMKS87PxyKi9byjtV+GtbruTmnj+zqeDm7im5e+f/6eEtefjkF1Fu7mX73ABUOJRSAAA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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "category_counts = df['label'].value_counts()\n",
        "\n",
        "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
        "\n",
        "# Bar chart\n",
        "category_counts.plot(kind='bar', ax=ax[0], color='skyblue')\n",
        "ax[0].set_xlabel('Label')\n",
        "ax[0].set_ylabel('Counts')\n",
        "ax[0].set_title('Bar Chart of Counts')\n",
        "\n",
        "# Pie chart\n",
        "category_counts.plot(kind='pie', autopct='%1.1f%%', ax=ax[1])\n",
        "ax[1].set_ylabel('')  # Remove default y-axis label\n",
        "ax[1].set_title('Pie Chart of Distribution')\n",
        "ax[1].axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
        "\n",
        "# Add legend for pie chart\n",
        "ax[1].legend()\n",
        "\n",
        "# Display the plots\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x8HvF6zKz8e6",
        "outputId": "dde4d0c6-b2b9-4a41-8a58-bdf2f0f9e706"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total size:  27977\n",
            " Duplicates: 5\n",
            "Missing Values by column:\n",
            " text     0\n",
            "label    0\n",
            "dtype: int64\n",
            "Missing Values: 0\n"
          ]
        }
      ],
      "source": [
        "print('total size: ', df.shape[0])\n",
        "\n",
        "# Do we have duplicates?\n",
        "print(' Duplicates:', len(df[df.duplicated()]))\n",
        "\n",
        "# Do we have missing values?\n",
        "missing_values = df.isnull().sum()\n",
        "print('Missing Values by column:\\n',missing_values)\n",
        "\n",
        "print('Missing Values:', df.isnull().sum().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNrrdbDIz8e6",
        "outputId": "4e218c8c-5760-4a34-9ad8-95da1c891535"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values and Empty Spaces by column:\n",
            " text     0\n",
            "label    0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "df.replace(\"\", np.nan, inplace=True)\n",
        "missing_values = df.isnull().sum()\n",
        "print('Missing Values and Empty Spaces by column:\\n',missing_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "waHdL8Ymz8e7",
        "outputId": "26122617-5a9f-4737-c519-c8beda8f2f4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text  label\n",
              "15524  happy birthday everyone birthday st october ha...      0\n",
              "24502  happy birthday everyone birthday st october ha...      0\n",
              "16742  need help anyone good pythagriam tribometry  h...      0\n",
              "24970  need help anyone good pythagriam tribometry  h...      0\n",
              "1646                 posting ara ara forget day  ara ara      0\n",
              "22603                posting ara ara forget day  ara ara      0\n",
              "11570                  real suppleroot hours  up day far      0\n",
              "12573                  real suppleroot hours  up day far      0\n",
              "22389                  real suppleroot hours  up day far      0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc7ea799-4532-427d-bec1-59c18ec252bf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
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              "      <td>posting ara ara forget day  ara ara</td>\n",
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              "      <th>11570</th>\n",
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              "      <td>0</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc7ea799-4532-427d-bec1-59c18ec252bf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bc7ea799-4532-427d-bec1-59c18ec252bf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bc7ea799-4532-427d-bec1-59c18ec252bf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7adbd94c-9933-4cbd-9a93-35589457c983 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_6590b600-581f-448f-8dde-02d1b8216888\">\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('top_5_duplicate_pairs')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#id_6590b600-581f-448f-8dde-02d1b8216888 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('top_5_duplicate_pairs');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_5_duplicate_pairs",
