{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" }, "colab": { "name": "titanic-project-test.ipynb", "provenance": [] } }, "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "6a09d4fb-60c5-4f45-b844-8c788a50c543", "_uuid": "8e892e637f005dd61ec7dcb95865e52f3de2a77f", "id": "FrwBQrji42hL" }, "source": [ "# পাইথন দিয়ে টাইটানিক প্রজেক্ট\n", "> *Python has been an important part of Google since the beginning, and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we're looking for more people with skills in this language.*\n", "\n", "> -- Peter Norvig, Director of search quality at Google, Inc.\n", "\n", "\"আর\" প্রোগ্রামিং এনভায়রনমেন্টএর পাশাপাশি আমরা পুরো এক্সারসাইজটা দেখাবো পাইথনে। এর মানে এই নয় যে আমরা \"আর\"এ করা এক্সারসাইজটা দেখবো না। এই পাইথনে করা এক্সারসাইজ বুঝতে আমাদের দেখে আসতে হবে পুরো টাইটানিক প্রজেক্ট \"আর\" এনভায়রনমেন্টে। সব জায়গায় বেসিক কনসেপ্ট একই। তবে, 'আর' দিয়ে বোঝা যায় ভালো। আগেই বলেছি যারা কম্পিউটার প্রযুক্তিতে পড়েননি অথবা মেশিন লার্নিং একদম ভেতর থেকে হাতেকলমে শিখতে চান, তাদের জন্য 'আর' অন্য লেভেলের জিনিস। \n", "\n", "কম্পিউটার প্রযুক্তিতে পড়েছেন আর পাইথন জানেন বলে 'মেশিন লার্নিং' শিখে যাবেন সেটাও ভ্রান্ত ধারণা। পাইথন একটা ভালো ফ্রেমওয়ার্ক, প্রায় অনেককিছুই করা যায়। তাই বলে মেশিন লার্নিং আর পাইথন পাশাপাশি সমার্থক শব্দ সেটা বলা যাবে না। সেটা সামনে গেলে দেখতে পাবেন। \n", "\n", "আমার কথা বললে বলবো, আমি দুটোই শিখেছি কারণ - দুটো 'দুই' জায়গায় ভালো। মেশিন লার্নিং শেখার শুরুতে 'আর' ভালো, প্রোডাকশন লেভেলে পাইথন ভালো। যেখানে যেটা লাগে। ছোট দূরত্বে রিকশা ভালো, বড় দূরত্বে হয়তোবা মোটর সাইকেল ভালো। আমাদের জানতে হবে কোথায় কি লাগবে? যুগটা অপটিমাইজেশনের যুগ। দরকার মতো আরো কিছু জিনিস শিখতে হতে পারে। লজ্জা করলেই ক্ষতি। কিছুই শেখা যাবে না। এই পঞ্চাশ বছরের কাছাকাছি বয়সেও আমাকে শিখতে হচ্ছে অনেককিছু। না শিখলে - ঝরে পড়ে যাবেন যে কেউ। " ] }, { "cell_type": "markdown", "metadata": { "id": "_iuwI49I42hQ" }, "source": [ "## ১. জুপিটার নোটবুক ইনস্টলেশন \n", "\n", "আমরা যারা পাইথন নিয়ে কাজ করি তাদের একটা ডেভেলপমেন্ট ইন্টারফেস দরকার। আর স্টুডিওর মতো কিছু একটা। সেখানে আমরা একটা ওয়েববেসড ইন্টারফেস দিয়ে তৈরি জুপিটার নোটবুক ব্যবহার করবো। পাইথন, আর থেকে শুরু করে এমন কোন নামকরা প্রোগ্রামিং এনভায়রনমেন্ট নেই যেখানে জুপিটার ফ্রেমওয়ার্ক কাজ করে না। মজার কথা হচ্ছে পুরো এনভায়রনমেন্ট ভ্যারিয়েবলসহ আপনার কাজ শেয়ার করা যায় সব জায়গায়, এমনকি গিট্হাবেও। \n", "\n", "### ১.১ জুপিটার ইনস্টলেশন - অ্যানাকোন্ডা\n", "\n", "জুপিটারের উইন্ডোজ ইনস্টলেশন একেবারে পানি ভাত। মানে ১. ডাউনলোড করে নিন অ্যানাকোন্ডা, নিচের সাইট থেকে। ২. এরপর ক্লিক, ক্লিক আর ক্লিক। শুধু একটা জিনিস খেয়াল রাখবেন, ইনস্টলেশন পাথে যাতে স্পেস দিয়ে কোন ফোল্ডার না থাকে। উদাহরণ হিসেবে বলা যায় \"C:\\Users\\Test\\Anaconda3\", ঠিক আছে তো?\n", "\n", "https://www.anaconda.com/download/\n", "\n", "### ১.২ জুপিটার নোটবুক চালু \n", "\n", "উইন্ডোজের রান অথবা কমান্ড প্রম্পটে লিখুন, পুরোটা লিখতে হয়না। তার আগেই চলে আসে ডেস্কটপ অ্যাপের নাম। \n", "\n", "jupyter notebook\n", "\n", "খেয়াল রাখবেন - প্রতিটা কমান্ড লিখবেন In [সংখ্যা] সেলে এবং সেটার আউটপুট আপনি দেখতে পারবেন Out [সংখ্যা] দিয়ে। প্রতিবার কমান্ড লেখার পর 'সেল' মেন্যু অথবা 'সেল' বাটনে 'কোড' হিসেবে সিলেক্ট করে রান বাটন চাপবেন। " ] }, { "cell_type": "markdown", "metadata": { "id": "kj1yMMJn42hR" }, "source": [ "### ১.৩ কোথায় পাবো এই স্ক্রিপ্ট?\n", "\n", "https://github.com/raqueeb/mltraining/blob/master/Python/titanic-project.ipynb" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4af5e83d-7fd8-4a61-bf26-9583cb6d3476", "_uuid": "65d04d276a8983f62a49261f6e94a02b281dbcc9", "id": "8OFBJXcc42hR" }, "source": [ "## ২. টাইটানিক জাহাজ ডুবিতে বেচেঁ যাবার প্রেডিকশন\n", "ক্যাগল কম্পিটিশন [টাইটানিক: বিপর্যয়ে মেশিন লার্নিং](http://www.kaggle.com/c/titanic)" ] }, { "cell_type": "markdown", "metadata": { "id": "jefehoTQ42hR" }, "source": [ "- প্রবলেম স্টেটমেন্টকে সংজ্ঞায়িত করা\n", "- ডাটা কোথায় পাবো?\n", "- এক্সপ্লোরাটরি ডাটা অ্যানালাইসিস\n", "- ফীচার ইঞ্জিনিয়ারিং\n", "- মডেলিং\n", "- টেস্টিং" ] }, { "cell_type": "markdown", "metadata": { "id": "xTdZ-RBv42hR" }, "source": [ "### ২.১ প্রবলেম স্টেটমেন্ট: কি সমস্যা সমাধান করবো?" ] }, { "cell_type": "markdown", "metadata": { "id": "nBDRuv7142hS" }, "source": [ "- আমরা জানতে চাইবো কারা কারা বেঁচে বা মারা গিয়েছিলেন?\n", "- আমরা মেশিন লার্নিং টুল ব্যবহার করে দেখতে চাইবো কোন ধরনের মানুষগুলো বেঁচে যাবেন?\n", "*আমাদের এই প্রতিযোগিতা চাচ্ছে যাতে আমরা বাইনারি 'আউটকাম' প্রেডিক্ট করি। এখানে ০ মানে উনি মারা গেছেন, ১ মানে উনি বেঁচে গিয়েছিলেন।* " ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3f529075-7f9b-40ff-a79a-f3a11a7d8cbe", "_uuid": "64ca0f815766e3e8074b0e04f53947930cb061aa", "id": "TlOGcAhA42hS" }, "source": [ "### ২.৩ ডাটা কোথায় পাব?\n", "আমরা ডাটা কালেক্ট করবো ক্যাগল সাইট থেকে\n", "\n", "- ট্রেনিং ডাটাসেট https://www.kaggle.com/c/titanic/download/train.csv\n", "- টেস্ট ডাটাসেট https://www.kaggle.com/c/titanic/download/test.csv" ] }, { "cell_type": "markdown", "metadata": { "id": "NBNHtBwR42hS" }, "source": [ "### ২.৪ পাইথন ক্র্যাশ কোর্স \n", "\n", "যারা পাইথন জানেন না তাদের জন্য 'আর' এনভায়রনমেন্ট ভালো। আর যারা জানেন না, তবে পাইথন শিখতে চান তাদের জন্য এটা একটা ছোট্ট ক্র্যাশ কোর্স। যেটুকু দরকার সেটুকু শেখাবো এখানে। তবে সেটার জন্য 'আর এনভায়রনমেন্ট' দেখে আসতে হবে আগে। পাইথনের সবচেয়ে বড় সুবিধা হচ্ছে এর হাজারো এক্সটার্নাল লাইব্রেরির সাপোর্ট। মেশিন লার্নিং এর জন্য Scikit-learn লাইব্রেরি একটা অসাধারণ জিনিস। Matlab এর মতো NumPy হচ্ছে \"অ্যারে\" কম্পিউটেশন মানে টেবিল নিয়ে কাজ করতে পারে। এই \"অ্যারে\" কিন্তু পাইথনের সাধারণ লিস্ট থেকে আলাদা। আর টেবিলের ডাটা নিয়ে কাজ করে Pandas লাইব্রেরি। আমাদের matplotlib হচ্ছে ডাটা গ্রাফ আর ভিজ্যুয়ালাইজেশন টুল। \n", "\n", "#### মডিউল\n", "\n", "১. পাইথনের সব ফিচার কিন্তু শুরুতেই লোড হয় না ডিফল্ট হিসেবে। আর সেটা আমাদের এই ল্যাঙ্গুয়েজের জন্য হোক - অথবা ডাউনলোড করা থার্ড পার্টি হোক। সেই মডিউলের ওই ফিচারটা ব্যবহার করতে হলে সেই মডিউলটাকে ইমপোর্ট করে নিতে হবে আগে। আগেই বলেছি, এখানে pandas হচ্ছে একটা পাওয়ারফুল ডাটা এনালাইসিস পাইথন লাইব্রেরি যেটা তৈরি করা হয়েছে numpy এর ওপর। numpy হচ্ছে আরেকটা পাইথন লাইব্রেরি যা আমাদেরকে ডাটার 'অ্যারে' ব্যবহার করতে সাহায্য করে। \n", "\n", "import pandas\n", "\n", "২. আবার যেই মডিউল আমরা ইমপোর্ট করি না কেন, সেটাকে ছোট করে নেয়া যায় 'এলিয়াস' মানে ছোট নাম দিয়ে। এখানে আমরা pandas ডাটাফ্রেমকে ইমপোর্ট করছি, সেটাকে নতুন করে অ্যাসাইন করছি pd নেমস্পেস দিয়ে। সংক্ষিপ্ত করতে। সংক্ষিপ্ত না করেও কাজ করা যায়। \n", "\n", "import pandas as pd\n", "\n", "৩. এখানে আপনি pandas কে ইমপোর্ট করে সেখানে তার ছোট্ট নাম দিয়ে কাজ করতে পারবেন পুরো ডকুমেন্ট ধরে। মডিউলের ইমপোর্ট এর পর আপনি তার ফাংশনগুলোকে এক্সেস করতে চাইলে মডিউলের পর (.) ডট দিয়ে মেথডগুলো ব্যবহার করবো। এরপর আমরা pandas লাইব্রেরি থেকে read_csv মেথড ব্যবহার করবো আমাদের কম্পিউটারে রাখা একটা csv ফাইল পড়তে। সেটাকে তারপর আমরা পাঠাবো ডাটাফ্রেমে। এখানে read_csv মেথডটা পুরো টেবিলটাকে পাঠাবে প্রথম সারিটাকে ডাটাফ্রেমের হেডার হিসেবে। প্রথম সারি মানে সেখানে ফিল্ডের নাম থাকে। \n", "\n", "pd.read_csv\n", "\n", "#### ২.৪.১ ট্রেনিং এবং টেস্ট ডাটা লোড করে নেব পান্ডা দিয়ে\n", "\n", "এখন থেকে আমরা সবকিছু রেফার করবো \"আর\" এর সাথে মিলিয়ে। মনে আছে ডাটাফ্রেমের কথা? আগেই বলেছি পাইথনে ডাটাফ্রেম নিয়ে কাজ করে পান্ডাজ নামের অসাধারণ লাইব্রেরি। " ] }, { "cell_type": "code", "metadata": { "_cell_guid": "e58a3f06-4c2a-4b87-90de-f8b09039fd4e", "_uuid": "46f0b12d7bf66712642e9a9b807f5ef398426b83", "collapsed": true, "scrolled": false, "id": "9oXYu_pE42hT" }, "source": [ "import pandas as pd\n", "\n", "train = pd.read_csv('https://github.com/raqueeb/mltraining/raw/master/Python/datasets/train.csv')\n", "test = pd.read_csv('https://github.com/raqueeb/mltraining/raw/master/Python/datasets/test.csv')" ], "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "836a454f-17bc-41a2-be69-cd86c6f3b584", "_uuid": "1ed3ad39ead93977b8936d9c96e6f6f806a8f9b3", "id": "SFkQhATl42hT" }, "source": [ "## ৩. এক্সপ্লোরাটরি ডাটা অ্যানালাইসিস\n", "শুরুতেই ডাটাফ্রেমের মাথা দেখি, মাত্র ৫টা সারি ধরে। train.head ফাংশন মানে দেখাও ট্রেইন ডাটাফ্রেমের মাথার কিছু অংশ। এখানে NaN\tমানে ডাটা নেই। ডাটাটা মিসিং। " ] }, { "cell_type": "code", "metadata": { "_cell_guid": "749a3d70-394c-4d2c-999a-4d0567e39232", "_uuid": "b9fdb3b19d7a8f30cd0bb69ae434e04121ecba93", "scrolled": true, "id": "GvQKkhqB42hT", "outputId": "77588e7a-c4b7-489a-be48-967aefd1b805", "colab": { "base_uri": "https://localhost:8080/", "height": 357 } }, "source": [ "train.head(5)" ], "execution_count": 2, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " PassengerId Survived Pclass ... Fare Cabin Embarked\n", "0 1 0 3 ... 