Tictanic.ipynb

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    "<center><h1> **প্রজেক্ট বিস্তারিত **</h1></center>\n",
    "#প্রজেক্ট বিস্তারিত \n",
    "\n",
    "\n",
    "শুরুর কথা । \n",
    "এখানে এসেছেন যেহেতু সেহেতু আপনি জানেন ডেটা সাইন্স/ মেশিন লার্নিং কি । কিংবা না জানলেও আগ্রহ জমেছে আপনার । \n",
    "আগ্রহ যেহেতু জমেছেই তাহলে একবার ঢু মেরে দেখে ফেলুন পুরোটা । \n",
    "বর্তমানে ডেটা সাইন্স আসলে আর কম্পিউটার সাইন্স রিলেটেড নয় । আসলে কখনোই ছিলো না । আমরা ভাবতাম আরকি । ডেটা সাইন্সের এই ফিল্ডটা আসলে হিউজ । তো ডেটা সাইন্স কেন দরকারী তা জানতে নিচের ভিডিওটা দেখে নিতে পারেন । \n",
    "<p>\n",
    "<a href=\"https://www.youtube.com/watch?v=PXLVLS1vJHY\">Is Data Science Really a Rising Career in 2020?</a>\n",
    " <p>\n",
    "এসম্পর্কিত আমার লেখা ব্লগ পড়তে চাইলে <a href=\"https://dspolok.blogspot.com/2020/06/blog-post_27.html\"> কেন ডেটা সাইন্সে আসবেন? </a>\n",
    " <p>\n",
    "এবার বলে নেই কেন প্রজেক্ট টাইটানিক । আসলে প্রজেক্ট টাইটানিককে বলা যায় ডেটা সাইন্সের Hello World! প্রজেক্ট । প্রতিটা প্রোগ্রামিং ল্যাঙ্গুয়েজেরই হয়তো প্রথম প্রোগ্রাম এক্সাম্পলটা হয় এই Hello World! এর । ডেটা সাইন্সের ফিল্ডেও সবাই এই প্রোজেক্টটা দিয়েই শুরু করে কমবেশি । \n",
    "আমরা এই প্রজেক্টে ল্যাঙ্গুয়েজ হিসেবে ইউজ করবো পাইথন । আর IDE হিসেবে Jupyter Notebook.\n",
    "<p>To install python: <a href=\"https://www.python.org/downloads/\">Python</a></p>\n",
    "<p>To install Jupyter Notebook: <a href=\"https://jupyter.org/install\">Jupyter Notebook</a></p>\n",
    "\n",
    "বিঃদ্রঃ প্রজেক্টটি সবচেয়ে সুন্দর ভাবে ভিজুয়ালাইজ করতে জুপিটার নোটবুক ইন্সটল করুন । এরপরে এই প্রোজেক্ট ডাউনলোড করে ওপেন করে দেখুন । প্রতিটা সেল নিজে রান করে করে দেখুন । \n",
    "এতে শিখতে সুবিধা হবে । আর এখানে ব্যাখ্যার জন্য কিছু ইমেজ ফাইল এড করা আছে এবং ক্যাগলে যে সাবমিশন করেছি সেগুলোর রেজাল্টও কিন্তু স্ক্রিনশট আকারে এড করে দেখিয়েছি যা গিটহাবে শো করবে না । "
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    "#আমার পরিচয়\n",
    "<p>নামঃ মোঃ হাসিবুল ইসলাম পলক\n",
    "<p>গ্রাজুয়েট, কম্পিউটার সাইন্স ,<a href=\"www.iubat.edu\">IUBAT</a>\n",
    "<p>ডেটা সাইন্সে একজন নিউবি । চেষ্টা করছি ভালো কিছু করার । আশা করি আপনাদের সহযোগিতা পাবো । \n",
    "<p>যোগাযোগঃ <a href=\"mailto:polok.hasibul@gmail.com\"> Email </a>"
   ]
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set() # Apply the default seaborn theme, scaling, and color palette\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
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   "source": [
    "প্রথমেই আমরা দরকারি লাইব্রেরী গুলী ইমপোর্ট করে লোড করে নেই । যেকোন একটা সেল রান করতে কিবোর্ডে (কন্ট্রোল+এন্টার) চাপতে হবে ।\n",
    "এখানে লাইব্রেরী গুলির কাজ সংক্ষেপে বলে দেই । \n",
    "<li>১ । matplotlib- এর কাজ হলো ডেটা গুলোকে গ্রাফ আকারে শো করা । এতে করে ডেটা গুলির ডিস্ট্রিবিউশন এবং পরস্পরের সাথে সম্পর্ক বোঝা যায় । To know more <a href=\"https://matplotlib.org/\">Matplotlib</a></li>\n",
    "<li>২ । seaborn- একই কাজ করে তবে এইটা  matplotlib এর উন্নত ভার্সন বলা যায় । To know more <a href=\"https://seaborn.pydata.org/\">Seaborn</a></li>\n",
    "<li>৩ । pandas- আমাদের ডেটাসেট নিয়ে কাজ করতে হলে ডেটাকে একটা ফ্রেমে নিয়ে আসতে হবে । সেই ফ্রেম থেকে আমরা নানান ধরনের দরকারি মিউটেশন করতে পারবো ডেটাতে । সেই কাজটি সহজ করে দিয়েছে এই লাইব্রেরী । To know more <a href=\"https://pandas.pydata.org/\">Pandas</a></li>\n",
    "<li>৪ । numpy- এইটা দিয়ে আসলে স্ট্যাটিস্টিক্স এর ব্যাসিক অনেক দরকারি ফর্মুলা গুলো ইজিলি এপ্লাই করা যায় । এবং ডেটাফ্রেম কে অ্যারে আকারে নিয়ে অনেক ক্যাল্কুলেশনেও হেল্প করে । পরে আমরা এর এপ্লিকেশন দেখবো । To know more <a href=\"https://numpy.org/\">Numpy</a></li>\n",
    "<li>৫ । warnings- অনেক সময় জুপিটার নোটবুকের অনেক লাইব্রেরী ভার্সন কমপ্লেক্সিটির ওয়ার্নিং দেয় । ঐগুলো এড়ানোর জন্য এই লাইব্রেরী ইউজ করা । To know more <a href=\"https://docs.python.org/3/library/warnings.html\">Warning</a></li>\n"
   ]
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   "source": [
    "এই প্রোজেক্টে আসলে আমরা কি করবো? এই প্রজেক্টে আমাদের কাজ হচ্ছে টাইটানিকের যাত্রীদের তথ্য থেকে প্রেডিক্ট করা যে\n",
    "সে বেঁচে গিয়েছিলো নাকি মারা গিয়েছিলো । অর্থাৎ একজন এর তথ্য থেকে আমরা প্রেডিক্ট করবো সে কি মারা গিয়েছিলো নাকি বেঁচে গিয়েছিলো । \n",
    "ডেটাসেট পাবো কোথায়? \n",
    "এখানেঃ<p> <a href=\"https://www.kaggle.com/c/titanic/data\">Titanic </a> </p>\n",
    "এখান থেকে ডেটা ডাউনলোড করে লোড করে নেবো পান্ডাস ডেটাফ্রেমে । \n",
    "মনে রাখতে হবে আপনার জুপিটার নোটবুক ফাইলটি যেখানে আছে ঠিক একই ডিরেক্টরি তে এই ডেটাসেট এর ফাইলটিও থাকতে হবে । \n",
    "আমাদের এখানে ডেটা সেট মোট তিনটি ভাগে ভাগ করা । \n",
    "<ul> train.csv- এই ডেটাসেট দিয়ে আমাদের মডেলকে ট্রেইন করবো । </ul>\n",
    "<ul> test.csv- এই ডেটাসেট দিয়ে আমরা আমাদের মডেলকে টেস্ট করবো । </ul>\n",
    "<ul> gender_submission.csv- আপাতত আমরা এই ফাইলটি পাশে সরিয়ে রাখি । </ul>"
   ]
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   "metadata": {},
   "source": [
    "<center><h1> **ডেটা কালেকশন **</h1></center>\n",
    "#ডেটা কালেকশন \n",
    "\n",
    "\n",
    "ডেটা কালেকশন রিয়েল ওয়ার্ল্ড প্রজেক্টে হিউজ একটা ব্যাপার । কিন্তু এখানে মাশাআল্লাহ আমাদের ডেটা রেডি করা আছে । "
   ]
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  {
   "cell_type": "code",
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   "metadata": {
    "scrolled": false
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "df.head()"
   ]
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   "source": [
    "এবার ডেটাসেটের একটা ডেস্ক্রিপশন দেখে নেই । \n",
    "<ul>PassengerId- যাত্রীর ক্রমিক নং </ul>\n",
    "<ul>Survived- যাত্রী বেঁচে ছিলেন নাকি মারা গিয়েছিলেন । ১ মানে বেঁচে গিয়েছিলেন, ০ মানে মারা গিয়েছিলেন । </ul>\n",