              "summary": "{\n  \"name\": \"top_5_duplicate_pairs\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"need help anyone good pythagriam tribometry  half hours need help discord ahappysleepdeprivednerd someone could help get done would great\",\n          \"real suppleroot hours  up day far\",\n          \"happy birthday everyone birthday st october happy birthday everyone birthday st octoberhappy birthday everyone birthday st october\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "duplicate_rows = df[df.duplicated(keep=False)]\n",
        "\n",
        "# Then sort the dataframe on all columns to ensure duplicates are adjacent\n",
        "sorted_duplicates = duplicate_rows.sort_values(by=list(duplicate_rows.columns))\n",
        "\n",
        "# Now, if we want to see 5 pairs of duplicates (10 rows), we can simply:\n",
        "top_5_duplicate_pairs = sorted_duplicates.head(20)\n",
        "\n",
        "top_5_duplicate_pairs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0e5Twwnuz8e7",
        "outputId": "09f7ba64-8403-4471-bee8-ad4d2185df8a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicates: 0\n"
          ]
        }
      ],
      "source": [
        "df = df.drop_duplicates()\n",
        "print('Duplicates:', len(df[df.duplicated()]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8_HgE0Wz8e8",
        "outputId": "613e70e1-861c-4cd4-c661-1c06381ed749"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values: 0\n"
          ]
        }
      ],
      "source": [
        "df = df.dropna()\n",
        "print('Missing Values:', df.isnull().sum().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "UxflJ7WDz8e9"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import string"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "51x8QpuVz8e9"
      },
      "outputs": [],
      "source": [
        "# cleaning data\n",
        "\n",
        "def strip_emoji(text):\n",
        "    emoji_pattern = re.compile(\"[\"\n",
        "        u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
        "        u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
        "        u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
        "        u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
        "        u\"\\U00002500-\\U00002BEF\"  # chinese characters\n",
        "        u\"\\U00002702-\\U000027B0\"\n",
        "        u\"\\U00002702-\\U000027B0\"\n",
        "        u\"\\U000024C2-\\U0001F251\"\n",
        "        u\"\\U0001f926-\\U0001f937\"\n",
        "        u\"\\U00010000-\\U0010ffff\"\n",
        "        u\"\\u2640-\\u2642\"\n",
        "        u\"\\u2600-\\u2B55\"\n",
        "        u\"\\u200d\"\n",
        "        u\"\\u23cf\"\n",
        "        u\"\\u23e9\"\n",
        "        u\"\\u231a\"\n",
        "        u\"\\ufe0f\"  # dingbats\n",
        "        u\"\\u3030\"\n",
        "                           \"]+\", flags=re.UNICODE)\n",
        "    return emoji_pattern.sub(r'', text)\n",
        "\n",
        "#Remove punctuations, links, mentions and \\r\\n new line characters\n",
        "def strip_all_entities(text):\n",
        "    text = text.replace('\\r', '').replace('\\n', ' ').replace('\\n', ' ').lower() #remove \\n and \\r and lowercase\n",
        "    text = re.sub(r\"(?:\\@|https?\\://)\\S+\", \"\", text) #remove links and mentions\n",
        "    text = re.sub(r'[^\\x00-\\x7f]',r'', text) #remove non utf8/ascii characters such as '\\x9a\\x91\\x97\\x9a\\x97'\n",
        "    banned_list= string.punctuation + 'Ã'+'±'+'ã'+'¼'+'â'+'»'+'§'\n",
        "    table = str.maketrans('', '', banned_list)\n",
        "    text = text.translate(table)\n",
        "    return text\n",
        "\n",
        "#clean hashtags at the end of the sentence, and keep those in the middle of the sentence by removing just the # symbol\n",
        "def clean_hashtags(text):\n",
        "    new_text = \" \".join(word.strip() for word in re.split('#(?!(?:hashtag)\\b)[\\w-]+(?=(?:\\s+#[\\w-]+)*\\s*$)', text)) #remove last hashtags\n",
        "    new_text2 = \" \".join(word.strip() for word in re.split('#|_', new_text)) #remove hashtags symbol from words in the middle of the sentence\n",
        "    return new_text2\n",
        "\n",
        "#Filter special characters such as & and $ present in some words\n",
        "def filter_chars(a):\n",
        "    sent = []\n",
        "    for word in a.split(' '):\n",
        "        if ('$' in word) | ('&' in word):\n",
        "            sent.append('')\n",
        "        else:\n",
        "            sent.append(word)\n",
        "    return ' '.join(sent)\n",
        "\n",
        "def remove_mult_spaces(text): # remove multiple spaces\n",
        "    return re.sub(\"\\s\\s+\" , \" \", text)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "1GmtK8YKz8e-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "994e556f-554c-440f-c5ed-d1a6649bd2da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  label  \\\n",