7.2500 NaN S\n", "1 2 1 1 ... 71.2833 C85 C\n", "2 3 1 3 ... 7.9250 NaN S\n", "3 4 1 1 ... 53.1000 C123 S\n", "4 5 0 3 ... 8.0500 NaN S\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 2 } ] }, { "cell_type": "markdown", "metadata": { "id": "YYQqeWIg42hU" }, "source": [ "### ৩.১ ডাটা ডিকশনারি \n", "\n", "- ভ্যারিয়েবল\t- মানে কি?\t- ভ্যালু কি হতে পারে\n", "\n", "- survival\t= বেঁচে গিয়েছেন/মারা গিয়েছেন\t1 = বেঁচে গিয়েছেন; 0 = মারা গিয়েছেন\n", "\n", "- pclass\t= টিকেটের ক্লাস বা শ্রেণী\t1st = প্রথম; 2nd = দ্বিতীয়; 3rd = তৃতীয়\n", "\n", "- sex\t= মহিলা না পুরুষ\n", "\n", "- Age\t= বয়স বছরে,\tএখানে অনেক ডাটা মিসিং\n", "\n", "- sibsp\t= উনার ভাইবোন অথবা স্বামী/স্ত্রীর সংখ্যা, ওই টাইটানিক জাহাজে,\tsiblings / spouses সংখ্যায়\n", "\n", "- parch\t= উনার বাবা মা অথবা বাচ্চাদের সংখ্যা,\tparent /children সংখ্যায়\n", "\n", "- ticket\t= টিকেট নাম্বার,\tকেবিন নম্বর ধরে টিকেট নম্বর\n", "\n", "- fare\t= টাইটানিক যাত্রীর ভাড়া\t\n", "\n", "- cabin\t= টাইটানিকের কেবিন নাম্বার\t\n", "\n", "- embarked\t= কোথা থেকে উঠেছেন, বিশেষ করে কোন পোর্ট থেকে\tC = Cherbourg, Q = Queenstown, S = Southampton" ] }, { "cell_type": "markdown", "metadata": { "id": "TTzMB07l42hU" }, "source": [ "আমরা জানতে চাইবো কতোটা সারি আর কলাম আছে আমাদের ডাটাসেটে। আমরা দেখতে পাচ্ছি ৮৯১ কলাম আর ১২ সারি। " ] }, { "cell_type": "code", "metadata": { "_cell_guid": "ed1e7849-d1b6-490d-b86b-9ca71dfafc7d", "_uuid": "5a641beccf0e555dfd7b9a53a17188ea6edef95b", "id": "-MRYuc6Y42hV", "outputId": "a681ea0d-16d0-450f-d8f7-cd0409bd353a", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "train.shape" ], "execution_count": 3, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(891, 12)" ] }, "metadata": { "tags": [] }, "execution_count": 3 } ] }, { "cell_type": "code", "metadata": { "id": "AJL6TEVa42hV", "outputId": "6ccc521a-321f-47b1-9818-3ea9e122d3d6", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "test.shape" ], "execution_count": 4, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(418, 11)" ] }, "metadata": { "tags": [] }, "execution_count": 4 } ] }, { "cell_type": "code", "metadata": { "_cell_guid": "418b8a69-f2aa-442d-8f45-fa8887190938", "_uuid": "4ee2591110660a4a16b3da7a7530f0945e121b46", "id": "dKi5xwwL42hV", "outputId": "7bf315f3-a83e-4d6c-8526-56d9bda505ec", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "train.info()" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ "\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 891 non-null int64 \n", " 1 Survived 891 non-null int64 \n", " 2 Pclass 891 non-null int64 \n", " 3 Name 891 non-null object \n", " 4 Sex 891 non-null object \n", " 5 Age 714 non-null float64\n", " 6 SibSp 891 non-null int64 \n", " 7 Parch 891 non-null int64 \n", " 8 Ticket 891 non-null object \n", " 9 Fare 891 non-null float64\n", " 10 Cabin 204 non-null object \n", " 11 Embarked 889 non-null object \n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.7+ KB\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "uloijNr_42hV" }, "source": [ "আপনারা দেখেছেন কি? Age, Cabin, Embarked ভ্যারিয়েবলগুলোতে ডাটা মিসিং। " ] }, { "cell_type": "code", "metadata": { "id": "JiBBXo3q42hW", "outputId": "c0b70b07-64cf-432f-944e-58d189bee478", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "test.info()" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ "\n", "RangeIndex: 418 entries, 0 to 417\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 418 non-null int64 \n", " 1 Pclass 418 non-null int64 \n", " 2 Name 418 non-null object \n", " 3 Sex 418 non-null object \n", " 4 Age 332 non-null float64\n", " 5 SibSp 418 non-null int64 \n", " 6 Parch 418 non-null int64 \n", " 7 Ticket 418 non-null object \n", " 8 Fare 417 non-null float64\n", " 9 Cabin 91 non-null object \n", " 10 Embarked 418 non-null object \n", "dtypes: float64(2), int64(4), object(5)\n", "memory usage: 36.0+ KB\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "abc3c4fc-6419-405f-927a-4214d2c73eec", "_uuid": "622d4d4b2ba8f77cc537af97fc343d4cd6de26b2", "id": "0ahYJYUW42hW" }, "source": [ "আমরা দেখতে পাচ্ছি বয়স ভ্যারিয়েবলটাতে অনেক ভ্যালু মিসিং। বেশ সমস্যার কথা। ৮৯১ সারির মধ্যে ৭১৪ সারিতে ভ্যালু আছে। কেবিনে দেখা যাচ্ছে মাত্র ২০৪টাতে ভ্যালু আছে। ভয়ঙ্কর সমস্যা। ডাটাফ্রেমে কোন কোন ভ্যারিয়েবলে কতোটা ভ্যালু মিসিং (NaN) সেটা জানতে আমরা ব্যবহার করছি isnull() মেথড + এরপর সেগুলোকে যোগ করেছি sum() মেথড দিয়ে। " ] }, { "cell_type": "code", "metadata": { "_cell_guid": "0663e2bb-dc27-4187-94b1-ff4ff78b68bc", "_uuid": "3bf74de7f2483d622e41608f6017f2945639e4df", "id": "lPwBqQWe42hW", "outputId": "5b8dbbe3-9001-4428-a95e-6d8bc3d2bca7", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "train.isnull().sum()" ], "execution_count": 7, "outputs": [ { "output_type": "execute_result", "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" ] }, "metadata": { "tags": [] }, "execution_count": 7 } ] }, { "cell_type": "code", "metadata": { "id": "GcZjfzBJ42hW", "outputId": "30e6dd42-ea36-430a-fce6-9398f4e97e59", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "test.isnull().sum()" ], "execution_count": 8, "outputs": [ { "output_type": "execute_result", "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" ] }, "metadata": { "tags": [] }, "execution_count": 8 } ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "176aa52d-fde8-42e6-a3ee-db31f8b0ca49", "_uuid": "b48a9feff6004d783960aa1b32fdfde902d87e21", "id": "RNzaH11q42hX" }, "source": [ "টেস্ট ডাটাফ্রেমে একই কাহিনী। মানে ডাটা মিসিং। এখানে ১৭৭টা সারি মিসিং বয়স ভ্যারিয়েবলে। কেবিনের সাথে কানেক্ট করা যাচ্ছে না ৬৮৭টা ভ্যালু। কোথা থেকে কে উঠেছে সেটাতে নেই ২টা ভ্যালু। আগেই দেখেছেন কেন সমস্যা করে মিসিং ভ্যালু? জানতে হলে আপনাকে পড়তে হবে মেশিন লার্নিং প্রেডিকশন চ্যাপ্টারটা। " ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c8553d48-c5e0-4947-bd13-1b38509c850c", "_uuid": "1a28e607e9ed63cefe0f35a4e4d72f2f36299323", "id": "DATgDgWX42hX" }, "source": [ "## ৪. ডাটা ভিজ্যুয়ালাইজেশন\n", "\n", "এটা ঠিক যে পাইথনও আস্তে আস্তে সুন্দর হয়ে উঠছে ডাটা ভিজ্যুয়ালাইজেশন লাইব্রেরিতে। matplotlib.pyplot লাইব্রেরি দেয় আমাদের ম্যাটল্যাবের মতো চমৎকার প্লটিং ফ্রেমওয়ার্ক। ছবিগুলো জুপিটার নোটবুকে একসাথে দেখানোর জন্য inline মোড নিয়ে আসা হয়েছে। seaborn হচ্ছে পাইথনের matplotlib ভিত্তিক স্ট্যাটিসটিকাল গ্রাফিক্যাল লাইব্রেরি। সুন্দর বটে। " ] }, { "cell_type": "code", "metadata": { "_cell_guid": "b1d8a6d2-c22d-435c-8c98-973e8f41b138", "_uuid": "26411c710f69b29939c815d5f5ab01d9177df7d0", "collapsed": true, "id": "Z8noMor042hX" }, "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "sns.set()" ], "execution_count": 9, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "FZLK41dM42hX" }, "source": [ "### ৪.