    "<ul>Pclass-টিকেট এর ক্লাস । ১ মানে ফার্স্ট ক্লাস, ২ মানে সেকেন্ড ক্লাস, ৩ মানে থার্ড ক্লাস ।</ul>\n",
    "<ul>Name- যাত্রীর নাম </ul>\n",
    "<ul>Sex- সে পুরুষ নাকি মহিলা </ul>\n",
    "<ul>Age- বয়স কতো ছিলো তার</ul>\n",
    "<ul>SibSp-শিপে তখন তার ভাইবোন কতজন ছিলো </ul>\n",
    "<ul>Parch-বাবা মা ছিলেন কিনা, বা ভাইবোন কতোজন ছিলেন । মানে সব মিলিয়ে বাবা-মা, ভাই-বোন কতজন ছিলেন ঐ শিপে ঐ যাত্রীর । যারা গৃহপরিচারিকার সাথে ছিলো অথবা বাবা-মা, ভাই-বোন কেউ ছিলেন না তাদের জন্য ০ দেয়া । </ul>\n",
    "<ul>Ticket- টিকেট নাম্বার</ul>\n",
    "<ul>Fare-ভাড়া কত ছিলো </ul>\n",
    "<ul>Cabin-কত নাম্বার কেবিনে ছিলো । </ul>\n",
    "<ul>Embarked- কোন পোর্ট থেকে উঠেছিলো । এখানে C = Cherbourg, Q = Queenstown, S = Southampton</ul>\n",
    "এবার দেখে নেই কোন মিসিং ভ্যালু আছে কিনা আমাদের ডেটাসেট এ ।"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1> **ডেটা এনালাইসিস এবং প্রিপ্রোসেসিং**</h1></center>\n",
    "\n",
    "#ডেটা এনালাইসিস এবং প্রিপ্রোসেসিং\n",
    "\n",
    "সাধারণত ডেটা সাইন্সে এই পাইপলাইনটা ফলো করে । তবে আমরা ডেটা প্রসেসিং + এনালাইসিস একই সাথে করবো ।\n",
    "\n",
    "\n",
    "<p>\n",
    "<img src=\"data_pipeline.PNG\">\n",
    "    </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "বেশ কিছু মিসিং ভ্যালু পেয়েছি আমরা ডেটা সেটে । এখানে আমরা মিসিং ভ্যালু অনেকভাবে ফিলআপ করতে পারি ।\n",
    "তবে এখানে আমি মিডিয়ান দিয়েই ফিলাপ করবো । \n",
    "মিডিয়ান কি জানতে দেখে নিন এখানে <a href=\"https://www.mathsisfun.com/median.html\"> Median </a>\n",
    "\n",
    "এখানে যে মিসিং ভ্যালু ট্রিট্মেন্ট করলাম এইটা হলো ডেটা প্রিপ্রসেসিং এর পার্ট । \n",
    "ডেটা প্রি প্রসেসিং এবং এনালাইসিস চাইলে আগে পরে হতে পারে কিংবা একসাথেও হতে পারে ।\n",
    "যখন যেমন দরকার পরে আরকি !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.fillna(df.median().iloc[0])\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "মিসিং ভ্যালু ফিলআপ করা শেষ এবার দেখি ডেটা টাইপ কিরকম আমাদের ডেটাসেটে । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এবার টেস্টের ডেটা সেটটিও লোড করে নেই আরেকটি পান্ডাস ডেটাফ্রেমে ।"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখানে মিসিং ভ্যালু আছে কিনা । থাকলে সেম ওয়েতে ফিলআপ করবো । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = test.fillna(test.median().iloc[0])\n",
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এবার দেখে নেই আমাদের ট্রেইন ডেটাসেটে কোন আউটলেয়ার আছে কিনা । আউটলেয়ার কি জানতে হলে দেখে নিন এখানে, \n",
    "<a href=\"https://www.mathsisfun.com/definitions/outlier.html\">Outlier </a> .\n",
    "সংক্ষেপে বলি একটুঃ ধরেন ১০ জনের বেতনের লিস্ট আছে আপনার কাছে । এরকম\n",
    "১০০০০,১২০০০,১৫০০০,১৬০০০,১৭০০০,১৩০০০,১০০০০,২০০০০,১৪০০০,১৩২০০০ ।\n",
    "এখানে দেখেন প্রথম ৯ টি ভ্যালু কিন্তু বেশ কাছাকাছি । মানে এরা বেশ ক্লোজ । কিন্তু লাস্ট ভ্যালুটা অনেক বড় সবার চেয়ে ।\n",
    "এই লাস্ট ভ্যালুর জন্য যদি তাদের গড় বের করি তা অনেক বড় হয়ে যাবে । \n",
    "এবং তাতে কিন্তু আমাদের সঠিক ইনফর্ম্যাশন পাওয়া যাচ্ছে না । তো এই ভ্যালুটাই হলো আউটলেয়ার । "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>**ডেটা ভিজুয়ালাইজেশন **</h1></center>\n",
    "\n",
    "#ডেটা ভিজুয়ালাইজেশন"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "তো আমাদের ডেটাতে বেশ ভালো রকমই আউটলেয়ার আছে দেখা যাচ্ছে । বক্সের বাইরে \n",
    "বেশি গাড়ো কালো রঙের ডট গুলিই আউটলেয়ার । বয়স এবং ভাড়ার ফিল্ডে আউটলেয়ার আছে । ভাড়াতে একটু বেশিই ! ওভারঅল একটা ডেস্ক্রিপশন দেখে নেই । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.0</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>446.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>687.0</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.398620</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166.699487</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                            Name  \\\n",
       "count    891.000000  891.000000  891.000000                             891   \n",
       "unique          NaN         NaN         NaN                             891   \n",
       "top             NaN         NaN         NaN  Palsson, Master. Gosta Leonard   \n",
       "freq            NaN         NaN         NaN                               1   \n",
       "mean     446.000000    0.383838    2.308642                             NaN   \n",
       "std      257.353842    0.486592    0.836071                             NaN   \n",
       "min        1.000000    0.000000    1.000000                             NaN   \n",
       "25%      223.500000    0.000000    2.000000                             NaN   \n",
       "50%      446.000000    0.000000    3.000000                             NaN   \n",
       "75%      668.500000    1.000000    3.000000                             NaN   \n",
       "max      891.000000    1.000000    3.000000                             NaN   \n",
       "\n",
       "         Sex         Age       SibSp       Parch    Ticket        Fare  Cabin  \\\n",
       "count    891  891.000000  891.000000  891.000000       891  891.000000  891.0   \n",
       "unique     2         NaN         NaN         NaN       681         NaN  148.0   \n",
       "top     male         NaN         NaN         NaN  CA. 2343         NaN  446.0   \n",
       "freq     577         NaN         NaN         NaN         7         NaN  687.0   \n",
       "mean     NaN  112.398620    0.523008    0.381594       NaN   32.204208    NaN   \n",
       "std      NaN  166.699487    1.102743    0.806057       NaN   49.693429    NaN   \n",
       "min      NaN    0.420000    0.000000    0.000000       NaN    0.000000    NaN   \n",
       "25%      NaN   22.000000    0.000000    0.000000       NaN    7.910400    NaN   \n",
       "50%      NaN   32.000000    0.000000    0.000000       NaN   14.454200    NaN   \n",