              "0  dear american teens question dutch person hear...      0   \n",
              "1  nothing look forward lifei dont many reasons k...      1   \n",
              "2  music recommendations im looking expand playli...      0   \n",
              "3  im done trying feel betterthe reason im still ...      1   \n",
              "4  worried  year old girl subject domestic physic...      1   \n",
              "\n",
              "                                              c_text  \n",
              "0  dear american teens question dutch person hear...  \n",
              "1  nothing look forward lifei dont many reasons k...  \n",
              "2  music recommendations im looking expand playli...  \n",
              "3  im done trying feel betterthe reason im still ...  \n",
              "4  worried year old girl subject domestic physica...  "
            ],
            "text/html": [
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              "  <div id=\"df-07233719-363e-421c-821f-89d0006542fe\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
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              "      <th>0</th>\n",
              "      <td>dear american teens question dutch person hear...</td>\n",
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              "      <td>worried year old girl subject domestic physica...</td>\n",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07233719-363e-421c-821f-89d0006542fe')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-07233719-363e-421c-821f-89d0006542fe button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-07233719-363e-421c-821f-89d0006542fe');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0cc2540b-f203-4569-8f0e-b1f368079fdc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0cc2540b-f203-4569-8f0e-b1f368079fdc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0cc2540b-f203-4569-8f0e-b1f368079fdc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 27972,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27972,\n        \"samples\": [\n          \"im ready gothanks previous person commented last post pushing edge mum work tomorrow thursday friday thats ill it creating set tomorrow week nothing ever goes way life ive ruined childhood trauma effects life result abandoned everybody ive ever loved even change heart still need go current situation thats it im going die soon\",\n          \"atoms ashim posting im anonymous molested  older sister cover secret played liar entire childhood put bad spot emotionally constantly getting trouble parents school bullied rediculed friends harrassed elementary school students teachers put medication rd grade everyday pulled classroom sit principal office would come home crying everyday mistreatment mom finally enough pulled public school transferred private christian school behind education next year retake rd grade students bullied started fight back aggressively also landed principals office th grade transferred back public school made friends bullies bad kids class want bullied anymore tough guy got girlfriend short lived ended writing notes wanted fuck bunch sexual stuff soon found grandma called principal school tried press charges minor gone puberty yet really understand ramifications saying grip parents tightened medication forced throat th grade teacher slapped across face class shut class still remember bead sweat rolling mustache red angry face slapped me made turn back hardened asshole teachers care school point wanted run away several times could never make friends felt like big fucking joke me fist fights recess involved somebody else typically started kids corned back fence friends said fuck friends need them th grade proceeded get girlfriend become sexually active th grade high school began relationship started take turn eventually ended longest friendship entire life devastating ended began get suicidal depression tried overdose medication class caught red handed taking entire bottle pills became violently ill blacked bathroom found carried nurses office school principal called ambulance contacted mom mom devastated pissed crying uncontrollably lied said accident switched meds antidepressants later stopped taking gag reflex overdose went  long term relationship breakups ended cutting arms burning lighter pulled class senior year high school arrested front whole school shooting front school paintball gun night before prank someone would clean charged  different things including terrorism absolutely drive become anything life think going live past  academically barely graduated high school