১ ক্যাটেগরিক্যাল ফিচারগুলো নিয়ে বার চার্ট \n", "- Pclass\n", "- Sex\n", "- SibSp\n", "- Parch\n", "- Embarked\n", "- Cabin\n", "\n", "এখানে আমরা ছবি দিয়ে একটা সম্পর্ক খুঁজবো কারা কারা বেঁচে গিয়েছিলেন - বাকি ভ্যারিয়েবলগুলোর সাথে কানেক্ট করে। পাইথনে একটা বারচার্ট ফাংশন ডিফাইন করছি যাতে বিভিন্ন ভ্যারিয়েবলগুলোকে একেকটা প্যারামিটার ধরে ধরে পাঠাতে পারি আমাদের নতুন তৈরি করা ফাংশনে। এখানে দুটো বারচার্ট তৈরি করবে আমাদের প্যারামিটার - ['Survived','Dead'] - সবকিছুর ভ্যালুগুলোকে যোগ করবো শেষে। " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "q9w6KcMA42hX" }, "source": [ "def bar_chart(feature):\n", " survived = train[train['Survived']==1][feature].value_counts()\n", " dead = train[train['Survived']==0][feature].value_counts()\n", " df = pd.DataFrame([survived,dead])\n", " df.index = ['Survived','Dead']\n", " df.plot(kind='bar',stacked=True, figsize=(10,5))" ], "execution_count": 10, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "fImr6S7_42hY" }, "source": [ "এখন ফিচারে পাঠাই একটা করে আমাদের ভ্যারিয়েবলগুলোকে। শুরুতেই 'Sex'" ] }, { "cell_type": "code", "metadata": { "id": "NNoyNgYa42hY", "outputId": "79c870f5-0268-4291-f762-96635ae5d888", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "bar_chart('Sex')" ], "execution_count": 11, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "To0D_uBn42hY" }, "source": [ "এই চার্ট বলছে পুরুষ থেকে বেশি বেঁচেছেন মহিলারা। পরেরটা 'Pclass'" ] }, { "cell_type": "code", "metadata": { "id": "zVxATJCv42hY", "outputId": "aa3a3a93-774c-4c33-8f45-deffb0bedf82", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "bar_chart('Pclass')" ], "execution_count": 12, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "SbYhZECf42hY" }, "source": [ "এখানে দেখা গেলো প্রথম শ্রেণীর যাত্রীরা বেঁচেছেন বেশি। এদিকে তৃতীয় শ্রেনীর যাত্রীরাও মারা গিয়েছেন অন্য যেকোন শ্রেণী থেকে। " ] }, { "cell_type": "code", "metadata": { "id": "eHtEj2sB42hY", "outputId": "ab3ad5e2-e91a-4985-f26e-8a03ad481812", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "bar_chart('SibSp')" ], "execution_count": 13, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "13r7KhNr42hZ" }, "source": [ "এখানের ব্যাপারটা একটু ভাবিয়েছে আমাদের। ওই ফ্যামিলিগুলোতে যারা দুই জনের বেশি ছিলেন তারা বেঁচেছিলেন বেশি। যারা শুধুমাত্র নিজেরা মানে একা ছিলেন তারা বেঁচেছেন কম। " ] }, { "cell_type": "code", "metadata": { "id": "JQKDMNyy42hZ", "outputId": "aed78953-5362-4555-dc78-b31c4fd5d7cf", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "bar_chart('Parch')" ], "execution_count": 14, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "2owLxVWR42hZ" }, "source": [ "যারা বাবা মা অথবা বাচ্চাদের নিয়ে ছিলেন টাইটানিকে - তারা বেঁচেছেন বেশি। যারা একা ছিলেন তারা অতোটা বাঁচতে পারেননি। " ] }, { "cell_type": "code", "metadata": { "scrolled": true, "id": "7oMmBktK42hZ", "outputId": "ecda4e78-5538-43ff-eee6-ec211d15ceda", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "bar_chart('Embarked')" ], "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "MJPzINif42hZ" }, "source": [ "এই চার্ট কি বলে?\n", "\n", "যারা Cherbourg থেকে উঠেছিলেন তারা অন্য জায়গা থেকে ওঠা মানুষদের থেকে বেঁচেছেন বেশি। Queenstown আর Southampton থেকে ওঠা মানুষগুলো বাঁচেননি বেশি। এর মানে হচ্ছে Cherbourg এলাকার মানুষ অবস্থাপন্ন। " ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "810cd964-24eb-44fb-9e7b-18bbddd4900f", "_uuid": "fd86ccdf2d1248b79c68365444e96e46a50f3f5a", "id": "DRqXwBkJ42ha" }, "source": [ "## ৫. ফিচার ইঞ্জিনিয়ারিং\n", "\n", "এটা নিয়ে একটা বিশাল বড় চ্যাপ্টার লিখেছি আগে। মেশিন লার্নিং প্রেডিকশন নিয়ে। 'ফিচার ইঞ্জিনিয়ারিং' হচ্ছে একটা ডোমেইন নলেজ নিয়ে কিছু ফিচার তৈরী করা যাতে আমাদের মেশিন লার্নিং অ্যালগরিদম চমৎকারভাবে কাজ করে। আমি অনুরোধ করবো সেই চ্যাপ্টারটা আবার দেখে নিতে। কারণ এটা একটা খুবই দরকারি জিনিস। \n", "\n", "আমরা জানি মেশিন তো আমার আপনার মতো ফিচার চেনে না। তার জন্য সেটাকে সংখ্যায় দিলে ভালো কাজ করে। সেরকম কিছু করবো আমরা এখানে। শুরুতেই আমরা ভালোভাবে দেখি আমাদের ডাটাসেটগুলো। " ] }, { "cell_type": "markdown", "metadata": { "id": "unFUR9PG42ha" }, "source": [ "### ৫.১ আচ্ছা, টাইটানিকের কিভাবে ডুবেছিল?\n", "আমরা এখানে রিসার্চ করতে গিয়ে দেখলাম টাইটানিকের পেছনের দিক থেকে ডোবা শুরু করেছিল। আর ওখানেই শুরু হয়েছিল ৩য় শ্রেণী। তারমানে Pclas কিন্তু একটা বড় ক্লাসিফায়ার। একটু পেছনে গেলে দেখবেন কিভাবে নাম এখানে বড় একটা রোল প্লে করেছিল। " ] }, { "cell_type": "markdown", "metadata": { "id": "Oo0WotxR42ha" }, "source": [ "### ৫.২ নাম \n", "প্রথমেই যোগ করে নেই টেস্ট আর ট্রেনিং ডাটাসেট। 'Title' ডাটাসেট তৈরি করি নাম থেকে। " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "UM4OFkcY42ha" }, "source": [ "train_test_data = [train, test] \n", "\n", "for dataset in train_test_data:\n", " dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)" ], "execution_count": 16, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Tj9ijgZU42ha" }, "source": [ "ট্রেইন আর টেস্ট ডাটাসেটে টাইটেলগুলোর সংখ্যা বের করি। এর আগেও ব্যাপারটা করেছিলাম \"আর\" দিয়ে। " ] }, { "cell_type": "code", "metadata": { "id": "6HNmxJ_A42ha", "outputId": "230a3736-bf22-454f-eab6-d72fa9b62676", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "train['Title'].value_counts()" ], "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Mr 517\n", "Miss 182\n", "Mrs 125\n", "Master 40\n", "Dr 7\n", "Rev 6\n", "Mlle 2\n", "Col 2\n", "Major 2\n", "Mme 1\n", "Jonkheer 1\n", "Don 1\n", "Ms 1\n", "Countess 1\n", "Lady 1\n", "Capt 1\n", "Sir 1\n", "Name: Title, dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 17 } ] }, { "cell_type": "code", "metadata": { "id": "gPxwBMG542hb", "outputId": "eeefccbe-28ad-4d9b-9c2a-6e90bd182353", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "test['Title'].value_counts()" ], "execution_count": 18, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Mr 240\n", "Miss 78\n", "Mrs 72\n", "Master 21\n", "Rev 2\n", "Col 2\n", "Dona 1\n", "Ms 1\n", "Dr 1\n", "Name: Title, dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 18 } ] }, { "cell_type": "markdown", "metadata": { "id": "4Q1p7VTq42hb" }, "source": [ "#### টাইটেলগুলোকে ম্যাপ করি সংখ্যার সাথে \n", "\n", "আগের মতো বাকি অদরকারি টাইটেলগুলোকে ম্যাপ করে দেই ৩ এর সাথে। \n", "\n", "Mr : 0 \n", "Miss : 1 \n", "Mrs: 2 \n", "Others: 3\n" ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "-9k_pa1S42hb" }, "source": [ "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n", " \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n", " \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n", "for dataset in train_test_data:\n", " dataset['Title'] = dataset['Title'].map(title_mapping)" ], "execution_count": 19, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "npE8ya1842hb" }, "source": [ "ভালো করে দেখুন Title ভ্যারিয়েবলগুলো। নতুন ম্যাপিং হয়ে গেছে আমাদের দরকার মতো। " ] }, { "cell_type": "code", "metadata": { "id": "51y-asGH42hb", "outputId": "52698f57-c048-432b-80e6-16feed2cdcd7", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "train.head()" ], "execution_count": 20, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS0
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C2
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS1
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S2
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS0
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" ], "text/plain": [ " PassengerId Survived Pclass ... Cabin Embarked Title\n", "0 1 0 3 ... NaN S 0\n", "1 2 1 1 ... C85 C 2\n", "2 3 1 3 ... NaN S 1\n", "3 4 1 1 ... C123 S 2\n", "4 5 0 3 ... NaN S 0\n", "\n", "[5 rows x 13 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 20 } ] }, { "cell_type": "code", "metadata": { "id": "AyDqHhNg42hc", "outputId": "630d3599-4fcf-4c75-f968-8b7be199fba5", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "test.head()" ], "execution_count": 21, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ0
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS0
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS2
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" ], "text/plain": [ " PassengerId Pclass ... Embarked Title\n", "0 892 3 ... Q 0\n", "1 893 3 ... S 2\n", "2 894 2 ... Q 0\n", "3 895 3 ... S 0\n", "4 896 3 ... S 2\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 21 } ] }, { "cell_type": "code", "metadata": { "id": "FS7YPz6n42hc", "outputId": "d74b0161-a4db-400a-aa04-d8b17a44f474", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "bar_chart('Title')" ], "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "7iEfSQ0Z42hc" }, "source": [ "টাইটেল বের করার পর নাম দরকার আছে কি? ফেলে দেই 'Name' ভ্যারিয়েবল। " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "299Q6ViA42hc" }, "source": [ "# delete unnecessary feature from dataset\n", "train.drop('Name', axis=1, inplace=True)\n", "test.drop('Name', axis=1, inplace=True)" ], "execution_count": 23, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "0x164imt42hc", "outputId": "6a8eac41-7382-4802-cf77-2c5f42685527", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "train.head()" ], "execution_count": 24, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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4503male35.0003734508.0500NaNS0