       "75%      NaN   54.000000    1.000000    0.000000       NaN   31.000000    NaN   \n",
       "max      NaN  446.000000    8.000000    6.000000       NaN  512.329200    NaN   \n",
       "\n",
       "       Embarked  \n",
       "count       891  \n",
       "unique        4  \n",
       "top           S  \n",
       "freq        644  \n",
       "mean        NaN  \n",
       "std         NaN  \n",
       "min         NaN  \n",
       "25%         NaN  \n",
       "50%         NaN  \n",
       "75%         NaN  \n",
       "max         NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "আমরা এখানে নাম টা বাদ দিয়ে দিচ্ছি । কারণ এই ২ টা জিনিস আসলে আমাদের কাজে লাগবে না প্রেডিকশনে । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>446</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>446</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>446</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    male  22.0      1      0   \n",
       "1            2         1       1  female  38.0      1      0   \n",
       "2            3         1       3  female  26.0      0      0   \n",
       "3            4         1       1  female  35.0      1      0   \n",
       "4            5         0       3    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   446        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   446        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   446        S  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df['Name']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "শিপে কতজন মহিলা এবং কতজন পুরুষ ছিলো দেখে নেয়া যাক কাউন্টপ্লট থেকে!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.countplot(df[\"Sex\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এবার দেখি বয়সের দিক থেকে মহিলা কতজন ছিলো এবং পুরুষ কতজন ছিলো । এইটা দেখবো বারপ্লট দিয়ে । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.barplot(x=df[\"Sex\"], \n",
    "                   y=df[\"Age\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এবার দেখি সারভাইভ করার দিক থেকে কারা বেশি সারভাইভ করেছিলো । পুরুষ নাকি মহিলা । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.barplot(x=df[\"Sex\"], \n",
    "                   y=df[\"Survived\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "আমরা এই সারমর্মে পৌছালাম যে শিপে পুরুষ যাত্রী বেশি ছিলেন এবং তাদের গড় বয়স মহিলাদের থেকে বেশি ছিলো । \n",
    "এবং পুরুষ যাত্রী বেশি থাকা সত্ত্বেও মহিলারাই বেশি বেঁচে গিয়েছিলো ।\n",
    "তাহলে আমরা বুঝতে পারলাম যে মহিলাদের বেশি প্রায়োরিটি দেয়া হয়েছিলো । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "plot = sns.lineplot(x=df[\"Age\"],\n",
    "                    y=df[\"Survived\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখানে দেখা যাচ্ছে সারভাইভালের সাথে বয়সয়ের সম্পর্কটা বেশ বিচ্ছিন্ন আকারে আছে । তবে এটা বোঝা যাচ্ছে যাদের বয়স কম তাদের সারভাভালের সংখ্যা বেশি ছিলো ।"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.jointplot(x=df[\"Age\"],\n",
    "                     y=df[\"Survived\"],\n",
    "                     kind='reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "উপরের সম্পর্কটাই এখানে পুরোপুরি বোঝা যাচ্ছে । বয়স যত কম তার সারভাইভালের চান্স ততোবেশি । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZBQUFZkZpkc2bs/Tll8dUU1OtgQPvsTrONafLzWHqcvPVfU4f6Ig4Jr+C6uqaRksAuBZQCAAASRQCAMCNQgAASKIQAABuFAIAQJLJf3bqb2HdOqtzSFCb9+Pr+VBqautVeYG/WAJwdetQhdA5JEiT5/305y546/Tp7+dAOXW60if7y1o1RZWiEABc3ThlBACQRCEAANwoBACAJAoBAOBGIQAAJFEIV2TrFNRoCQDXAgrhCrreOFBBXaPV9caBVkcBAL/pUPch+EpI+C0KCb/F6hgA4FccIQAAJFEIAAA3CgEAIIlCAAC4UQgAAEkUAgDAjUIAAEiiEAAAbhQCAEAShQAAcKMQAACSKAQAgBuFAACQRCEAANwoBACAJAoBAOBmaiHk5uYqMTFR8fHx2rRp02Xr//73v2vMmDEaPXq0nnjiCZ0/f97MOACAJphWCGVlZcrMzFRWVpa2bdum7OxsHT161LO+qqpKzz77rNatW6d3331XvXv31ssvv2xWHABAM0wrhMLCQg0aNEgREREKDQ1VQkKC8vLyPOvr6+u1dOlSRUVFSZJ69+6t0tJSs+IAAJphWiGUl5fLbrd7xg6HQ2VlZZ5xZGSkHnroIUlSTU2N1q1bp5EjR5oVBwDQjECzduxyuWSz2TxjwzAajf+jsrJSTz75pPr06aNx48a16Dm6d+/a5pz+YreHWR2hTcjvvaAAm9TgXvpIe3//m3Opvl6BQUFt3k+nTjbP0hfvma9ytRemFUJ0dLSKi4s9Y6fTKYfD0Wib8vJy/epXv9KgQYO0YMGCFj/HmTNVcrkMz/hq/kfjdFY2uw35zdNcfl9mH9otQsWVF3RPWDef7dOb9789s9vD9OIzM9u8n3Onyz1LX+zvNyv+2KHe+4AAW5O/SJt2ymjIkCEqKipSRUWFqqurlZ+fr+HDh3vWNzQ0aNasWXr44Ye1cOHCKx49AO3RbZ276P/Yo3Rb5y5WRwFaxLQjhKioKM2ZM0dpaWmqr6/XhAkTFBcXp/T0dGVkZOjUqVP69NNP1dDQoN27d0uSYmJitHz5crMiAQCaYFohSFJKSopSUlIafW/9+vWSpNjYWB0+fNjMpwcAtAB3KgMAJFEIAAA3CgEAIIlCAAC4UQgAAEkUAgDAjUIAAEiiEAAAbhQCAEAShQAAcKMQAACSKAQAgBuFAACQRCEAANwoBACAJAoBAOBGIQAAJFEIAAA3CgEAIIlCAAC4UQgAAEkUAgDAjUIAAEiiEAAAbhQCAEAShQAAcKMQAACSKAQAgBuFAACQRCEAANwoBACAJAoBAOBGIQAAJJlcCLm5uUpMTFR8fLw2bdp02fpDhw5p/PjxSkhI0MKFC3Xp0iUz4wAAmmBaIZSVlSkzM1NZWVnatm2bsrOzdfTo0UbbzJ07V0uWLNHu3btlGIZycnLMigMAaEagWTsuLCzUoEGDFBERIUlKSEhQXl6ennrqKUnSN998o5qaGvXv31+SNH78eK1du1aTJ0/2+jkCAmyXfa9H5HU+SO97V8p6JcHdupucpHW8zd+j6/UmJ2kdb/J36XF1vveS9+9/e9Ytou3vv8NRo+/q6hUaHOST/Ukd671v7rWYVgjl5eWy2+2escPh0P79+39yvd1uV1lZWYueI/IK//Nf+8zYVqQ1X/fuXb3aLnbWSpOTtI63+Vc/utTkJK3jTf4RL672Q5LW8fb9b89mPP182/fhgxw/di289/9h2ikjl8slm+2HNjIMo9G4ufUAAP8yrRCio6PldDo9Y6fTKYfD8ZPrT58+3Wg9AMC/TCuEIUOGqKioSBUVFaqurlZ+fr6GDx/uWX/TTTcpJCRE+/btkyRt37690XoAgH/ZDMMwzNp5bm6u/vjHP6q+vl4TJkxQenq60tPTlZGRodjYWB0+fFiLFi1SVVWV+vbtqxUrVig4ONisOACAJphaCACA9oM7lQEAkigEAIAbhQAAkEQhAADcKIQraG5SvqtdVVWVkpOTdfLkSaujtNgrr7yipKQkJSUladWqVVbHabE1a9YoMTFRSUlJ2rhxo9VxWmXlypWaP3++1TFaZerUqUpKStKYMWM0ZswYffLJJ1ZH8lpBQYHGjx+vhx9+WM8995w1IQw0curUKWPEiBHG2bNnjYsXLxopKSnGkSNHrI7ltX//+99GcnKy0bdvX+Prr7+2Ok6L7N2715g4caJRW1tr1NXVGWlpaUZ+fr7Vsbz24YcfGqmpqUZ9fb1RXV1tjBgxwvjiiy+sjtUihYWFxn333Wc8/fTTVkdpMZfLZQwbNsyor6+3OkqLffXVV8awYcOM0tJSo66uzpg0aZLx3nvv+T0HRwg/8t+T8oWGhnom5WsvcnJytHTp0nZ517fdbtf8