convinced pull shit together go college failed college blew grant money food diamonds girlfriend relationship ended joined marine corps go die country iraq opf fallujah ex girlfriend came see california led wanted get back knew knew company getting deployed iraq fight real full metal jacket mental breakdown weeks deployed threatened kill bunch people landed hospital diagnosis ptsd bipolar major depression fraudulent enlistment charge given discharge honorable conditions plane ticket home tried go back school failed racked another bill year discharge girlfriend broke up friends considered failure everything let love life walk away soon keep depression control jumped relationship first girl would talk gas station convinced looks everything chick nice funny showed attention wanted cock cake care fucked head son already whose father jail decided would pull shit together roll model later started self medicating weed really cop felt great high time friends wifes friends supplying me temper really chilled smoked manic didnt filled uncontrollable anxietydepression rage often super asshole little man syndrome didnt smoke often thought redflag option stop internal pain instead saying loud would impulsively things hurt myself punched structure point shattered wrist several fingers felt better little bit marriage ended year later point interest forming new relationships anyone tired looking new connects weed moved around different states legal dispensaries soon realized weed helping actually making things worse control anymore desperate plea help sister gave place stay moving sister family found work started busting ass make things work could get place entire focus work smoking weed numb pain living often would isolate away everyone else could smoke weed cigarettes really acceptable lifestyle eventually led renting room house full strangers closer work work roommates knowing weed would go room steal weed play instruments work put huge panic major anxiety depression eventually leave situation move back sister brother law take well year started throwing offensive false accusations sexually since interested dating divorce nephews would repeat said friends friends parents soon everyone treated fucked way made things even worse instead giving getting defensive fucking talking shit decided pack shit leave found cheap place live way woods away everyone else keep myself working employer  years started feel little comfortable around coworkers opened little relate mentioned high school paintballing incident normal conversation conflicts employer soon started take place coworker told boss paintballing played sociopath wanted job pay concerned thought boss knew well enough believe later sit boss talk future goals told goal make better future plans start business real grown shit like one bit fact retaliated cutting hours bonus pay singled rest crew made work jobs less time complete pressure unbearable redflag plate everyday every hour constantly mind started feel ill overwhelming uncontrollable feelings panic attack quit job health insurance afford cost housing car payments insurance cellphone bill ended going doctor heart would beat chest throwing every morning years wanted know why turns heart enlarged may diabetes stage  hypertension might heart attack stroke cant relieve stress take care myself got way refused take medications instead still self medicating stop weed without losing mind emotionally stopped smoking weedcigs mt dew per doctors request save life anxiety debilitating obsess death cant hold conversation cant share eye contact feel extremely uncomfortable every social situation encounter cant bring back normal feel like normal wanting kill ending existence im running short money bank feel like work people fucking minion worker slave would rather die life completely closed feel though time running out well self control hide painful memories mom traveled  miles past slipped said going kill extremely worried depressed feels like lose child even tho ive said nothing recently actually moved woods far away everyone kill myself nobody would find me cant sleep anymore mind always thinking ways go every tree look at see hanging every gun looks see blood scattered wall body slumped over every tall building see body bouncing pavement every light pole see think truck wrapped around body half way hood windshield go jog try clear mind feel urge step front car semi head hurts cant help punch face burn propane torch relieve pain extremely difficult time analyzing events life absolutely hating people theyve done me hating unable forgive forget move on shitty things ive said done people control myself thought redflag might parents kept here feel