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" ], "text/plain": [ " PassengerId Survived Pclass Sex ... Fare Cabin Embarked Title\n", "0 1 0 3 male ... 7.2500 NaN S 0\n", "1 2 1 1 female ... 71.2833 C85 C 2\n", "2 3 1 3 female ... 7.9250 NaN S 1\n", "3 4 1 1 female ... 53.1000 C123 S 2\n", "4 5 0 3 male ... 8.0500 NaN S 0\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 24 } ] }, { "cell_type": "markdown", "metadata": { "id": "tK6hRwaR42hd" }, "source": [ "নাম কিন্তু নেই আর!" ] }, { "cell_type": "code", "metadata": { "id": "tRxQ_STT42hd", "outputId": "2e308504-1caf-4858-f6e9-83cec1e7016a", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "test.head()" ], "execution_count": 25, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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PassengerIdPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitle
08923male34.5003309117.8292NaNQ0
18933female47.0103632727.0000NaNS2
28942male62.0002402769.6875NaNQ0
38953male27.0003151548.6625NaNS0
48963female22.011310129812.2875NaNS2
\n", "
" ], "text/plain": [ " PassengerId Pclass Sex Age ... Fare Cabin Embarked Title\n", "0 892 3 male 34.5 ... 7.8292 NaN Q 0\n", "1 893 3 female 47.0 ... 7.0000 NaN S 2\n", "2 894 2 male 62.0 ... 9.6875 NaN Q 0\n", "3 895 3 male 27.0 ... 8.6625 NaN S 0\n", "4 896 3 female 22.0 ... 12.2875 NaN S 2\n", "\n", "[5 rows x 11 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 25 } ] }, { "cell_type": "markdown", "metadata": { "id": "JjDRMGBN42hd" }, "source": [ "### ৫.৩ মহিলা পুরুষকে ম্যাপিং করি সংখ্যায় \n", "\n", "male: 0\n", "female: 1" ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "juiRAjiT42hd" }, "source": [ "sex_mapping = {\"male\": 0, \"female\": 1}\n", "for dataset in train_test_data:\n", " dataset['Sex'] = dataset['Sex'].map(sex_mapping)" ], "execution_count": 26, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "gbAbxBMR42hd", "outputId": "a58c727e-0c8f-44e6-c036-952822117f6a", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "bar_chart('Sex')" ], "execution_count": 27, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "GfgrKLXE42hd" }, "source": [ "এখানে কিন্তু মহিলা পুরুষ নেই আর! সেখানে তাদেরকে রিপ্রেজেন্ট করা হচ্ছে ০ এবং ১ দিয়ে। " ] }, { "cell_type": "markdown", "metadata": { "id": "JspZfJQf42he" }, "source": [ "### ৫.৪ বয়স " ] }, { "cell_type": "markdown", "metadata": { "id": "BQVHHlVz42he" }, "source": [ "#### ৫.৪.১ প্রচুর বয়সের ডাটা মিসিং আছে আমাদের ডাটাসেটে \n", "একটা কাজ করি বরং \n", "\n", "টাইটেলের \"গড়\" বয়স দিয়ে ভরে ফেলি আমাদের না থাকা ভ্যালুগুলোর জায়গায় - তাহলে আমাদের Random-Forest এনসেমবল ক্লাসিফায়ার ভালো ভাবে কাজ করবে। \"Mr\": 0, \"Miss\": 1, \"Mrs\": 2 এবং Others: 3 এর গড় বয়স দিয়ে দিচ্ছি এখানে। " ] }, { "cell_type": "code", "metadata": { "id": "Nrm52MBf42he", "outputId": "6a5c6a96-8a54-40cd-9712-fd09b2115223", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "train.head()" ], "execution_count": 28, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitle
0103022.010A/5 211717.2500NaNS0
1211138.010PC 1759971.2833C85C2
2313126.000STON/O2. 31012827.9250NaNS1
3411135.01011380353.1000C123S2
4503035.0003734508.0500NaNS0
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass Sex ... Fare Cabin Embarked Title\n", "0 1 0 3 0 ... 7.2500 NaN S 0\n", "1 2 1 1 1 ... 71.2833 C85 C 2\n", "2 3 1 3 1 ... 7.9250 NaN S 1\n", "3 4 1 1 1 ... 53.1000 C123 S 2\n", "4 5 0 3 0 ... 8.0500 NaN S 0\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 28 } ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "dWPSng0842he" }, "source": [ "# fill missing age with median age for each title (Mr, Mrs, Miss, Others)\n", "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)\n", "test[\"Age\"].fillna(test.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)" ], "execution_count": 29, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "fm94tB2642he", "outputId": "9ff5d516-f6f8-4fd0-cb4e-7b7a0c260da2", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "train.groupby(\"Title\")[\"Age\"].transform(\"median\")\n", "train.head()" ], "execution_count": 30, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitle
0103022.010A/5 211717.2500NaNS0
1211138.010PC 1759971.2833C85C2
2313126.000STON/O2. 31012827.9250NaNS1
3411135.01011380353.1000C123S2
4503035.0003734508.0500NaNS0
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass Sex ... Fare Cabin Embarked Title\n", "0 1 0 3 0 ... 7.2500 NaN S 0\n", "1 2 1 1 1 ... 71.2833 C85 C 2\n", "2 3 1 3 1 ... 7.9250 NaN S 1\n", "3 4 1 1 1 ... 53.1000 C123 S 2\n", "4 5 0 3 0 ... 8.0500 NaN S 0\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 30 } ] }, { "cell_type": "markdown", "metadata": { "id": "oKmnDtnb42he" }, "source": [ "দেখুন কোন বয়স কিন্তু আর বাদ নেই। বয়সকে গড় টাইটেলের আউটকাম দিয়ে ভর্তি করা হয়েছে। \n", "\n", "আমরা একটা চার্ট আঁকি এখানে। মারা যাওয়া মানুষগুলোর বয়স ১৬ থেকে ৩৪ এর মধ্যে বেশি দেখা যাচ্ছে আমাদের ছবিতে। তার আগের অথবা পরের বয়সগুলোর মানুষ বেঁচে গিয়েছে বেশি। প্লটে দেখা যাচ্ছে ৩০ বছরের মানুষগুলো মারা গিয়েছে বেশি। আমরা অনেকগুলো চার্ট আঁকবো কারণ আমাদের বয়স কিন্তু একটা বড়ো ক্লাসিফায়ার। প্রথমে কোন লিমিট রাখবো না - মানে শুরু থেকে শেষ পর্যন্ত - facet.set(xlim=(0, train['Age'].max()))" ] }, { "cell_type": "code", "metadata": { "id": "BTGSdWJk42hf", "outputId": "3b95e40c-c8d0-4a76-f2e9-c12968a1e866", "colab": { "base_uri": "https://localhost:8080/", "height": 203 } }, "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", " \n", "plt.show() " ], "execution_count": 31, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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f/8wPfvADGhoa+Kd/+ieqq6svqNC8PFkYJNMUFHjSXYI4zaW2RyAcZ82Wemo311Poc7Lw2lGMK/em90MgQ4d4CCUeRklEUBIR6P85DPFo6nsigpKIpu5PRlESUUjGUJIxSMZSQc5iw9RsoNkwNSv0fZmqBVRLKhRqFlAsoKqgaZhWC6h2UFRQFEDp+07q5/eDpmGAqYNpoBgJSIRR2prw6AmUZAL0WOo5xEOQiIDVienwYDh9mJ5CDE8hprsAw12AmZWfqu0y8AKL547mmT8e4Mmv3ITVMrKGxMprVuaRNsk80iaZRdpDDCVXbPGe+fPnM3/+fBobG/nSl77EDTfcwJgxYwb8+I6OIIZhXsYKxYUoKPDQ1hZIdxmiz6W0R1t3hLXbG/jLey2MLc3hrrmjKcx1AtDTExnMMsHQUeMBtGgPWqwn9XM8hBoL9P0cRE2EUBIR1GQExUhianYMiwPTYsfUbBiaHaMvHJqqre+2LExnLqa77/b++6yYqjUtPYRut51gMHb2HaaBkoygJiJosQBarAetdx9qbDu2WA9aPEDS6SPhKSORU07cU0rCU4JpG5xFksrzXLgdFn76u9184qaqQTnnUCCvWZlH2iTzSJtkFmmPzCNB/9zOGyz9fj8tLS3ouo6maei6TmtrK36//6zjGhsbmTp1KnB2D+b7SkpKmDJlCq+99toFBUshxOBqbA9Ru/Uou450MHVMHp9dNAGP6xJ7ygwdLdKJJdKBJdyBFm7HEm7HEulEjfeiJsIYVheG1Y1hc2FYnH2h0Ukyq5B4zqjUsFFLavioqVpP6yEcJhQV05qFbs1Cd+Wffb+RxBLpwhJuw9pdj7NxB5ZwG7o9h5hvLHHfWGK5VRiOixvKqigK868qY8UrB7iquoDR/uxLfEJCCCGEEAMIlnl5edTU1FBbW8uSJUuora2lpqbmjGGwAIsWLWLlypUsXLiQ7u5u1q9fz7PPPgtAXV0dVVWpT8Y7OzvZtm0bCxcuvAxPRwhxPseaA/x+Sz0HT/Qwc1w+n7+9BoftAgcvmCZauB1rsAlroBFrsAlLsBlLpBPdmoXhyEG3ZaPb3SSyS4jmT8CwuTGsztSwUvHhVAvJrAKSWQWnbjMNLOEOrIGTZB3finfvSgyLi2h+NdHCKcR8Yy9o+KzbaeWm6SX8tHYv33jomqE7f1YIIYQQGWNA7ya/8Y1vsGzZMp566imys7NZvnw5AA8//DCPPPIIU6ZMYcmSJezcubM/MH7pS1+ivLwcgOeee44tW7ZgsVgwTZMHHniAuXPnXqanJIT4ICfbQ7y4sY66kz1cPaGQhxfXYBvgHDs1HsTa04Ct+xi27npsvccxVRvJrHx0u5eEq5BI/gR0R25qvqIYXIraHzYjkAr2kU5sPcfIPvQHLOF2Yr5xRIumEC2YiGE7/5z0mlG5HDzRw6rN9SNqSKwQQgghLg/FNM0hMXFR5lhmFhn3n1nO1R7t3RF+t+kIu+o6uLqmkBljC87bQ6UkY9i66nB0HMDefgAt2k0yq5BkViGJrEIS7iJM6+DM+RuuPnSO5WWgJCLYeo6lgn/vceLe0YRLryFSOAk024c+LhhJsOKVA3z5nmnDfkisvGZlHmmTzCNtklmkPTKPzLE8N+laEGKYCkUTvLzpCG/saWH6uHw+f/tE7LYP76HUQm04W3biaHsPa+9Jku4iEp5SghVzSWYVyhDWDGZancTyJxDLnwB6HHtXPe5jr+Hd+zyRwimES64m7qs6qw1lSKwQQgghBosESyGGGcM02bSzkZc2HmFsWQ4P3TaBLMcHz7+zBFtwtuzE2fwOaqyXeO4YogWT6R2z8LJteSEuM81GLL+aWH41ajyEvfMg3r3Po5gmwYp5hEuvwbQ6+w8/NST2CJ+4aWwaCxdCCCHEUCbBUohh5EhjL7985QC6YfCxG8ZQ7HOddYwaD+I6+Sauk9tQE2FiuWMIlc0m4S6WXslhxrBlESmeQaRoOpZgE87W98iuW0u4eAahUfNIuv0oisItV5Xxi1cOMH1sAWPLLm61WSGEEEKMbBIshRgGwtEET6/Zx7uH25k31c+kSh/K6dt0mAb2joO4jm/F0XGQeO4YQuXXk3D7h992HuJsikLSU0LAU4IaD+Foe4/87U+R8PgJVC2E3CpumVXGj1e/x7c+dw1Ou1wahBBCCHFh5N2DEEPcniMd/OKVA5QXuPncR2vOmEepJCJknXiDrGObMC12ovkT6Jz6aUyLPY0Vi3QybFmES68h7L8KR8cBcnf/GsOejb3qFo7kufn1+oP8P7dPTHeZQgghhBhiJFgKMUSFo0l+++eD7Knv5GM3jaPAc2r1Ty3SRdaxjWSd3E48p4JA1S2pBXiEeJ+qES2YSDR/AvbOOnL2r+IBRWPVsYm8fSCPmdVF6a5QCCGEEEOIBEshhqC9Rzv5ae0+Rvs9fGbRBIoKPHR3h7EEmvDUr8fRtpdo3gS6Jn4Cwz68t5EQl0hRieWNI+Ybi63nGLfr2+l99R16lAfIHjfzzCHVQgghhBAfQoKlEEOIbhi8vKme13c28tFrKqjs23tQCbSQu/Ml7B0HiRRNpXPKAzLcVVwYRSHurSSeM4q2/TtxbfwFln1rcVx7D1rxuHRXJ4QQQogMJ8FSiCGiszfKf6/ag2HAgwuryXJa0SJdeA6vxdm2h3DhFLqmfApTs53/ZEJ8GEWhsHoaf3jLy430UPqn/0LNH4X9uvvQckvSXZ0QQgghMpTsLSDEEPDu4Xa++fSblOVn8Ykbx+DW4uTse4nCrf8bxdSJXvsZwiWzJFSKQaGqCtdOKuEPxzz0TPsUiief8O+/TXTLLzGjwXSXJ4QQQogMJMFSiAxmmCYvbazjF3/czx1zKrl2QgHuhi0Ubf4OWrSbzsn3ESq7Dk7b8F6IweBx2ZgxLp9VWxowymfiuOFzGMFOgs8vI7ZnPaahp7tEIYQQQmQQGQorRIaKxJL8ZPV7dPbGeGDheHJDR/FufQlTs9Az/g50V366SxTD3KjibNq6o6z9SwN3zh2NbcpCjFHTSex9leTeP2Of82kspbI1iRBCCCGkx1KIjNTaFeaJFTtQgE9e56N83y/x7X6WcPF0esbfKaFSXDEzxuXT2h1h5+E2ANTsQmzX3oOl6jqir/6EyPqnMMLdaa5SCCGEEOkmwVKIDLP3aCdPrHiLyZVe7iqox/+X72NYHHROvo+4rwpk+wdxBWmayuxJxby+s4nWrggAiqKg+cdjv+EhUFVCK/+V2J4/YRpGmqsVQgghRLpIsBQig7y+s5H/WfUeS6c7WNjxLFmNb9I94WOES68BVUaui/TIzrIxfVw+L2+qJ5Y4NbdSsdiwTrgR+3X3kjywmfDvvoHeeiSNlQohhBAjy6OPPsqPfvSjQT/vk08+yVe+8pULeoy8UxUiA5imyarN9Wzd2cAjY4+Sf2Q74dJriOZPlB5KkREqi7Pp6IlSu/Uod99Qdcb/lqqnANt196KffI/I2h9gGTsb+9WfQLHKXqpCCCFGph07dvC9732PQ4cOoWkaY8aM4Wtf+xpTp04d1L/zrW99a1DPdymkx1KINEvqBk+v2cfxfXv4ak4tOeETdE+8h2jBJAmVIqPMGJtPIJRg867Gs+5TFAVL2WTsNzyE0d1M6Pl/IXl8dxqqFEIIIdIrGAzyxS9+kQceeIDt27fz+uuv8/d///fYbBe2LZxpmhhDaJqJBEsh0igaT/KfK9+honkDn7auJeqfSWDsrRi2rHSXJsRZVE3l+inF7Krr4EDDBy/Yo9hc2KbfhnXSfKIbf0Zkw49l70shhBAjSn19PQCLFy9G0zQcDgdz585lwoQJZw0xPXHiBNXV1SSTSQA+/elP88Mf/pD77ruPadOm8dOf/pS77777jPM/88wzfPGLXwRg2bJl/PCHPwTgox/9KK+++mr/cclkkuuuu4733nsPgHfffZf77ruPWbNmceedd7Jt27b+Y48fP84DDzzAjBkzeOihh+jq6rrg5y3BUog0CUYS/PRX67k79FumudvpnngPsbxx6S5LiHNy2CzMneLnle0N/Yv5fBCtcAz2Gx7CNJKEnv8aibptmKZ5BSsVQggh0mP06NFomsZXv/pVNm7cSE9PzwU9ftWqVTz++OO8/fbbfPKTn6S+vp6jR4/237969WruuOOOsx53++23U1tb2//75s2byc3NZdKkSbS0tPA3f/M3/O3f/i3bt2/nq1/9Ko888gidnZ0AfOUrX2HSpEls27aNv/u7v+N3v/vdBT9vCZZCpEFXb4T1v/wZ9+kvYy2bRGDcbdJLKYaM3GwHM8cX8OLGOsLR5Icep1hs2CbejO2qJcS2v0Bk3X9ihC/s4iqEEEIMNW63m1//+tcoisLXv/51Zs+ezRe/+EXa29sH9PiPfexjjBs3DovFgsfjYf78+f2B8ejRoxw5coSbb775rMfdcccdbNiwgUgk9cHv6tWruf3224FUWL3hhhu48cYbUVWVOXPmMHnyZDZu3EhjYyO7d+/mH/7hH7DZbFx99dUfeP7zkWApxBXW1tTM8d88zlXWOoJTlhIrlLmUYuipKPJQUejhd6/XkdTP3ROp5pZgn/sgitVB6IV/JXFoq/ReCiGEGNaqqqr493//d15//XVWr15Na2sr3/nOdwb0WL/ff8bvd9xxB3/4wx8AqK2tZcGCBTidzrMeN2rUKKqqqnj11VeJRCJs2LChv2ezsbGRtWvXMmvWrP6vt956i7a2NlpbW8nOzsblcvWfq6Sk5IKfs6wKK8QV1LL7L5hbn0HzjCcxYQ4o8tmOGLqmjPGxdU8zq7cc5a65o8/5v7OiWbBOuAGteByxt14mcfgvOG54CDUr98oVLIQQQqRBVVUVd999N8899xwTJ04kGo323/dBvZjKX3U4XH/99XR2drJv3z5qa2v5l3/5lw/9W4sXL6a2thbDMBg7diyjRo0CUmF1yZIlPPHEE2c95uTJk/T29hIOh/vDZWNj41l1nI+8qxXiCjCTcdrW/Yzk1hU0FN2Is2aehEox9CkK104sIhCO86cdDQykE1L1+rHP+TSKw0Poxa9L76UQQohhp66ujp///Oc0NzcD0NTURG1tLdOmTaOmpoY333yTxsZGAoEAP/7xj897PqvVyqJFi/jud79LT08Pc+bM+dBjb7vtNrZs2cJvfvMbFi9e3H/7nXfeyauvvsqmTZvQdZ1YLMa2bdtobm6mtLSUyZMn8+STTxKPx9mxY8cZiwANlLyzFeIy07tO0v38o5yoO8zRiiXkjxqb7pKEGDSapjJnSjENrUG27mke0GMUzYK1ei72WR8n9tbLRF75PxjhD15lVgghhBhq3G43O3fuZOnSpUyfPp177rmH8ePHs2zZMubMmcNtt93GnXfeyd13381HPvKRAZ3zjjvuYOvWrSxatAiL5cMHnRYWFjJ9+nTeeecdbrvttv7b/X4/Tz31FD/+8Y+ZPXs2N954Iz/72c/6tzP5/ve/z86dO7n22mv50Y9+xF133XXBz1sxB/BRcX19PcuWLaO7uxuv18vy5cuprKw84xhd13niiSfYtGkTiqLwhS98gaVLlwLwox/9iDVr1qCqKlarlS9/+cvMmzfvggrt6AhiGPKpdqYoKPDQ1hZIdxkZL35gE5Gtv2FzeAzZY2dSWui5LH/H7bYTDMYuy7nFxRlpbRKJJdnw9glmTypi+riCAT/O1JMkD71B8sQuHNc/gKXq2gseejMQ8pqVeaRNMo+0SWaR9sg8BQWX533ccDGgOZaPPfYY999/P0uWLGHVqlU8+uijrFix4oxjVq9eTUNDA+vWraO7u5u77rqL2bNnU1ZWxtSpU/nc5z6H0+lk//79PPDAA2zevBmHw3FZnpQQ6WYmYkQ3PUP05EFeDs5k/IQq/PnudJclxGXjtFu4cVoJG945idNupbrCO6DHpeZezkMrqiL25oskjmzHMe+zqM7sy1yxEEIIIQbTeYfCdnR0sHfv3v4xuosXL2bv3r39e568b82aNSxduhRVVfH5fCxYsIC1a9cCMG/evP6Vi6qrqzFNk+5uGfYkhie98wShlx4j2N3NL7tmUl0zVkKlGBHcLhvzpvhZ9+ZxDh2/sG1F+leO1ayEV/4ridJRw0EAACAASURBVCNvXqYqhRBCCHE5nDdYNjU1UVRUhKZpAGiaRmFhIU1NTWcdd/qytH6/v3/C6ulefvllKioqKC4uvtTahcg48f0bCa/+N3p8k/jliUqunlRGcZ7sTylGjtxsB/Om+lm7vYEDDRf2AaKiWbDW3IT1qiXE/vJbIn/6EUZUhoEJIYQQQ8EV3W5k+/bt/Md//Ac///nPL/ixeXnS45NpZJz5KUYiRvsff4x+fD/RaZ/g+c1tzJ9Vjr/gyoVKt9t+xf6WGJiR2iZut52PZtlZ+5ej2O0WplcXXtgJvOMwyysJ7HqV6Iv/H/mL/oasCddecl3ympV5pE0yj7RJZpH2EEPJeYOl3++npaUFXdfRNA1d12ltbT1r406/309jYyNTp04Fzu7BfOedd/jnf/5nnnrqKcaMGXPBhcriPZlFJpSfYnQ3EVn3JIrbR8uYJfzu9ePMnlSMx2m5You3jLSFYoaCkd4mNk3hxmkl1G6pJxCMMaUq78JPMmYumreS1ld+hvbOa9jnfhrVcXFvsuQ1K/NIm2QeaZPMIu2ReSTon9t5h8Lm5eVRU1NDbW0tALW1tdTU1ODz+c44btGiRaxcuRLDMOjs7GT9+vXceuutAOzatYsvf/nL/Od//ieTJk26DE9DiPRI1G0jtOoJtPKptBTfwO+2pkJlkc+V7tKESLsct52bppeycWcj7xxsu6hzaL4y7PM+A0D4eZl7KYQQQmSqAW03UldXx7Jly+jt7SU7O5vly5czZswYHn74YR555BGmTJmCrut861vfYsuWLQA8/PDD3HvvvQB8/OMf5+TJkxQVFfWf87vf/S7V1dUDLlR6LDPLSP8UzdQTxN74NcljO7HNvJPGWBYvvX4kbaFypPeOZSJpk1OC4Tiv72pifHkOH5lRxsXuJqJ3niCx6xXU/FE45n3mglaOHemvWZlI2iTzSJtkFmmPzCM9luc2oGCZCSRYZpaR/GJnBNqIrPsvFJsD69SP0tid4MWNR7huUhHFvvQs1CMhJvNIm5wpHtfZvKeJ7Cwbd1xfidVy3gEzH8jUEyQPbiV5cs8F7Xs5kl+zMpW0SeaRNsks0h6ZR4LluV3RxXuEGOqSx94hsvFnWMdcizb6Khrbw6lQOTF9oVKIocBm07hxWgk7DrTy7J8O8ombqnA7rRd8HkWzYq25Ea14HLEdL5E4tDXVe+m+iDmcQgghxBX20OPraO+ODPp5871Onv76wgEdW19fz7Jly+ju7sbr9bJ8+XIqKysvuQYJlkIMgGnoxLa/QPLQVmwzl6D5ymhsD/Hi63VcW1MkW4oIMQCapnJtTRHvHe3il68c4O4bqijyOS/qXO/ve5ms20boxa9ju+pubJNuRlEuridUCCGEuBLauyN852/nDPp5v/bfWwZ87GOPPcb999/PkiVLWLVqFY8++igrVqy45BrkCizEeRihLsKr/x29+SD2eQ+i+cpo6uupvGZCIf58CZVCDJiiMGm0jylj8nhuwyHePtjGxU7IUFQN67jrsV/3SZL7NxJe9QR6V+Pg1iuEEEIMIx0dHezdu5fFixcDsHjxYvbu3UtnZ+cln1uCpRDnkDyxh/BLj6F6i7Fd/XEUm4vmjjAvbKxjVnUBJfmyv6oQF6OiyMP8q8p4+2AbL286QiyuX/S5VE8+ttmfRCscS/j33ya6/QXMZHwQqxVCCCGGh6amJoqKitA0DQBN0ygsLKSpqemSzy3BUogPYBo60e0vEN3wE6zTbsc67noURaG5I8zK11KhsrRAQqUQl8LjsjF/Zhmg8PM/7qOxPXTR51IUBUvlDBxzP4PRWkfo+a+RPL5r8IoVQgghxDnJHEsh/ooR6iLy5/8GPYF93oMo9tRQVwmVQgw+TVO5qrqAE61OXnitjhnj8pk9uRiLdnGfeypOD7aZd6K31hF9/Wm0gjHY5zwAspKfEEIIgd/vp6WlBV3X0TQNXddpbW3F7/df8rmlx1KI0yRP7CH84mOoOUXYrvmEhEohrpCyQje3Xl3OibYQP1+znxOtwUs6n1ZYhf2Gh8BqJ/TCv9L9l1WYenKQqhVCCCGGpry8PGpqaqitrQWgtraWmpoafD7fJZ9beiyFAEw9mVr19fBWrNNvQ8sf1X9fU/upOZUSKoW4fJwOK3Om+DnRGuTlzfWMK83hphml2G3aRZ1P0axYq+ehlU4ktP914m++guP6T2GpmDrIlQshhBADk+91XtAKrhdy3oH6xje+wbJly3jqqafIzs5m+fLlg1KDYpoXux7fldXREcQwhkSpI8Jw2rTX6Gkhsv4psNiwTVuEYnP139fUHmblxsNcXV2Y0aHS7bYTDMbSXYY4jbTJpYkndHbVddDUEWLuVD9TxuShqspFny8nx0nnwd0k9r2KmluK4/pPoeYUD2LF4kINp+vIcCFtklmkPTJPgUyrOCfpsRQjWuLgFqJv/Brr2OvRKmegKKfeuJ5oDfLSpiMZHyqFGI5sVo1ZEwrp7InwzqF2duxv5aYZpYwpyUG5iHypKApa0VjU/EqSR98m9LtvYa2+AfvMO/qHvAshhBDi4kmwFCOSGQ8T3bQCvbUO+7X3oGYXnnH/seYAqzbXc93EIorz5E2nEOniy3Fy84xSTraH+PNbJ9i2r4WPTC/Dn+86/4M/gKJZsFZdg6V0IolDWwn+9v/FNu12bJMXoFhsg1y9EEIIMXJIsBQjTvLkXqKv/V/U/NHY534aRbOecf+Rxh5qtx7j+snFFOZe3JtXIcQgUhRKC9yU5GVxpKmXl16vw5ftYPbkYkYVeS6uB9PhxjZlIcboq0ge2ETovT9hv/rjWMZej6LKunZCCCHEhZJgKUYMMxkntn0lybptWKfcilY45qxjDh7v4ZXtDcyb6icvZ+CToIUQl5+iKlSV5jC62MOxlgDrtjdgs2rMnlTM+DIvykXkQdWdh+2qu9A7TxDf9QrxnWuwzfo4lsoZKBdzQiGEEGKEkmApRgS97SiRDf+DmuXDPu+zKLazQ+N79Z28+vZJ5k3148t2pKFKIcRAqJrK6JIcRvuzOdkeYsvuJja8fYLpY/OZWpVHltN6/pP8Fc1Xhjr7kxitdcTefIH4jpewzbpbAqYQQggxQBIsxbBm6glib/+exN4NWCfejKV04gce9+a+Vrbva+HG6SXkuO1XuEohxEXpGyJbWuCmsyfKkaZetu1rYVSRhxnjCy54mGz/Aj+FVRgth4ltl4AphBBCDJQESzFs6c2HiGz8GaozG8fcz6A4z14i2jTh9Z2N7DvWxfyZZbguoqdDCJF+vhwHvhwH06ryONYSYP2O48QTOjWjfEys9JFzAUPbFUVBKx6HWjQ2FTDffIHY9pXYp92GZdzss+ZlCyGEEAN17Mm/Qe9tH/Tzatn5jPpfPz7vccuXL+eVV17h5MmTrF69mvHjxw9aDRIsxbBjxiOpuZRH3sQ68WZUf/UZ24i8zzBMXtl+nMaOIPNnll30JuxCiMxhtWqMLfMytsxLTzBGQ0uAFzfWYX/jKNXlXsaXeynwOgfUk3lGwOw4Rnz/RmLbX8A6eQG2iTejOGQbIiGEEBdG723H/8A3B/28Tb96bEDHzZ8/nwcffJBPfepTg16DBEsxrCQb3iW66ReovnLsNzz0gXMpAZJJg99vPUookuCm6aVYLRIqhRhuctx2prjtTBmTRzhhcOBYJy9urANgbGkO48q8lBe50dRzp0xFUdDyK9HyKzF620jW7yC4649Yx87GOnE+mq/0SjwdIYQQ4pLNmjXrsp1bgqUYFoyeFqJbn8XoOol18kK0gsoPPTYcTfLixjpsVo25U/xomsybEmJYUxQKfS5cNo0ZY/PpCcU52R7i1XdO0BOKU5qfxRh/DqP8HvKyHefszVSzC7BN+yhmZC7Jhp1Eav8dJacI26T5WEbPkmGyQgghRiwJlmJIMxOx1OI8+17FUnUt9ikLUdQP731s747wwsY6Koo8TK70cVEb4Akhhi5FIcdtJ8dtZ2Klj1hcp6UzzLGWANv2NWOaUFHkpqLIQ3mhm1zPBwdNxenBWj0Xy7jZGC2Hie/+E9Gtz2IdNxfrhHloudKLKYQQYmSRYCmGJNM0SdZtI/aX36L6ynDc8FkUx9mL85zuaFOA32+tZ8bYfEYVZ1+hSoUQmcxu06go9lBR7AHTJBBJ0NoV4eDxbrbsbkI3oDQ/i7JCN6X5LopyXVgsp0Y5KKqG5q9G81djBDvRj+8iUrscxZmDZfwcrGOvQ3V50/gMhRBCiCtDgqUYUkzTRD/5HrFtz2PqCazTb0fzlZ33ce8eamPTrmbmTPJTkDvw1SGFECOIouBx2fC4bFSV5gAQjiRo7Ylwsi3E7iMd9ARj+DwOSgtcFPvcFPuc5OU4UFUF1e1DrbkJy4QbMNob0E/uJf7WKrTC0VjHzsYyaoYs+COEEGLYkmAphgy95TCxbc9jBDuwjp/7oau9ni6pG/zpzeM0tASZP7MUt8t2haoVQgwHLqeVSqeVyuLU77pu0BWI0dEbZV9DJ1v2xAhHExR4nRT7XBT7XBTmusjzjcJWUIk5aT56y2ESB7cQ3fosWl4FljFXY6mcierOS++TE0IIMeI88cQTrFu3jvb2dh566CG8Xi9/+MMfBuXcimma5qCc6TLr6AhiGEOi1BGhoMBDW1vgivwtvf0YsR0vYbQdxTJuNlrZFBT1/Avu9ATj/O71IzjtFq6eUIBlGK/86nbbCQZj6S5DnEbaJLNczvZIJHS6AjE6A1F6QnG6gnGC4Tg5bhtFXheFPheFuU4KPRbsgRPoLYfQW+pQPXlo5VOxlE1BKxqLoo2sz3qv5HVEDIy0SWaR9sg8BQXnnnY1EOnex/JyGtBVrL6+nmXLltHd3Y3X62X58uVUVlaecYyu6zzxxBNs2rQJRVH4whe+wNKlSwHYvHkzP/jBDzh48CCf/vSn+epXvzroT0QML+8PeY2/+weMrpNYRs/COmnBgN941Tf2UvvGMSZUeKku98oiPUKIy8Zq1VLh0efqv03XDXqCcbqCMRrbQ+w72kVXMIpFUynIGU9h3hTKbb3kt7fgOLoCM9SJVjweS/lUtJJq1NxSFEVWrBZCiOEm3eHvchrQu/THHnuM+++/nyVLlrBq1SoeffRRVqxYccYxq1evpqGhgXXr1tHd3c1dd93F7NmzKSsro7y8nG9/+9usXbuWeDx+WZ6IGB5MQyd55E3i79ZiJmJYxlyNdfrt51zp9XSGYbJ1dzPv1rVz/aRimU8phEgLTVPx5Tjw5ThO3WiahKNJuoIxeoJx3u6x0x0qJhDykZ9lMr69l7KubeS8VYvFiKEVVWHxT0gtDpQ/CsUiQ/mFEEJkrvMGy46ODvbu3cvTTz8NwOLFi3n88cfp7OzE5/P1H7dmzRqWLl2Kqqr4fD4WLFjA2rVr+fznP8+oUaMAWL9+vQRL8YGMQDvxA6+T3P96ajXFqmtRC6vOO4fydF29MX6/9SiqArfMKsdpH1nDyoQQGU5RcDmtuJxWSgtO3WzoBr3hBN2hGO8GK+hNJIiFevAd7qDy5Dv4tQ249V70rAKsRVXYS8aiFYxO9WqOsOGzQgghMtd5r0hNTU0UFRWhaakeI03TKCwspKmp6Yxg2dTURElJSf/vfr+f5ubmy1CyGC5MPUHy2Dsk9r6G3n4UrbQG21V3oeYUXdh5TNh5uI3XdzYxqdLHuLIcGfoqhBgyVE3F67Hj9dhPu7WEeEKnJxTnvWCMQDCCGmzDvq+dooNHKbIEcBMk4chD85XhKhmNJa8cNbcMxe2TYbRCCCGuuCHzUWdenizRnmkuZgKzmUwQObqL4L43CB/agdVbhGfMNBw33I1isV7w+QKhOC+8eoju3hiL5475qzdmI4vbPXKfe6aSNsksQ7E9fLmu036rBNMkGE7Q3BulqztIorcd9UQ7WUffosi2GZ8SwEoCPAU4C8twFlZgzS/FmuvHmluM6nRf0EiQy20wFsIQg0vaJLNIe4ih5LzB0u/309LSgq7raJqGruu0trbi9/vPOq6xsZGpU6cCZ/dgXqorvSqsaZqQiGJGelNfiQhmMg7JeN/3GKaeBJS+zjEl1UumKKDZUKx2sNpRLKkv7C4UuxvFkYWiDpk8/6EuZKUyMxYieWIPySNvkjyxBzW7EK1oLNbZn0J15RADYsEEkBjw3zcNeOdwG5t3NVFVksNHppegKozYVThlBdLMI22SWYZbe/g8dnweO5AHVJNM6nSH4hwNxAkFg+i9HWgt3RTYdlJg20aOGsGpB1AUBS07HyW7EDW7CDW7ANWdh+LJR3Xnp65dV4iseJl5pE0yi7RH5pGgf27nTTh5eXnU1NRQW1vLkiVLqK2tpaam5oxhsACLFi1i5cqVLFy4kO7ubtavX8+zzz572Qq/VKZpYkZ6MHpbMXta0HtbMbubMQJtmJEezGgQFAXFnoViy0qFRM0KmqXvy3pqqFHfji0mZupnPQlGApJJTD0BehwzEcWMRyAeAYstdV6HB8WVg+Lyorq8KC4viisHNSsXJSsXxZk9JIczmdEgyeYD6I370Rv3YfS2ouZVoBVW4bjp8yj2rEs6f3NnmLXbGsCEj8woJWcI9kIIIcRgslg08nOc5Oc4gRygFNMwCUQSNAai7A3G6A7ECAWCeCMRykNJirqayLEcw0UELR7ADPeAxYaa5UNx+1Dd+aiefBR3Hqrbh+LOQ3HmDGi7JyGEECPPgPaxrKurY9myZfT29pKdnc3y5csZM2YMDz/8MI888ghTpkxB13W+9a1vsWXLFgAefvhh7r33XgB27NjBP/7jPxIMBjFNE4/Hw7e//W3mzZs34EIvpcfSNJIYXU0YHQ3o7ccw2o+id54ARUmFOJc3dbHMyk0FPYcbxea6LCvwpXpCY6ke0HgYoiHMWBAzFsKMhzGjIcxYADMSgEQ0FS5d3tMu9D4UV27qZ1dfvWlYKbCgwENray9mqBOj8zh6x4nUf9/O45jBTlRfGWpuKWpeOarXP+BVXc8lEtPZvKuR/Q3dTB3jY7Q/W+ZS9hluvTHDgbRJZpH26GOahKJJugIxugJRuoIxOntjKIpCodfBqFwVv9sgzxbHYYQxogGIBDCjgdTonXg4dV3K8vX1dOalAqg7rz+AYnMNaLit9MZkHmmTzCLtkXmkx/LcBhQsM8GFBEszHkFvrSPZtB+j6QB6+zEUZw5qdmFquE92YepnR2bP2zT1ZCp0vn9RjwYwYyGIBjGjwf7b0Kyp59fX+6k4PChOT9/Q274vmxMs9lQI7f9uBd6/+JunviVjmPEIZiIK8UgqBEcCGKFOjEA7ZrADNdpDoqcNRbOmhlS581CyC1A9BSie/EEJku9LJAx2HGjlzf2tlBe6mTI6D5tt8M4/HMib5swjbZJZpD3O4bRtUDp7U2GzozeGpioU5Topyc/Cn+ei2JeF05oalWJGejGjqakiRIMY7wfPcDcoaip4evJQPaeuC+r714i+USvypjnzSJtkFmmPzCPB8tyGRbA09QR68yGSx3ejn9iD0duC6vWjektRfSWpJdmtjg987FDXPxc0Fuzr7QxBPJwaepuIpu6LR/vmhCZAT6TmiPb9fCpYnsbSN0f0/fmhFhuKzdkXUj0oDg/ZhYUEk6nbLxfdMNl5uJ2te5opyHEyebQPT5bs4/ZB5E1z5pE2ySzSHheor2ezMxCjszdCdzBOR28Uu1Wj2OdKhU2fiyKfC/tpH/T1j8qJ9KS+wr0YkV6I9mKEezBDXanRQu58HPl+Es581Jyi1Ae+OUWpkUNDcArIcCFBJrNIe2QeCZbnNmRXkTF620g2vEuyYRd680FUTz5qfiWWCTeg5hSPmL29FEUBmzMV8DwF53/AILF6XSjd4cty7kTCYHd9B9v3teB2WJk7xY8ve3h+MCCEEBlJUchyWslyWikv7BvdY5oEwgk6AlGaO8LsO9pFZyCK22XD73NSku+mOM9FkdeJJacIPmDrqP4PQ8Pd2IgQb2sheewdzHA3ZqgrNdTWnY+aU4zq9aN5/Sje4tTvzuwr/B9BCCHEhRgy6cs0TfT2YySOvoVe/xZGuButsAq1cDTWiR+5rD1n4soIRhK8daCNnYfbKfA6uXpCEQVeaVchhMgIioIny4Yny0Zlceom0zDpCaXmaTa0BHj3UBs9oThetx1/ngt/XhbFeS4KvE40VTnjw1Cn10XMO+aMP2Em46mQGezECHWRqH8TM9SFEWgHVesLnCVovlJUbwmqrxQly5dRW6gIIcRINWSCZeT33ybZ24FWNBZLzU2ouSUyXGYYME043hpk1+F26hp7qCjKZsFVZbhdMuRVCCEynaIqeD0OvJ5To0p03aA7GKMrEOPwyR62728lGI7j8zhSYTM/i6JcFx7P2R8cKhYbSnYhZBdy+kx60zQhHsYIdGAGO0i2HMY8sh0z0I6ZiKF6i1Fzy9DyylBzy1B9Zak1ByRwCiHEFTNk5lg2b9+AYcuSi0SG8HpddF/CUNjuYJw9RzrYXd+BRVWpLPYwujhbFuW5SDJ/LPNIm2QWaY/0Suo6XYE4Xb1RuoMxOgNxgtE4PreD4jxX6ivXRb7XiUW7sOu8GY9iBNsxA+0YwVTwNHrbwDRSw2nzKlDzytF85anAKSOcPpTM6css0h6ZR+ZYntuQ6bFU3T7MRDzdZYiLZJrQ2Rvl0IluDp3ooSsQo6LIw/UTi8n12GXbECGEGMYsmkaB13nG9Aa7w8rJ5l46AzEOn+jhzf2tBEJxctw2inNTq9AW5jopzHWesUDQX1NsDjRfGfjKzrjdjIVSK5n3tpFs2EnivT9j9LaiODypLbHyK9DyKtB8FSjZhbI/pxBCXKIhEyzF0JNIGJxsD3GkqZfDJ7qJJw1K87OoLvdS6HWianIRF0KIkcpqUcn3Osk/LWzqukFPME5XMEZDa4BdR9rpCsRw2i0UeJ0U5zopyE3N2cx12znXjBjFnoVmz4L8Uf23maaBGerGDLRh9LaSaDpErLcVMx5KrSbvq0DLH9XXw1nWvzWKEEKI85NgKQZNNK7T1BGioTVIQ3OAtu4IvmwHhV4n19QU4ZOeSSGEEOegaSq+HAe+nFNzNk3DJBhN0B2I0R2M0dAWojsQIxpP4vM4KPA6UmEzx0F+jgO3y/ahlxpFUVHcPnD70PzVp/5GIoYRaMPsbUU/+R6J/a+lejftWai+8tN6N8tRsoukd1MIIT6ABEtxwUwTAuE4x5oDtHSFaWoP09wZJhRLkOdxku91MKEil7lT/GjSKymEEOISKKqCx2XD47JRXnRqflMiqdMbitMdjNPYHuJAQxfdwThJ3cCXnQqZBV4HPo+DvGwHOW4bqvrBiVOx2s8aTmuaZmoLlPd7N5v7ejejfb2beeWp+Zu+MtS8clSHzL0SQoxsEizFh0okUiv7vT8sqaMnSltPhM7eGIoC2Vk2vG47Po+dqpIcPC4ryodctIUQQojBZLVo5OU4ycs5czGeeFynJxynJxSnuSPMoePd9IYThKNJsrNs5Hrs5GU7yM224/M4yHXbPrCXU1GU8/RutpFs2o95cHOqd1OzofpKUX3laHnlqLmlqLmlKFb7lfjPIYQQaSfBcoRK6iahSIJgNEEwnCAQitPbdyHuDSUIhOPEEjoelxW304rLYSXbZaWmIpdst438XBfBkCymJIQQIrPYbBoFNudZ+yDrukEgnCAQSV3zDp/oIRhuJxBJEE/ofR+WpoJnrjvVw5mTZSPbbcNuPbV40If2bkYDmL1tGIE2EnXb+laq7UBxZqdCpq8MLbc0FT69fhSLBE4hxPAiwXIYSSYNwrEk4Wjy1PdogmAkSTASJ/T+79EkiaSB06bhtFv6v1x2jYIcJ6OKPGQ5UredY6LKlX1yQgghxCXQNBWvx47Xc3agSyZ1gpEkoWjqg9WG1iDh4wlCfddPTVXxuKxkZ6XCZk6WrW94rhWPy0aW04rFmQ3ObLSiqv7zmoaBGe5KbYUS6CDRvqV/SxTFmYPqLUHNLUXLLUHNLUkFTlkwSAgxREmwzFCmCfGETiR2KiRGYqmv9wNi6P3b40ki0SSGCQ6bhtOmYbdq2G19X1YNr9tOsc+Vut9uSX36KuFQCCGEwGLR8Hq0DwydmCbRhE44kroeh6IJmjvD1DcFCMcSfddhHbtVI8thIctpxeO04nalvmc5rWQ5SnH5K3E5LNgtGph9gTPYgRHsIHHkTcxQJ0agjf+/vfuPjbMu/AD+fn4/d9cf12tpe2XqRL6YAmZ8XYGYqUgZdoSurS5kOl0iGyNmYQOV6BRluo3EilFMNkWRkJgYTRR3Y6UuEwcJLl/JFjRLs+FMgTnWW7v1113vx/Pz8/3jubt1g9FJgadd36/kcs+vWz63z55nfffzS1INSLVNkOMtQeCMN0OuTUKqboAkc61nIpq7GCzfJ8IHCnYQCPNWKTAWHRQsD7miE4RFy0XBCo4VbQ+KLMGcHhJVBbouw1AVxEwNiRoThq7A1IJrNFVmWCQiIno3SRJMXYWpq0hc5BLhB+GzUP5lr+1hquBgdLKIgu3Bsl0U7eD/fgBBTyFdRcRUUWUmETU/iGidikiTgirJQtTPwHQz0E8dhzx4CMiNQhSnIFUlINc2Qa5NlgJnM+SaRkixBGeqJaLQMVjOgucL5AtBKJwqOMFvMgsOpkrHghDploKiC11VgqBYDoO6Al2VYWgK6msMtGjRyjldUzijKhER0TwgyVJlWMlMXNdD0fFg2cEvkW0neM8WbDiOD8v1YdsSik4VbCcCy70Cqvw/iOlAfbaIRKGIupE0qqVBxEQOETcL1bfgGHF4sQagpglqbTO0RBPMRBJa/ApIMn/cI6L3Hp80b0EIIG+5yObsYGKbgl0Z7D99vKLt+pWup2ape2k5MNbXGLiyIQZTD0KiqSmcMZWIiGiBU1UFVaqCqsjM1wIAhIDj+rBcD7btw3Y9TLk+xlwftu3B1J5TSAAAEqRJREFU9jwIx4HmZGFMZBAZHYHpv46YyKFGyqNKKmIKEUxKNcjKceS0BGyzDm6kAaKqAUa0GtGIhqihIjlegGM5iBoqomZw7GJLtBARXWhBBkvPF8jmHWSmLEzmbExO2cGyGrkgQOYKDjRVrkxgEzGCLjAxU0NDbSTYN4KwyK6nRERE9J6RJGiaAk1TgEsNoyWOEDjjOPDzGajFSdQXJ9FoT0KxT0HPZWAOZyEgYUqqxqRUg0GpFmfcKgw7VRi2IzhtR6FoOiKGipgZhM3yONKqSHnWeBVVFxyPRTToqgyJPyMRLSiXbbB0XR/jWQvjUxYmshbGMqXtnIVcwUXUUBAzdcRMBREzeBA2JaKl39Kp7IZKRERE85skQdV1QG8A0FA57AMoAigKAcmzoFgZJKwMFokc3OwEZPskFCsDxcrA06Kw9TgKeh3yahxZpRYZvwbjU1U4ORlD0RUolrr1Fi23NJ9EMJa0HETLIbQ8i255cqOqiF5Z1qwqosHUFYZRonlsXgdLIYBM3sbYZBFjWQujkwWczViYyBZRsLzKQ6z8wLoqWYOqqIaYoUJmcCQiIqKFTJIgVBOuagKxRuhVBqamrHPnhQ/ZzkGxs9BK4bPBPgPFykKxMpCdHDyjGp6ZgFtTDy9SDzdaDy+SQFGrQ06KIG/7KFpeMKmR5WJyysbwWL4ymdH02e89X5ybVXdaEK2N6cEsu1H93PFSy6jKn+eI5ox5EywnpiykRyYxOlnEmckCRieLGM9a0DUleOCUHkRXt9SgKlqPmKFxTCMRERHROyXJ8I1q+EY1UN3y5vO+B9nOBkHTzkDNn4E+8SpkK4uElYHsFuGZcbiRBLxIAm60AV59Am4kCJ++XnXekCLH9c8Lm+Xt4fE8Tgx7KNjlWfXPnTc0GTGz1AJqBqGzJnouiMZMDVWRc62mDKNE7515Eyz7/u8EhGOjOqqhNqrjg1dUoTqmQ1O5phMRERHR+05W4Jtx+GYczlud9xwodql1085Cyw7BGD1eavGchOQ7cM06eJG6IGxG61ETScA1E/ASCfh63dvOZSGEqLR8lmfhL4fRiSkrODetZbT80lQFUTMYNxozy2uNThsjap6bvChqqogawdIwUUNlKCV6G/MmWN76v4tgF4thF4OIiIiILoWiwSu1Vr4VybMhW1kodhaylYGWeQPG2VcqXW3hu/DMOLxIXdDdttTN1jPr4Ebq4Bs1lUkWL7bG6IWEELAdH4XS2qLBuNBgjGi+4GAsU4TleLAcH5btBdt2aek4x4MsSZXVACK6UprgUamUI2IolUkfTV0J3o1guTlTC/YNPdhnSKXLzbwJlkRERER0+RCKDi8atFS+lfOD57QWT3sKcrmrrVFTCp+JoMutGYdn1pXe4xDa+VPpSpIEo7Sm+H9dXiHgeqIUPIM1SIN3v7KfyTkYnSzC9nw4rg/b8eG4wTW2e/61lbJowau81nmkFErjtRHA94OgWjpfDrRmOdDqQZg1dS4NQ+FjsCQiIiKiOWem4AnfhWxPBUHTzkIpTkLLDpWOBWEUkgTPqK0EzXOvWvhGLTyjFr4eA6SZWw8lSYKmStBUGVURbXbfTQh4voDteLCnB1C3HEg9KIqMyVywuoHt+nDdUjgtnbccH5bjwnKCfV2VYejnWlIjhnquO2+p22+5a2/wriFS6RKscngZzRqDJRERERHNP7JaGeP5lkrLqQRBMwfZmYJSnAjCp5MrzXg7Bcmz4OtV8PQaeEYNfKOm1BJaA1+vgW9Uw9Or4OtVEIrxrqxhLkkSVEWCqsiIXuSaeDyKiYn8Jf15QohpgbPcldcthU8PRcvD5JQNy/WmdfH1KuNQi5YLSZIQNZRgfOm0MajTJz6qrGcaKY1PLYVWRWa3XrrEYPnaa69hy5YtmJiYQDweR29vLxYvXnzeNZ7nYceOHXjxxRchSRLuvfde3HXXXTOeIyIiIiJ615WWU/FUE1604eLX+V4QNJ08ZDsH2S1AKY4FAdTNQ3YKkJ08JCcPCQKeFoOvx+BrMfh6VfBuVMPXosG2FiltR+GrkaA77iW0iM7uq0qVLrXV7+DzQgg4nl8Jm8HLrWwPj+dRHPFg2UFgDQJpMPbUsl3oWtBKGjVVxAy1FELPTYpUbjWNGtp5kyFFDRW6JnP90svEJQXLrVu3Ys2aNeju7saePXvw8MMP4ze/+c151+zduxf/+c9/sH//fkxMTKCnpwef+MQnsGjRorc9R0REREQUGlmBX2qpnJHnQHYLQdh0C5BcC7JbhJIbgebZkNwiZM+C5FrBtmtB8iwIRYNQTfiqCaGY8DUz2FdMCNUIjqsGhGJAKDp8RYeSr4ZeFBCyFnxe1gBZrWwLWQ0C67vUgqqrCnRVQfXFmlAvQohg3Gk5hFql0Fnen8zZ08al+pXAWv6M5/mVLrzmBZMfRQz1vHGkRmkSpPLYVF2TS+/BvqbK0FUZqiIh+Fvxg4XvhQAg3rwdfAGI6fulY6WNC77tO4ntC8eMwXJ0dBRHjx7FU089BQDo7OzE9u3bMTY2hkTi3Bxc/f39uOuuuyDLMhKJBJYvX459+/bhnnvuedtzl8ovTMLLX1p3AHrvFV0NnvWWk4tTCFgfcw/rZG5hfcw9rJO5h3VyaTwAkHRA04FLGGopCQHJdyELB7LvQPZtyL5bejlQ3ALgZCALD7LvQiq9y8JDRATXzDY6CkgQkgxAgpCmbUMCyq8LA2pp/8JohdInzgti5StF+RMCUum9/HcARQAKIBml88IvXXMRdun1X/JKr/fEfY+/V3/yZWHGYJlOp9HU1ARFCQb0KoqCxsZGpNPp84JlOp1GS8u5xXOTySROnz4947lLtbTjjv/qeiIiIiIiInp/cKQtERERERERzcqMwTKZTGJ4eBieFzQqe56HkZERJJPJN103NDRU2U+n02hubp7xHBEREREREc1vMwbL+vp6tLa2oq+vDwDQ19eH1tbW87rBAsCKFSvwhz/8Ab7vY2xsDM899xw6OjpmPEdERERERETzmyTEm0bevsng4CC2bNmCTCaDmpoa9Pb24qqrrsKGDRuwefNmfOxjH4Pnedi2bRsOHjwIANiwYQNWr14NAG97joiIiIiIiOa3SwqWRERERERERBfDyXuIiIiIiIhoVhgsiYiIiIiIaFYYLImIiIiIiGhWGCyJiIiIiIhoVuZ0sHzttdewevVqdHR0YPXq1Xj99dfDLtKC09vbi/b2dnz0ox/F8ePHK8dZN+EYHx/Hhg0b0NHRgZUrV+K+++7D2NgYAOCf//wnurq60NHRgXXr1mF0dDTk0i4cGzduRFdXF3p6erBmzRocO3YMAO+TsO3cufO8ZxfvkfC0t7djxYoV6O7uRnd3N1588UUArJOwWJaFrVu34rOf/SxWrlyJ733vewD4zArLG2+8Ubk3uru70d7ejptuugkA6yQszz//PHp6etDd3Y2uri7s378fAOtjRmIOW7t2rUilUkIIIVKplFi7dm3IJVp4Dh06JIaGhsStt94q/vWvf1WOs27CMT4+Lv7+979X9n/4wx+Kb3/728LzPLF8+XJx6NAhIYQQu3btElu2bAmrmAtOJpOpbP/lL38RPT09QgjeJ2EaGBgQ69evrzy7eI+E68L/Q4QQrJMQbd++XTzyyCPC930hhBBnzpwRQvCZNVfs2LFD/OAHPxBCsE7C4Pu+aGtrqzyzjh07Jm644QbheR7rYwZztsVydHQUR48eRWdnJwCgs7MTR48erbTO0Pujra0NyWTyvGOsm/DE43HcfPPNlf0bbrgBQ0NDGBgYgGEYaGtrAwB84QtfwL59+8Iq5oJTXV1d2Z6amoIkSbxPQmTbNrZt24bvf//7lWO8R+Ye1kk4crkcUqkU7r//fkiSBABoaGjgM2uOsG0be/fuxapVq1gnIZJlGdlsFgCQzWbR2NiI8fFx1scM1LALcDHpdBpNTU1QFAUAoCgKGhsbkU6nkUgkQi7dwsa6mRt838fvfvc7tLe3I51Oo6WlpXIukUjA931MTEwgHo+HWMqF46GHHsLBgwchhMCvf/1r3ich+tnPfoauri4sWrSocoz3SPgefPBBCCGwdOlSfP3rX2edhOTkyZOIx+PYuXMnXnrpJcRiMdx///0wTZPPrDngwIEDaGpqwnXXXYeBgQHWSQgkScJjjz2GjRs3IhqNIpfL4Ve/+hX/X78Ec7bFkoje3vbt2xGNRvHlL3857KIQgEceeQQvvPACvva1r+FHP/pR2MVZsP7xj39gYGAAa9asCbsoNM1vf/tbPPPMM3j66achhMC2bdvCLtKC5XkeTp48iWuvvRZ/+tOf8OCDD2LTpk3I5/NhF40APP3001i1alXYxVjQXNfFL3/5S/z85z/H888/j1/84hd44IEHeI9cgjkbLJPJJIaHh+F5HoDgQTgyMvKmbpn0/mPdhK+3txcnTpzAY489BlmWkUwmMTQ0VDk/NjYGWZb5W/8Q9PT04KWXXkJzczPvkxAcOnQIg4ODuO2229De3o7Tp09j/fr1OHHiBO+REJX/3eu6jjVr1uDll1/mcyskyWQSqqpWuvMtWbIEdXV1ME2Tz6yQDQ8P49ChQ1i5ciUA/rwVlmPHjmFkZARLly4FACxduhSRSASGYbA+ZjBng2V9fT1aW1vR19cHAOjr60NrayubmucA1k24fvKTn2BgYAC7du2CrusAgOuvvx7FYhGHDx8GAPz+97/HihUrwizmgpHL5ZBOpyv7Bw4cQG1tLe+TkNx7773429/+hgMHDuDAgQNobm7Gk08+iXvuuYf3SEjy+XxlrJIQAv39/WhtbeVzKySJRAI333wzDh48CCCY5XJ0dBSLFy/mMytku3fvxi233IK6ujoA/HkrLM3NzTh9+jReffVVAMDg4CBGR0fxoQ99iPUxA0kIIcIuxMUMDg5iy5YtyGQyqKmpQW9vL6666qqwi7Wg7NixA/v378fZs2dRV1eHeDyOZ599lnUTkn//+9/o7OzE4sWLYZomAGDRokXYtWsXXn75ZWzduhWWZeHKK6/Eo48+ioaGhpBLfPk7e/YsNm7ciEKhAFmWUVtbi29961u47rrreJ/MAe3t7Xj88cdxzTXX8B4JycmTJ7Fp0yZ4ngff9/GRj3wE3/3ud9HY2Mg6CcnJkyfxne98BxMTE1BVFQ888ABuueUWPrNC1tHRgYceegif/vSnK8dYJ+F45pln8MQTT1QmuNq8eTOWL1/O+pjBnA6WRERERERENPfN2a6wREREREREND8wWBIREREREdGsMFgSERERERHRrDBYEhERERER0awwWBIREREREdGsMFgSERERERHRrDBYEhHRvLd27VrceOONsG077KIQEREtSAyWREQ0r73xxhs4fPgwJEnCX//617CLQ0REtCAxWBIR0byWSqWwZMkSfO5zn0MqlaocHx8fx1e/+lV8/OMfx6pVq/DTn/4UX/ziFyvnBwcHcffdd+Omm25CR0cH+vv7wyg+ERHRZUENuwBERESzsWfPHnzlK1/BkiVLsHr1apw9exYNDQ3Ytm0bIpEIDh48iFOnTmH9+vVoaWkBAOTzeaxbtw6bN2/GE088gePHj+Puu+/GNddcg6uvvjrkb0RERDT/sMWSiIjmrcOHD2NoaAh33HEHrr/+enzgAx9AX18fPM/D/v37sWnTJkQiEVx99dXo6empfO6FF17AlVdeiVWrVkFVVVx77bXo6OjAvn37Qvw2RERE8xdbLImIaN5KpVJYtmwZEokEAKCzsxO7d+/GnXfeCdd1kUwmK9dO3z516hSOHDmCtra2yjHP89DV1fX+FZ6IiOgywmBJRETzUrFYxJ///Gf4vo9ly5YBAGzbRiaTwejoKFRVxenTp/HhD38YAJBOpyufTSaTuPHGG/HUU0+FUnYiIqLLDbvCEhHRvPTcc89BURQ8++yzSKVSSKVS6O/vR1tbG1KpFG6//Xbs3LkThUIBg4OD2LNnT+Wzn/nMZ/D6668jlUrBcRw4joMjR45gcHAwxG9EREQ0fzFYEhHRvLR79258/vOfR0tLC6644orK60tf+hL27t2Lhx9+GNlsFsuWLcM3v/lN3HnnndB1HQBQVVWFJ598Ev39/fjUpz6FT37yk/jxj3/MdTCJiIjeIUkIIcIuBBER0Xvt0UcfxdmzZ9Hb2xt2UYiIiC47bLEkIqLL0uDgIF555RUIIXDkyBH88Y9/xO233x52sYiIiC5LnLyHiIguS7lcDt/4xjcwMjKC+vp6rFu3DrfddlvYxSIiIrossSssERERERERzQq7whIREREREdGsMFgSERERERHRrDBYEhERERER0awwWBIREREREdGsMFgSERERERHRrDBYEhERERER0az8P82P5gNn7IYCAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "8nSAHBOM42hf" }, "source": [ "আমাদের প্লটের বয়সসীমা কমিয়ে নিয়ে আসি ০ থেকে ২০ এর মধ্যে plt.xlim(0, 20) দিয়ে। এরপর ২০ থেকে ৩০, ৪০, ৫০, ৬০, ৭০ এবং ৮০ পর্যন্ত। আপনার মেশিন, আপনার মনের মাধুরী মিশিয়ে তৈরি করুন একেকটা প্লট। নিচের দিকে দেখলেই বুঝবেন ০ থেকে ৮০ বছর পর্যন্ত বয়স দেয়া আছে নিচের এক্সিসে। \n", "\n", "#### ছবির মানে কী? \n", "\n", "আপনারা ভালো করে লক্ষ্য করলে দেখবেন \"০\" মানে মারা গিয়েছেন ৩০ বছর বয়সের মানুষ বেশি। আবার বেঁচেছেন ২০ থেকে ৩৪ বছর বয়সের মানুষ। নিচের চারটা ছবি দেখলে তাই মনে হয়। ডাটার ঘনত্ব ৩০ থেকে ৩৪ বয়সের দিকে।" ] }, { "cell_type": "code", "metadata": { "id": "f3If3mvi42hf", "outputId": "1bb4ef0c-ac00-45bf-8a1c-0c97c30bc1d3", "colab": { "base_uri": "https://localhost:8080/", "height": 220 } }, "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", "plt.xlim(0, 20)" ], "execution_count": 32, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(0.0, 20.0)" ] }, "metadata": { "tags": [] }, "execution_count": 32 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "7U3n4_ld42hg", "outputId": "e69f8969-0692-4ba7-ce86-5863429aef72", "colab": { "base_uri": "https://localhost:8080/", "height": 220 } }, "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", "plt.xlim(20, 30)" ], "execution_count": 33, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(20.0, 30.0)" ] }, "metadata": { "tags": [] }, "execution_count": 33 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "3KH1QFLF42hg", "outputId": "5986f2c5-d69b-4c53-b1d0-7117a907be9d", "colab": { "base_uri": "https://localhost:8080/", "height": 220 } }, "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", "plt.xlim(30, 40)" ], "execution_count": 34, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(30.0, 40.0)" ] }, "metadata": { "tags": [] }, "execution_count": 34 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "YCX6_BcQ42hg", "outputId": "b4b2004f-1079-4cd5-8935-c58c83edeadc", "colab": { "base_uri": "https://localhost:8080/", "height": 220 } }, "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", "plt.xlim(40, 60)" ], "execution_count": 35, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(40.0, 60.0)" ] }, "metadata": { "tags": [] }, "execution_count": 35 }, { "output_type": "display_data", "data": { "image/png": 