+fMVHBysoKAg/exnP9O3335rdSyv3XvvvXrjjTcUGBioM2fOqKGhQaGhoVbH8tq5c+eUmZmpWbNmWR2lVY4dOyZJeuyxxzR69Gi9+eabFify3t/+9jclJiYqOjpaQUFByszMVL9+/fyew7TJ7dqr5iblu9otX77c6gitdvvtt3u+Pn78uP7617/qrbfesjBRywUFBWnt2rV67bXXNGrUKEVFRVkdyWtLlizRnDlzVFpaanWUVrlw4YIGDx6sxYsXq76+XmlpaerVq5eGDh1qdbRmnThxQkFBQZo1a5ZKS0v1y1/+UrNnz/Z7Do4QfoRJ96x35MgRPfbYY5o3b5569uxpdZwWy8jIUFFRkUpLS9vNZ3xs3rxZN9xwgwYPHmx1lFYbMGCAVq1apbCwMF1//fWaMGGC/vnPf1odyysNDQ0qKirS888/r+zsbO3fv19bt271ew4K4Ueam5QP5tq3b5+mT5+u3/72txo3bpzVcVrkiy++0KFDhyRJXbp0UXx8vD777DOLU3ln165d2rt3r8aMGaO1a9eqoKBAzz/f9umo/am4uFhFRUWesWEYCgxsHydBevToocGDB+v6669X586dNXLkSEvOTFAIP9LcpHwwT2lpqZ588kmtXr1aSUlJVsdpsZMnT2rRokWqq6tTXV2d9uzZo7vvvtvqWF7ZuHGjduzYoe3btysjI0MPPPCAFixYYHWsFqmsrNSqVatUW1urqqoqbd26VQ899JDVsbwyYsQIffDBB7pw4YIaGhr0r3/9S3379vV7jvZRn34UFRWlOXPmKC0tzTMpX1xcnNWxrgkbNmxQbW2tXnjhBc/3UlNTNWnSJAtTee/+++/X/v37NXbsWHXq1Enx8fHtstjaqxEjRuiTTz7R2LFj5XK5NHnyZA0YMMDqWF7p16+fZsyYocmTJ6u+vl5Dhw7VI4884vccTG4HAJDEKSMAgBuFAACQRCEAANwoBACAJAoBAODGn50CP+HkyZN66KGHdMcdd3i+ZxiG0tLSNGHChDbvf+rUqZoyZYpGjRrV5n0BvkAhAE3o3Lmztm/f7hmXlZUpOTlZMTEx6tOnj4XJAN+jEIAWiIqK0q233qpDhw7ptdde04kTJ3Tu3Dldd911Wr16tW677TZNnTpV4eHhOnbsmCZNmqRRo0Zp6dKlOnbsmAICApSamqq0tDRJ0p49e7RhwwadPn1agwcP1nPPPaeAAM7kwhr85AEt8PHHH+urr76SzWZTt27dlJ2drd27dysmJqbRhyl169ZNu3bt0tSpU7Vs2TL17NlTeXl5ys7OVk5Ojk6cOCFJunjxov7yl79o165dev/991VSUmLVSwM4QgCaUlNTozFjxkj6fkbKyMhI/e53v9P999+v22+/XX/+85914sQJffTRR42mSbjnnns8XxcWFmru3LmSpLCwMO3YscOzLjExUZ06dVKXLl3Us2dPnTlzxk+vDLgchQA04cfXEP4jKytLOTk5mjJlilJSUhQREdHoI0v/+4NxAgMDG02h/vXXXysyMtKz7j9sNpuYSQZW4pQR0AoffPCBxo0bp0cffVS9evVSQUGBGhoarrjt4MGD9fbbb0v6fkbOadOm6fjx435MC3iHIwSgFR577DEtWbJEW7ZskST1799fn3/++RW3XbJkiZ599lmlpKTIMAzNnDlTMTEx/owLeIXZTgEAkjhlBABwoxAAAJIoBACAG4UAAJBEIQAA3CgEAIAkCgEA4EYhAAAkSf8fs4FAIFBjhnEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.barplot(x=df[\"Parch\"], \n",
    "                   y=df[\"Survived\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখান থেকে বোঝা যাচ্ছে যাদের বাবা-মা,ভাই-বোন ছিলো শিপে তাদের সারভাইভালের চান্স না থাকাদের চেয়ে বেশি ছিলো । \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.barplot(x=df[\"Pclass\"], \n",
    "                   y=df[\"Survived\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখানে বোঝা যাচ্ছে যাদের কেবিন ক্লাস যত উচুতে ছিলো তাদের বাঁচার চান্স ততো বেশি ছিলো!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.barplot(x=df[\"Embarked\"], \n",
    "                   y=df[\"Survived\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এবার দেখে নেয়া যাক আমাদের কলামগুলির ভিতরে সম্পর্ক কিরকম !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.017515</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.097360</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.146563</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.017515</td>\n",
       "      <td>-0.097360</td>\n",
       "      <td>0.146563</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000756</td>\n",
       "      <td>-0.137702</td>\n",
       "      <td>-0.093258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.000756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.137702</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>-0.093258</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144 -0.017515 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.097360 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000  0.146563  0.083081  0.018443   \n",
       "Age            -0.017515 -0.097360  0.146563  1.000000  0.000756 -0.137702   \n",
       "SibSp          -0.057527 -0.035322  0.083081  0.000756  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.137702  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500 -0.093258  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age         -0.093258  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mtx = df.corr()\n",
    "corr_mtx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "তো এখানে দেখা যাচ্ছে কেউ একজন বাঁচবে কিনা তার সাথে টিকেট ক্লাস, বয়স, ভাইবোনের সংখ্যা এই কলামগুলি \n",
    "নেগেটিভ্লি রিলেটেড । অর্থাৎ এই কলামগুলির ভ্যালু যত বাড়বে তার সারভাইভাল এর চান্স ততো কম । \n",
    "আবার ভাড়া এবং টোটাল ফ্যামিলি মেম্বারের সাথে সম্পর্ক পজিটিভ । মানে এদের ভ্যালু যতো বেশি হবে তার সারভাইভালের চান্স ততো বেশি । "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "কিন্তু মডেলে কোন প্রকার ক্যাটাগরিক্যাল ভ্যালু দেয়া যাবে না । তাকে দিতে হবে নিউমেরিক্যাল ভ্যালু । তাই আমরা ক্যাটাগরিক্যাল ভ্যালুগুলোকে নিউমেরিক্যাল কোডে রুপান্তর করবো । \n",
    "এই রুপান্তরের ২ টি ধরন আছে । একটা হলো লেবেল এনকোডিং আরেকটি হলো ওয়ান হট এনকোডিং । যখন আপনি কোন নিউমেরিক ভ্যালু প্রেডিক্ট করতে চাইবেন এবং আপনার ক্যাটাগরিক্যাল ভ্যালু কম তখন আপনি ওয়ান হট এনকোডিং করবেন । \n",
    "যখন আপনি ক্যাটাগরিক্যাল ভ্যালু প্রেডিক্ট করতে চাইবেন । যেমন আমরা এখানে যেটা করছি । তখন আপনি লেবেল এনকোডিং করবেন । তো আমরাও এখানে লেবেল এনকোডিংই করবো ।"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>596</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>669</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>472</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare  \\\n",
       "0            1         0       3    1  22.0      1      0     523   7.2500   \n",
       "1            2         1       1    0  38.0      1      0     596  71.2833   \n",
       "2            3         1       3    0  26.0      0      0     669   7.9250   \n",
       "3            4         1       1    0  35.0      1      0      49  53.1000   \n",
       "4            5         0       3    1  35.0      0      0     472   8.0500   \n",
       "\n",
       "   Cabin  Embarked  \n",
       "0      0         3  \n",