hard even around family hour without irritated rude hate everything me starting understand everyone else hates too hurtful things go away cannot help desperately want become ash float away space dot planet insignificant every way mankind nothing offer feel ashamed illness feel like waste space oxygen pray god blow heart chest mom dad would live fact son willingly killed himself\",\n          \"wanna use omegle cus self esteems low ppl always compliment saw ppl saying got doxxed using omegle shit uhh meh oh well\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27970,\n        \"samples\": [\n          \"got job guess excited ready kill soonan uncle mine recently helped get job works at know going go already tell going make want kill myself going get morning im morning person so going either use buy method use now actually great motivation hurry up going write short story book seem happening fulltime cant focus time actually want fuck it lets get with\",\n          \"enough shitty placeive enough im sick peoples shit got denied poor peoples health insurance dependents year late slashed budget last year yay adults without dependents get health insurance paid social security number years deal get social security payments years theyre out deny help yay thats great way cut budget costs im sure smarky mba thought budget scheme weasels infinite wisdom lol going stand living world another week longer denied beggar lowest sink to sink lower die now fuck them\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df['c_text'] = (df['text']\n",
        "                     .apply(strip_emoji)\n",
        "                     .apply(strip_all_entities)\n",
        "                     .apply(clean_hashtags)\n",
        "                     .apply(filter_chars)\n",
        "                     .apply(remove_mult_spaces))\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "BhaBGQe1z8e_"
      },
      "outputs": [],
      "source": [
        "def remove_stopwords(sentence):\n",
        "    # List of stopwords\n",
        "    stopwords = [\"a\", \"about\", \"above\", \"after\", \"again\", \"against\", \"all\", \"am\", \"an\", \"and\", \"any\", \"are\", \"as\", \"at\", \"be\", \"because\", \"been\", \"before\", \"being\", \"below\", \"between\", \"both\", \"but\", \"by\", \"could\", \"did\", \"do\", \"does\", \"doing\", \"down\", \"during\", \"each\", \"few\", \"for\", \"from\", \"further\", \"had\", \"has\", \"have\", \"having\", \"he\", \"he'd\", \"he'll\", \"he's\", \"her\", \"here\", \"here's\", \"hers\", \"herself\", \"him\", \"himself\", \"his\", \"how\", \"how's\", \"i\", \"i'd\", \"i'll\", \"i'm\", \"i've\", \"if\", \"in\", \"into\", \"is\", \"it\", \"it's\", \"its\", \"itself\", \"let's\", \"me\", \"more\", \"most\", \"my\", \"myself\", \"nor\", \"of\", \"on\", \"once\", \"only\", \"or\", \"other\", \"ought\", \"our\", \"ours\", \"ourselves\", \"out\", \"over\", \"own\", \"same\", \"she\", \"she'd\", \"she'll\", \"she's\", \"should\", \"so\", \"some\", \"such\", \"than\", \"that\", \"that's\", \"the\", \"their\", \"theirs\", \"them\", \"themselves\", \"then\", \"there\", \"there's\", \"these\", \"they\", \"they'd\", \"they'll\", \"they're\", \"they've\", \"this\", \"those\", \"through\", \"to\", \"too\", \"under\", \"until\", \"up\", \"very\", \"was\", \"we\", \"we'd\", \"we'll\", \"we're\", \"we've\", \"were\", \"what\", \"what's\", \"when\", \"when's\", \"where\", \"where's\", \"which\", \"while\", \"who\", \"who's\", \"whom\", \"why\", \"why's\", \"with\", \"would\", \"you\", \"you'd\", \"you'll\", \"you're\", \"you've\", \"your\", \"yours\", \"yourself\", \"yourselves\" ]\n",
        "\n",
        "    # Sentence converted to lowercase-only\n",
        "    sentence = sentence.lower()\n",
        "\n",
        "    words = sentence.split()\n",
        "    no_words = [w for w in words if w not in stopwords]\n",
        "    sentence = \" \".join(no_words)\n",
        "\n",
        "    return sentence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "0AW53yx5z8fA"
      },
      "outputs": [],
      "source": [
        "df['c_text2'] = (df['c_text'].apply(remove_stopwords))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JcAv8ZiTz8fA",
        "outputId": "a05a35df-0c7d-4a71-fee3-d8f2d0e1688d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')\n",
        "\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "def lemmatize_text(text):\n",
        "    # Tokenize the sentence\n",
        "    word_list = nltk.word_tokenize(text)\n",
        "\n",
        "    # Lemmatize list of words and join\n",
        "    lemmatized_output = ' '.join([lemmatizer.lemmatize(w) for w in word_list])\n",
        "\n",