       "1     82         1  \n",
       "2      0         3  \n",
       "3     56         3  \n",
       "4      0         3  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = ['Sex','SibSp','Parch','Ticket','Cabin','Embarked']\n",
    "\n",
    "for col in cat_cols:\n",
    "    df[col] = df[col].astype('category')\n",
    "    #print(col,'---->', dict(enumerate(df[col].cat.categories)))\n",
    "    df[col] = df[col].cat.codes\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "দেখুন এনকোডিং এর সাথে সাথে সে কি সুন্দর ক্যাটাগরিক্যাল ভ্যালুগুলোকে একটা নিউমেরিক ভ্যালুতে কনভার্ট করে ফেলেছে!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "চলেন এবার আলাদা আলাদা করে আরেকবার আউটলেয়ার গুলী দেখে নেই । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Fare'].plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "তো দেখা যাচ্ছে আমাদের বয়সের ফিল্ডে একটি মাত্র আউটলেয়ার আছে । আর ভাড়ার ফিল্ডে প্রচুর আউটলেয়ার । \n",
    "এখন কথা হচ্ছে আমরা প্রথমে এখানে কোন আউটলেয়ার বাদ দিবো না । আউট লেয়ার বাদ না দিয়েই দেখতে চাই আমাদের মডেল কিরকম প্রেডিক্ট করতে পারে! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এনকোডিং করার পরে আরেকবার দেখে নেই সম্পর্ক গুলো কিরকম আছে । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.042939</td>\n",
       "      <td>-0.017515</td>\n",
       "      <td>-0.065229</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>-0.056554</td>\n",
       "      <td>0.012658</td>\n",
       "      <td>-0.000637</td>\n",
       "      <td>0.012985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>-0.097360</td>\n",
       "      <td>-0.026385</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>-0.164549</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.276235</td>\n",
       "      <td>-0.176509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>0.146563</td>\n",
       "      <td>0.078141</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>0.319869</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>-0.537419</td>\n",
       "      <td>0.173511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.042939</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061611</td>\n",
       "      <td>-0.123164</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>0.059372</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>-0.137704</td>\n",
       "      <td>0.118492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.017515</td>\n",
       "      <td>-0.097360</td>\n",
       "      <td>0.146563</td>\n",
       "      <td>0.061611</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.030464</td>\n",
       "      <td>-0.137702</td>\n",
       "      <td>0.025776</td>\n",
       "      <td>-0.093258</td>\n",
       "      <td>-0.118078</td>\n",
       "      <td>-0.149530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.065229</td>\n",
       "      <td>-0.026385</td>\n",
       "      <td>0.078141</td>\n",
       "      <td>-0.123164</td>\n",
       "      <td>-0.030464</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423338</td>\n",
       "      <td>0.069238</td>\n",
       "      <td>0.163254</td>\n",
       "      <td>-0.019989</td>\n",
       "      <td>0.069165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>-0.137702</td>\n",
       "      <td>0.423338</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020003</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>0.033585</td>\n",
       "      <td>0.043351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>-0.056554</td>\n",
       "      <td>-0.164549</td>\n",
       "      <td>0.319869</td>\n",
       "      <td>0.059372</td>\n",
       "      <td>0.025776</td>\n",
       "      <td>0.069238</td>\n",
       "      <td>0.020003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.013885</td>\n",
       "      <td>-0.176515</td>\n",
       "      <td>0.011146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>-0.093258</td>\n",
       "      <td>0.163254</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>-0.013885</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.310987</td>\n",
       "      <td>-0.230365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>-0.000637</td>\n",
       "      <td>0.276235</td>\n",
       "      <td>-0.537419</td>\n",
       "      <td>-0.137704</td>\n",
       "      <td>-0.118078</td>\n",
       "      <td>-0.019989</td>\n",
       "      <td>0.033585</td>\n",
       "      <td>-0.176515</td>\n",
       "      <td>0.310987</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.070245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0.012985</td>\n",
       "      <td>-0.176509</td>\n",
       "      <td>0.173511</td>\n",
       "      <td>0.118492</td>\n",
       "      <td>-0.149530</td>\n",
       "      <td>0.069165</td>\n",
       "      <td>0.043351</td>\n",
       "      <td>0.011146</td>\n",
       "      <td>-0.230365</td>\n",
       "      <td>-0.070245</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Sex       Age     SibSp  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.042939 -0.017515 -0.065229   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.543351 -0.097360 -0.026385   \n",
       "Pclass         -0.035144 -0.338481  1.000000  0.131900  0.146563  0.078141   \n",
       "Sex             0.042939 -0.543351  0.131900  1.000000  0.061611 -0.123164   \n",
       "Age            -0.017515 -0.097360  0.146563  0.061611  1.000000 -0.030464   \n",
       "SibSp          -0.065229 -0.026385  0.078141 -0.123164 -0.030464  1.000000   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.245489 -0.137702  0.423338   \n",
       "Ticket         -0.056554 -0.164549  0.319869  0.059372  0.025776  0.069238   \n",
       "Fare            0.012658  0.257307 -0.549500 -0.182333 -0.093258  0.163254   \n",
       "Cabin          -0.000637  0.276235 -0.537419 -0.137704 -0.118078 -0.019989   \n",
       "Embarked        0.012985 -0.176509  0.173511  0.118492 -0.149530  0.069165   \n",
       "\n",
       "                Parch    Ticket      Fare     Cabin  Embarked  \n",
       "PassengerId -0.001652 -0.056554  0.012658 -0.000637  0.012985  \n",
       "Survived     0.081629 -0.164549  0.257307  0.276235 -0.176509  \n",
       "Pclass       0.018443  0.319869 -0.549500 -0.537419  0.173511  \n",
       "Sex         -0.245489  0.059372 -0.182333 -0.137704  0.118492  \n",
       "Age         -0.137702  0.025776 -0.093258 -0.118078 -0.149530  \n",
       "SibSp        0.423338  0.069238  0.163254 -0.019989  0.069165  \n",
       "Parch        1.000000  0.020003  0.216225  0.033585  0.043351  \n",
       "Ticket       0.020003  1.000000 -0.013885 -0.176515  0.011146  \n",
       "Fare         0.216225 -0.013885  1.000000  0.310987 -0.230365  \n",
       "Cabin        0.033585 -0.176515  0.310987  1.000000 -0.070245  \n",
       "Embarked     0.043351  0.011146 -0.230365 -0.070245  1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mtx = df.corr()\n",
    "corr_mtx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এবার একটু ডিস্ট্রিবিউশন দেখে নেই ডেটাসেটের । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.distplot(df[\"Age\"],\n",
    "                    hist=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "বয়সের কলামটা মোটামোটি বলা যায় নর্মাল ডিস্ট্রিবিউটেডই শুধু ঐ শেষের আউটলেয়ার টা বাদে । বলা হয়ে থাকে নর্মাল ডিস্ট্রিবিউটেড ডেটা তে সবচেয়ে ভালো প্রেডিকশন দেয় মডেল । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.distplot(df[\"Fare\"],\n",
    "                    hist=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ভাড়ার ডিস্ট্রিবিউশনটা একটু লেফট স্ক্যাটার্ড । মানে বোঝাই যাচ্ছে শিপে আসলে ধনী লোক কমই ছিলেন । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.distplot(df[\"Pclass\"],\n",
    "                    hist=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "টিকেট ক্লাসও কিন্তু উপরের কথাটাকেই ভ্যারিফাই করে !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "তো আমাদের ট্রেইন ডেটাসেট নিয়ে আপাততো কাজ শেষ । এবার টেস্ট ডেটা সেটে নজর দেই ।"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1100.5</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>1100.5</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1100.5</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>1100.5</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>1100.5</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare   Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292  1100.5        Q  \n",
       "1  47.0      1      0   363272   7.0000  1100.5        S  \n",
       "2  62.0      0      0   240276   9.6875  1100.5        Q  \n",
       "3  27.0      0      0   315154   8.6625  1100.5        S  \n",
       "4  22.0      1      1  3101298  12.2875  1100.5        S  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "টেস্ট ডেটাসেটের কলামগুলো সব একই ট্রেইন ডেটা সেটের মতো । তাই আলাদা ডেস্ক্রিপশন দিলাম না । "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "একটু দেখে নেই টেস্ট ডেটাসেটে আউটলেয়ার কিরকম! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "তো দেখা যাচ্ছে এখানেও সেম ভাবেই বয়সের এবং ভাড়ার কলামে আউটলেয়ার আছে । একটু ডেস্ক্রিপশন দেখি টেস্ট ডেটাসেটের ।"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418</td>\n",
       "      <td>418</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dibden, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1100.5</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>327.0</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.462919</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.174731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433.334079</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.360791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1100.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1100.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId      Pclass                 Name   Sex          Age  \\\n",
       "count    418.000000  418.000000                  418   418   418.000000   \n",
       "unique          NaN         NaN                  418     2          NaN   \n",
       "top             NaN         NaN  Dibden, Mr. William  male          NaN   \n",
       "freq            NaN         NaN                    1   266          NaN   \n",
       "mean    1100.500000    2.265550                  NaN   NaN   250.462919   \n",
       "std      120.810458    0.841838                  NaN   NaN   433.334079   \n",
       "min      892.000000    1.000000                  NaN   NaN     0.170000   \n",
       "25%      996.250000    1.000000                  NaN   NaN    23.000000   \n",
       "50%     1100.500000    3.000000                  NaN   NaN    32.000000   \n",
       "75%     1204.750000    3.000000                  NaN   NaN    56.500000   \n",
       "max     1309.000000    3.000000                  NaN   NaN  1100.500000   \n",
       "\n",
       "             SibSp       Parch    Ticket         Fare   Cabin Embarked  \n",
       "count   418.000000  418.000000       418   418.000000   418.0      418  \n",
       "unique         NaN         NaN       363          NaN    77.0        3  \n",
       "top            NaN         NaN  PC 17608          NaN  1100.5        S  \n",
       "freq           NaN         NaN         5          NaN   327.0      270  \n",