        "    return lemmatized_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "A4nvmBuCz8fA"
      },
      "outputs": [],
      "source": [
        "df['c_text3'] = df['c_text2'].apply(lemmatize_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "hKhmcGPvz8fB"
      },
      "outputs": [],
      "source": [
        "df['text_length'] = df['c_text3'].apply(lambda x: len(str(x).split()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUW4fulYz8fC",
        "outputId": "50acb4f9-4bf2-4ddd-e70d-e2a2a3697b4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "95th Quartile of Text Lengths: 231.0\n"
          ]
        }
      ],
      "source": [
        "text_lengths = [len(text.split()) for text in df[\"c_text3\"]]\n",
        "\n",
        "# Find the 95th quartile\n",
        "quartile_95 = np.percentile(text_lengths, 95)\n",
        "\n",
        "print(f\"95th Quartile of Text Lengths: {quartile_95}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "_DvLd3EPz8fC",
        "outputId": "e2905483-b166-431f-aa97-17ca1163a1bb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(text_lengths, bins=20, edgecolor='black')\n",
        "plt.xlabel('Word Length')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Text Lengths')\n",
        "\n",
        "# Adding a vertical line for the 95th quartile\n",
        "quartile_95 = np.percentile(text_lengths, 95)\n",
        "plt.axvline(x=quartile_95, color='red', linestyle='--', label='95th Quartile')\n",
        "plt.legend()\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "-_Qv1SVez8fF"
      },
      "outputs": [],
      "source": [
        "df = df.drop(['text', 'c_text', 'c_text2'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "J_HokOffz8fH",
        "outputId": "046bd799-a047-4655-f488-3680826d6342"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "0    14134\n",
              "1    13838\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13838</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df.label.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "yVDog6jez8fI",
        "outputId": "80fbd382-673a-469d-942b-389029d570de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Label  Label Encoded\n",
              "0  Non-mental-health              0\n",
              "1      Mental-health              1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-add2173f-2a79-48f4-985c-dc2ad976277c\" class=\"colab-df-container\">\n",
              "    <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>Label</th>\n",
              "      <th>Label Encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Non-mental-health</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mental-health</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-add2173f-2a79-48f4-985c-dc2ad976277c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-add2173f-2a79-48f4-985c-dc2ad976277c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-add2173f-2a79-48f4-985c-dc2ad976277c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1d156bfa-a179-4e51-9ef1-120e41404641\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1d156bfa-a179-4e51-9ef1-120e41404641')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1d156bfa-a179-4e51-9ef1-120e41404641 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_6d7bd0e4-cc72-41b0-99bc-3ea3e7d58210\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dr')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6d7bd0e4-cc72-41b0-99bc-3ea3e7d58210 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dr');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dr",
              "summary": "{\n  \"name\": \"dr\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Mental-health\",\n          \"Non-mental-health\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label Encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "data = {\n",
        "    'Label': [\"Non-mental-health\", \"Mental-health\"],\n",
        "    'Label Encoded': [0,1]\n",
        "}\n",
        "\n",
        "dr = pd.DataFrame(data)\n",
        "dr"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['text_length'], axis=1)"
      ],
      "metadata": {
        "id": "Btdpka7eGKhC"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bceKURpJ2MsI",
        "outputId": "a5af7be8-7fbb-4316-fd77-91bcc7e19e70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame saved\n"
          ]
        }
      ],
      "source": [
        "df.to_csv(\"clean.csv\", index=False)\n",
        "\n",
        "print(f\"DataFrame saved\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "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": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}