       "mean      0.447368    0.392344       NaN    38.174731     NaN      NaN  \n",
       "std       0.896760    0.981429       NaN    76.360791     NaN      NaN  \n",
       "min       0.000000    0.000000       NaN     0.000000     NaN      NaN  \n",
       "25%       0.000000    0.000000       NaN     7.895800     NaN      NaN  \n",
       "50%       0.000000    0.000000       NaN    14.454200     NaN      NaN  \n",
       "75%       1.000000    0.000000       NaN    31.500000     NaN      NaN  \n",
       "max       8.000000    9.000000       NaN  1100.500000     NaN      NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.countplot(test[\"Sex\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "টেস্ট ডেটাসেটে দেখা যাচ্ছে পুরুষের সংখ্যা ছিলো প্রায় দ্বিগুন নারীর সংখ্যার !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.distplot(test[\"Age\"],\n",
    "                    hist=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখানকার বয়সের ফিল্ডটাও বেশ ভালো ডিস্ট্রিবিউটেড । একটা ভালো আউটপুট আশা করতেই পারি !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "দেখে নেই টেস্ট ডেটাসেটে কলামগুলির ভিতরকার সম্পর্ক কিরকম !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026751</td>\n",
       "      <td>0.006208</td>\n",
       "      <td>0.003818</td>\n",
       "      <td>0.043080</td>\n",
       "      <td>-0.009637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.026751</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269709</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.018721</td>\n",
       "      <td>-0.392489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.006208</td>\n",
       "      <td>0.269709</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.078097</td>\n",
       "      <td>-0.011824</td>\n",
       "      <td>-0.145168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.003818</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>-0.078097</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.306895</td>\n",
       "      <td>0.108721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.043080</td>\n",
       "      <td>0.018721</td>\n",
       "      <td>-0.011824</td>\n",
       "      <td>0.306895</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>-0.009637</td>\n",
       "      <td>-0.392489</td>\n",
       "      <td>-0.145168</td>\n",
       "      <td>0.108721</td>\n",
       "      <td>0.154824</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId    Pclass       Age     SibSp     Parch      Fare\n",
       "PassengerId     1.000000 -0.026751  0.006208  0.003818  0.043080 -0.009637\n",
       "Pclass         -0.026751  1.000000  0.269709  0.001087  0.018721 -0.392489\n",
       "Age             0.006208  0.269709  1.000000 -0.078097 -0.011824 -0.145168\n",
       "SibSp           0.003818  0.001087 -0.078097  1.000000  0.306895  0.108721\n",
       "Parch           0.043080  0.018721 -0.011824  0.306895  1.000000  0.154824\n",
       "Fare           -0.009637 -0.392489 -0.145168  0.108721  0.154824  1.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mtx = test.corr()\n",
    "corr_mtx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "তো সেম একই ভাবে আমরা কোন ক্যাটাগরিক্যাল ভ্যালু দিয়ে আমাদের মডেলকে টেস্ট করতে পারবো না । \n",
    "তাই এখানেও আমরা ক্যাটাগরিক্যাল ভ্যালুগুলোকে লেবেল এনকোডিং করে নিয়েছি ।"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>Embarked</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
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       "      <td>62.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name  Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    1   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)    0   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    1   \n",
       "3          895       3                              Wirz, Mr. Albert    1   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0   \n",
       "\n",
       "    Age  SibSp  Parch  Ticket     Fare  Cabin  Embarked  \n",
       "0  34.5      0      0     152   7.8292      0         1  \n",
       "1  47.0      1      0     221   7.0000      0         2  \n",
       "2  62.0      0      0      73   9.6875      0         1  \n",
       "3  27.0      0      0     147   8.6625      0         2  \n",
       "4  22.0      1      1     138  12.2875      0         2  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cat_cols = ['Sex','SibSp','Parch','Ticket','Cabin','Embarked']\n",
    "\n",
    "for col in cat_cols:\n",
    "    test[col] = test[col].astype('category')\n",
    "    #print(col,'---->', dict(enumerate(df[col].cat.categories)))\n",
    "    test[col] = test[col].cat.codes\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "সেম ভাবে এখানেও আমাদের নামের কলামটা কোন কাজে আসবে না । তাই এটাও বাদ । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
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       "      <td>7.8292</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>894</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare  Cabin  \\\n",
       "0          892       3    1  34.5      0      0     152   7.8292      0   \n",
       "1          893       3    0  47.0      1      0     221   7.0000      0   \n",
       "2          894       2    1  62.0      0      0      73   9.6875      0   \n",
       "3          895       3    1  27.0      0      0     147   8.6625      0   \n",
       "4          896       3    0  22.0      1      1     138  12.2875      0   \n",
       "\n",
       "   Embarked  \n",
       "0         1  \n",
       "1         2  \n",
       "2         1  \n",
       "3         2  \n",
       "4         2  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del test['Name']\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>**মেশিন লার্নিং মডেল ট্রেইনিং**</h1></center>\n",
    "\n",
    "\n",
    "#মেশিন লার্নিং মডেল ট্রেইন\n",
    "\n",
    "\n",
    "\n",
    "আমরা এখানে ডিসিশন ট্রি দিয়ে মডেলকে ট্রেইন করবো । ডিসিশন ট্রি দিয়ে করার কারণ হচ্ছে এই মডেলটা আমার কাছে বুঝতে ইজি মনে হয়েছে । \n",
    "ব্যাপারটা অনেকটা এরকম । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survived\n"
     ]
    }
   ],
   "source": [
    "a = \"male\"\n",
    "a_age = 23\n",
    "a_class = 1\n",
    "if a==\"male\":\n",
    "    if a_age <=23:\n",
    "        if a_class == 1:\n",
    "            print(\"survived\")\n",
    "        else:\n",
    "            print(\"No!\")\n",
    "    else:\n",
    "        print(\"No!\")\n",
    "else:\n",
    "    print(\"No!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ডিসিশন ট্রিও ঠিক একই ভাবে কন্ডিশন মিলিয়ে মিলিয়ে চলতে থাকে । এবং ডিসিশন নেয় । To know more <a href=\"https://scikit-learn.org/stable/modules/tree.html\"> Decision Tree </a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(random_state=7)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<li>sklearn- এটা হলো পাইথনের লাইব্রেরী যার আন্ডারে DecisionTreeClassifier\n",
    "ক্লাসটা আছে । আমরা এই ক্লাসের একটি অবজেক্ট ক্রিয়েট করেছি tree নামে । </li>\n",
    "<li>gender_sub- এই ডেটাফ্রেমে আমরা আমাদের টেস্টের রেজাল্ট লোড করে নিয়েছি । এর সাথেই আমরা প্রেডিকশনের ভ্যালু ম্যাচ করে দেখবো কতো ভালো প্রেডিকশন হলো ।</li>\n",
    "<li>এইটা পেয়েছি আমরা gender_submission.csv</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এবার ট্রেইন করার জন্য আমরা ট্রেইন ডেটাকে ২ ভাগে ভাগ করে ফেলি । \n",
    "<ul>X_train- এখানে আছে আমাদের ফিচার কলাম গুলী । অর্থাৎ যার উপর বেস করে আউটপুট প্রেডিক্ট করবে আমাদের মডেল । \n",
    "যেহেতু আমরা সারভাইভাল প্রেডিক্ট করবো তাই দিয়েছি !=Survived </ul>\n",
    "<ul>Y_train- এখানে আছে সারভাইভালের ডেটাগুলো । তাই ==Survived</ul>\n",
    "<ul>X_test- টেস্ট ডেটাসেট থেকে এই ফ্রেমের ভ্যালুগুলো পেয়েছি । </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=7, splitter='best')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = df.loc[:, df.columns!='Survived']\n",
    "Y_train = df.loc[:, df.columns=='Survived']\n",
    "X_test = test.loc[:,]\n",
    "tree.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>7.2500</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>669</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>472</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare  \\\n",
       "0            1         0       3    1  22.0      1      0     523   7.2500   \n",
       "1            2         1       1    0  38.0      1      0     596  71.2833   \n",
       "2            3         1       3    0  26.0      0      0     669   7.9250   \n",
       "3            4         1       1    0  35.0      1      0      49  53.1000   \n",
       "4            5         0       3    1  35.0      0      0     472   8.0500   \n",
       "\n",
       "   Cabin  Embarked  \n",
       "0      0         3  \n",
       "1     82         1  \n",
       "2      0         3  \n",
       "3     56         3  \n",
       "4      0         3  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>892</td>\n",
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       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>221</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare  Cabin  \\\n",
       "0          892       3    1  34.5      0      0     152   7.8292      0   \n",
       "1          893       3    0  47.0      1      0     221   7.0000      0   \n",
       "2          894       2    1  62.0      0      0      73   9.6875      0   \n",
       "3          895       3    1  27.0      0      0     147   8.6625      0   \n",
       "4          896       3    0  22.0      1      1     138  12.2875      0   \n",
       "\n",
       "   Embarked  \n",
       "0         1  \n",
       "1         2  \n",
       "2         1  \n",
       "3         2  \n",
       "4         2  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test করার আগে শেষ বারের মতো দেখে নেই আমাদের ডেটাফ্রেম ২ টির কি অবস্থা । "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>**মডেল ভ্যালিডেশন**</h1></center>\n",
    "\n",
    "\n",
    "#মডেল ভ্যালিডেশন"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "প্রেডিক্ট করলাম । এবার প্রেডিক্ট রেজাল্ট দেখি । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Y_ID = test.loc[:, test.columns=='PassengerId']\n",
    "Y_ID = Y_ID.values\n",
    "\n",
    "prediction = pd.DataFrame({ 'PassengerId':Y_ID.ravel(),'Survived': Y_pred.ravel()})\n",
    "\n",
    "prediction.to_csv(\"Pred1.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এবার সাবমিশনের পালা । দেখা যাক আমরা কিরকম প্রেডিক্ট করলাম । সাবমিশন করতে শুরুতেই আমাদের সাবমিশন লিঙ্কে যেতে হবে । লিংকঃ <a href=\"https://www.kaggle.com/c/titanic/submit\"> Submit </a>\n",
    "ওখানে গেলে আমরা এরকম একটা সাবমিট অপশন দেখবো । <img src=\"submit.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "সাবমিশনের ঐ লোগোতে ক্লিক করে আমাদের Pred1.csv ফাইলটা আপলোড করে সাবমিট করবো । তারপরে একুরেসি দেখবো । "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "রেজাল্ট কতো হলো দেখেন তো ! দেখেন তো এরকম কিনা । <img src=\"pred1.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "৬৪% ঠিকঠাক উত্তর । খারাপ না । কিন্তু আরো ভালো করা দরকার । কিভাবে করা যায়? চলেন চলে যাই titanic_2nd_try.ipynb ফাইলে । "
   ]
  }
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