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KFRUVOfvNnz9fmzdvVhAEamlp0c6dOzVv3jxJ0iuvvKL7779ff//3f6+3vvWt5+EyAAAAAACF0q+hsGvXrlVtba02bdqk8vJy1dXVSZKWLVumFStWaOrUqaqpqdHu3bs1d+5cSdI999yjSZMmSZLWrVunzs5OrV69OnPODRs26Kqrrhro6wEAAAAADLJ+PceyGDAUtrgwPKO40B/Fhz4pLvRH8aFPig99Ulzoj+LDUNizG9TJewAAAADgYnX7+h062tox4Oe9dFSpHv3S3H7tu2/fPtXW1qq1tVWjRo1SXV2dJk+enHcbCJYAAAAAMAiOtnboy5+aMeDn/cI/vNDvfdesWaMlS5aopqZGW7Zs0erVq/XYY4/l3YY+J+8BAAAAAAx9zc3N2rNnj6qrqyVJ1dXV2rNnj1paWvI+N8ESAAAAAC4CTU1NGjdunBzHkSQ5jqOxY8eqqakp73MTLAEAAAAAeSFYAgAAAMBFoLKyUocOHZLv+5Ik3/d1+PBhVVZW5n3uIRMs/UP/p+DUMQ2Rp6MAAAAAQFEZPXq0qqqq1NDQIElqaGhQVVWVKioq8j73kJkVNvnyj5Rs2iv5SdkjxsgaOU72qEo5I8fLGjle9shxskpGyLKsQjcVAAAAAHq5dFTpOc3gei7n7a+1a9eqtrZWmzZtUnl5uerq6gakDUMmWMbffrOca5IyqS6ZUy1h9fLUMaWO/kmmvVVBW7NkjOzysbJHjpc1qlLOyHGy06EzMbzQlwAAAADgItbfZ02eT1deeaU2b9484OcdMsEyzYolZI2qlD2q9zhgk+zoDp0njyiVHj7b1iw5biZ0hu/jZJePlVU+lkonAAAAAORhyAXLs7HipbLil8m+5LKc9cYYqetUGDLbjyk4eUT+of8LK52njkmBL3vEaFkjouA5cmwYPsvHyiqrkGVfUH9MAAAAADCgLorEZFmWVFImp6RMGj2p13aT6pJpPyZzqlVBe6u8116RaT8uc+qYTOdJWcNGZe7rdEaOkzViTHfwjPd/PDMAAAAAXIguimDZFyuWkDVyvDRyvJwe20zghyGzvVWmvVXekX3Sgd0KTrXKnGqR3ITsEZeGwbN8jOwRY6LPl8oqGy3LjRfkmgAAAABgsBAs+2DZjqyyCqms9xS8mSG27a1h+Ow4Lu9Yo0zniSiMHg+H55aNJngCAAAAuGARLPOQM8S2YmKv7cYYqbNNQcdxgicAAACACxbB8jyyLEsqHSGndMS5B89TrTIdJ6RYQvawS2SVVYQTDJVdKrusQtbwiuj9EiYXAgAAAIaAP238S/knjg74eZ3yS3X5vd/qc7+6ujo988wzeuONN7R161ZNmTJlwNowZBLJlhf2y+/q0vBSV8NLYiotcVUadzUs4aok4UTvrhx76Dw2pF/BM9ku03FCpuOkTOcJBc0H5L+xJwyfHSdkOttkJYaHAXP4aNkjRssuuzQMolEAtUpHyrLtAlwhAAAAgDT/xFFV3rZuwM/b9P01/dpv1qxZ+sQnPqGPf/zjA96GIRMsLx9fpmPHpK6kr6PHO5RqNupK+epK+UqmAnVGyzHHUiLuqDTuqjThaljCUWkiCqNxR6XRe0miO5S6TnGGLsuypMRwWYnh0mme2ylJJgikrjYFnSejAHpC3qH/lf7UJtN5UqbjuEyyQ1ZpeVTlHC1r+CWyh18ia9iorOWRstzEIF8hAAAAgMEyffr083buIRMsx4wcppGJPgKgMUp6gZJRyMwOni0nOqNtQc62zqQv25LiMUclcUeJWBhES+OOSqJwmog7Kk1E2+PhtkTcVUncKXiF1LJtqbRcTmm51OP5nWnG92Q626Iq50mZrjZ5h/dKXaei9SdlOk5KTkzWsJGyo8CZHUAzQXTYSFlObJCvEgAAAEAxGzLBsl8sS/GYo3jMUVl/jzFGXhAolQoywTPp+UqmfCW9QMdOdirpGaV8P9rHV1cqCq9eIMdSJpCmg2dJzFEinvU52h6P2SqJuUrEbSWifQajWmo5rqzho6Tho87yx2CkVGcYNLvaosDZJu/4oTCAdkUBtLNNipWoc0SFgsSIKIBWyB42MhxyW1ouu3SkrGEjpVhJWHUFAAAAcEG7sILln8Oy5DqOXMdR6bkea4w8PzuMBkr5YehMeYFOdqR07GSXUl6glG+U8sKwmoqqqkkvkKQwZMZsxd2sQBoFz3BbuBx37SighsvxdGB1bdl5Vk4ty5LipbLipZLGnOWSw/s+y2KeThw5GobNthb5LW/IJNtlkqdkOsMgKhPIKhkRDsMtjYJnuiI6LFxnl5bLKi2X4sMIoQAAAMAQRbDMh2XJdR25rqNh+vOGh/p+kAmbXhQ8059Tnq/jSV+eH8jzAnlROE35gVJe9nIgx46qtW5W6HS7K6PxKLhm9onZ0SvaP2v92Yb3pu/7jI0aJsceedZrM15SpisMm2HVs12mvVVea6PU1R4G0Wg4rgJfVukIWSXlmdBpDRspu6RcVklZFFBHZJblJgiiAAAAQJEgWBaY49gqdWyV5jNvTlQ5TXlGKT8KqH4gLx0+vUAdnb5O+El5vpHvhxVUzw+6j8sKqZZlKebaYeDsETrT7yNHlCjwfcXc7mCaPia9LhazFYuPUHzYKPWVAY2fkulql7rawgDadSqciOjEYSnZIRO9FIVRychKlIWv0ih4lpRH1dHuABq+ysIXj2UBAADARezBBx/Ujh07dPToUd1+++0aNWqUnn766QE5N//SvhBkKqc69+G8PRkjP0gH1Ch4+kae50fv4XDftvak2jtS8oJ0QA338fzontWsYOv7Ro5jKe6G4TP96g6r0ecowMbckYrFLgm3lThhQM06Jubailm+HL9LSrWHobOrXUp2KDhxSDq6XybVGYXR9mhbe1jlLAln2bXiw6PlMqmkTHY0+66VKJMSw6LQOixc58YHopcAAABwkXPKL+33o0HO9bz98cUvflFf/OIXB/znS/0Mlvv27VNtba1aW1s1atQo1dXVafLkyTn7+L6vBx98UM8995wsy9Ly5cu1aNEiSdLzzz+vr3/96/rDH/6gpUuXatWqVQN+IRggliXHseQ4tnSWPFVWllBbW1f/zpmuqKarpdGwXs/3M+s836gz6etUFFb9rIqqH5jcY7xwW2CMXCcKnY6tmDtcMXdEJnzGXVuxhK1YmaO4Y6nE8VSipBLyFFdS8SCpWHtSTtthuSYl20/K9rtkeZ1SqlNKRVVSy5YVHxYGzviwsPoZhdDu5XCb4sNkRfeqWvFhUiwhyyrOx9kAAABgcF1+77cK3YTzpl/Bcs2aNVqyZIlqamq0ZcsWrV69Wo899ljOPlu3btWBAwe0Y8cOtba26pZbbtF1112niRMnatKkSXrooYe0fft2JZPJ83IhKGJZFdWBZLKrpX5YJc0OpJlQmvLV0WXUEgTyfVue78rzHfl+IlNhTQ8JTh9nWVYUVqUSVxoe+BqW8jTM9VRy0lOpnVKJdURxNSpupRQzKbny5ATJzMv2uiTjS048MzGSHS/NhFArMUyKD88s5wTS6N2KlRBOAQAAUPT6/Kd+c3Oz9uzZo0cffVSSVF1drfXr16ulpUUVFRWZ/bZt26ZFixbJtm1VVFRo9uzZ2r59u+666y5dfvnlkqSdO3cSLDFgLNtSzHYUG+jHakbDgb3sqmnWfam+b3TCD9QShVo/SL8beQrkycg3Qbgc+LKSSTmnkrKDpGJBSiWOr1KnQyX2SZU6vhKWr4TtK255istTTJ5iSsk1SblBSrbxFNiujBOXcRKSG4XNKIg6iVKZUSOVDNxwVt9YiaxYiax4iRQrDZdjJeH6eInkxJn4CAAAAAOqz2DZ1NSkcePGyXEcSZLjOBo7dqyamppygmVTU5MmTJiQ+VxZWamDBw+ehyYD51lmOLCUkDOw5zZZVdYgvA/VCwK1RYE1lQ6rfvfQX983Mn5Slp+S5XfJSnqygqRsPyXbpGQHLXLNYbnylLADJexAcdtX3IrCquUpZjy58sIhvwoUWK4CJ6bAjsu4cclJSG48E0idWImcRImcRKmceBRUY4nwPtVYXHKzPydkueHxcmKEVgAAgIvQkJm8Z/jwuOKuKXQzkKWsLJ+pbDHQ/CiQJqOJl1KerzY/fX9q0P2e8mS8lIJUlyw/JeOnpFRS6vQkPxWF1uNS0CwnSMm1fMVto7gdhC/LVyx6ufKjwOrJNp4s48vYrowdC4cAu3FZbkx2LAygTjwhN1EiJ14iO14iOxaG2fR2y41Hy3FZbjx6T8h2Y+FnN3t9rOiHCI8ZM6LQTUAW+qP40CfFhz4pLvQHhpI+g2VlZaUOHTok3/flOI5839fhw4dVWVnZa7/GxkZde+21knpXMPN16lRSyc5+ThaD8+6cJu/BeVdWllBHR/cwc9eS3JijP/PxqjkCPwhn/40mUDoZTbrk5VRZ0zMDp2T8QPJTMr4ndSWldk8y4Wcr8GX5HbJ1UnErUMIxStiBYrZRzDaKW4Fcy5drBYpFy44C2fLlGF+28cMAG4QvYzthlTR6yY3enTB4yomF4TYr5Ibb4pnjuo9xu9dlbeu1Lr1snz3UjhkzQkeOnMy/AzAg6I/iQ58UH/qkuNAfxYegf3Z9BsvRo0erqqpKDQ0NqqmpUUNDg6qqqnKGwUrS/PnztXnzZs2dO1etra3auXOnHn/88fPWcACDw3ZsxR0pHhvAYcE9ZwqOlju89CRMQebxNl462KaDrOfLk5EnX0HSU+D7UaXVC4OqK5U4RiWOUdwxmfAatzsUc04pZoVB1rWMXCuQYwVyFEQBNgyxtglkGV8KPMn3Mu8m/dn3JMuKQqYry3ElOx1Mw/dkSYm8wOodSB03quZmBeLMebIDrJuzrtey7YbB13IYfgwAAAquX0Nh165dq9raWm3atEnl5eWqq6uTJC1btkwrVqzQ1KlTVVNTo927d2vu3LmSpHvuuUeTJk2SJO3atUt//dd/rba2Nhlj9PTTT+uhhx7SzJkzz9NlAShq52GmYBOk71sNcp656vmBOv1AJ7Oex+oHgbxU9gzCYWBNV2LTw4YdO/f5q+lnq8YdWyUxWwnXqNSREjGjuC3FnUBxW4rZRuXDXXldXXKtQK5l5CgKq74nk+qUutpkAl8K/PA92qYgiNZ5UhCGWxP43QE38MNqcDrkGiM5jmSnQ2vPkJsVSG03qta6PSq28ZxQnBty3axQnL3N7RGa3aIfmgwAAM4fyxgzJG5cfPkXv1Sys7PQzUCEobDFhf44D7Kqqjn3qKbfs4NqOpymq62eUWCMOlOePC8MrX5g5DqWYq6juOsong6qrq14zFEi5oTv8Wh7zjY7WhceF4s5ch0ramYYROVnhdB0KPW9THjNDaV+79Bqgqww65/5+EwVt0fADTwpGpos2z19IHVjkn26IcunqeC6WUOQ0/tkltOhOJ5bDT5L5ZYhZcWHPik+9ElxoT+KD0Nhz27ITN4DAIMqz6pqz7Df/dzV9DNTe1dIkylf7Z2pzHovE16zgqxnlPQDWZJiTm44zQTSKIwmYo4SsXj39lh3OI1nwqqtmOso39G0xpjcwJpVYe0OqFlDijNVWF/GO00F92zHZg9P9lNZwbbn8OHuIJoqKVEqsKN7bePdodaNR7Max7rvxU2H1p735Lrx7tmP0/vYAzxzNAAAQxTBEgAGQfdzVx2V5nuyzLNWo+G+0fDflOeHFdIowJ7sSOlYW1f4WBs/t9Iazh6cXU2NhvtmhdO4a4fV0qiimsgJptF73OkRbONyYgkN9l2fYbD1siq33QHU+J6GlbpqO3EqnFgq615Zk+yQ6TiRG2j93iHW+OGsyd1hNpVzr21O6My8J7qDrBvOeqxopuP0/ukwm7tfduDNCrYMNQYAFDGCJQAMNZlnrdoaiIf+pKupqaz7UlNZwdPzjTq7fLW1p6IZgv2sqmpYgU2lK6teINuyFI85UVBN35/qKOE6isdtJdxoyG+v6qmTU0kNj+1fNdXKTKYUToXc85DEqGHqKG0fgD+trD+3TJU2XT1Nh890eE31WO/JdJ2SaT/eXYn1U7nHB9GMyl4qXPZSkp8MA7Pj9qi4ZgfSKIimnysbvedsy35PP4fWjXcfw3NoAQB5IFgCwEUuu5qat6iammj1FKEAABdaSURBVA6pZ7o39fgpv9e9qUmvO9SmvHA/PzDh5ElOeG9pPD38NwqhCfdM96bm3pfqxmPyvECOY+c97DctDLPRhEmx3mF2IBljTlsx7V5O5QbbjpMyfotM4MnKCrcm57ik5EXrvGQYkrOrpU73s2S7w2hJdyiNlciKpauspwu10XJ6f4YNA8AFjWAJABg4WdXUkgE4nQlM1nDfHtVU35fvm173pmYeS9PjOavJlK/AmKxKqpOppqbDaCaopiuusdyKaywdWF07szwYRT7LsrqHzJ6nn2GCQApS3WEzE1hPsy7ZLtNxXMZPRcE1HViT4XYv2tdLynjJMMRadneV1E2oq6RUnuXmhtF4SRRe06E1CrLp7bGSKNQmst7jDBMGgCJAsAQAFC3LthS3nbyfo5qeTCkIup+Pmh1UwzCaNYlSl9cdUrNm+82edCnphcG252NpcsKqa+cO8c1MmBSFVsfJHTYcsxV3HBUiJ1m2LdlRYBvgc2eGDfvdFdKyYY5OHjsZrUtGQfRMobX7uMx7ZphwKpxtOFNdDcOrFSuJKqZZYTRWmgmtuetLouDaXY0Nq6wEVgDoL4IlAOCiYdtWOOGQBmhYZtZjafxMWM19FE3KN+ro9HXSj+5RjQJrdkjN3Kvq+/I9I8exsiZUsuWmq6TpsOrm3qMay67AZoXbmGPnBF7bLsw9lNnDhq1o+qr4qGFynPzve+0eJtx9T2pmeG9W9dT4SelUs0xmaHAyrLKm90sfk11ltZ2sKmvPQJoVXGNRlbVHUE1vz14msAK4UBEsAQD4c+X5WJrTSt+nGgXOdNU0rLRmPYrG99WZ9OQF4T7pEJsOqpkQmzVTsGVZUdgM2x3PXs6umkbVVTcrqGbudc2E2PBcbrTs2AN3/+q5yBkmPBCzWUUyMw17UShNh9Z0UM0E16SUPCaTuc81mTN0OL3fuQXWkpzg2h1YE7Lcksy9q9nL3McKoNAIlgAAFJOs+1QHVOYxNd3hM3cCJZMJpJ1JX6c6Ut37Z4XX9Gcvc09rGGiNiR5b49hyXVsxx4pCZ7gcy6q6xlxbI4Yn5Pt+5piYa2eqtG46xGZ9dh1bjm0NWnjNnmnY0rABO+/ZA2uP4Jpszw2sfVVYLSdroqW4LDf7XtWsEBorjYJp9v2rCXWcukT+qSAKuPHuyiszBgPoB4IlAAAXg0xglRIDNRQ4iwm6A2c6gPo5AbQ70HYlfaX8LnV0JcPAGpjoPcg8o9ULsoJs9B6GV0uu40TvdvSysgJt9O5aijlO9DlcdlxLcceWkxVkex7rROvcAZxBONt5D6xZQ4KzZ/3NGR7ccVymrceswl5KzZYvr6srE1SNFx1j/KxH3PScPKnn8N+STGjt3n7m2YMJrcCFg2AJAADylnlsTT//ZZGeUOlcpMOrn1NFjYJpVigNt4czAXd0edE2hZM3ZVVf/UCZ8Js+vjsUG9m2lQmZjp0bZLvfnbA6GwXYsOraHXwdx5Jr26f5HK7LDrLpe2tt69wrszmBNX5ux6aNGjVMra2973s1QRAF0KyKanZQzcwcnJSSp7LuY83at+d9sOnQ2vMxN9F797NWe4TRWEmPgJv7SJycZ7bGEpLtElyBQUKwBAAAQ8K5hte8mHRgjcKrOX2Q7a60hiG1s8vXqc4wzAZRmA2ytqfPGQTZ548qtNF+CsIJnBzbjqrM4bJrR0HU7g6ijp0bdJ0ewTd9jnS4tW1brqNwfXSedIAOLFvt7Sk5ji3Hlpx0yE3PGBw7DzMGZ0Lrae5LTc8K7EXDgDvbZE4dkwm8zPacx9z4Xo9Jm5KSMWHYju7D7Z41uPtZrZkAmxNos4cUx5X9qJz0uTLbbf45DUgESwAAgN6yhg4rNrg/2gRhkA383DDqB0aBCcNskLXsB4F8E4bYlOerK6kwzBrTO9waE54/E24DBUbyAyMjyfeC7p9pJEWV23SItS1FwdOKXrZsW5mAml6XHV6dHgHXcSw5liXb6d4/vZ9tJeTYJeHPcCTbteUMszLntnv83PTymYqSJvCjABpVSYPsIcBeptoqPyWT6pTpPBntE4XX4DTV155VWykMmE5Yee0eNhyX5cZ6h9L0UOHMPvHcAOuEx6ScSxScSmWdl2HDKG4ESwAAgCJi2ZZcOQpvhR28mV5PNzzZpANsFESDnp+j8OqbINqurPXd+yZTvoIo8AbGKDA99ws/mx6ffdO9zjdGgZ+9rbsqbFnKBE/bygqi0bJtS45lR+FVsu10EI7LsROZoGz3CKy2LTlxO3Nuy4qCrSXZUUXXtgK5CuQYX7Y8ucaLln3Z0Ton8GT5vuxUUlZ7h+zAk2U8WYEvy3hS4MsKsquwKTXKV5Ds6p68KQjC4c5uLBM2LScefu4RaHsNEc7sl4iOj+eG4dOsY5ZhnCuCJQAAAE7Lsi05iiq3xcoYGaPuwBtVZdOhNwyyUaXWKGtbVKUNTr89mfJljOQbLxwabYxkpMAoE3aNiY6Jfn73+nRwtmSMK9+44XmVblt0nqiybIL08ZJlKQysjiVb6UArubYU8wO5XqC45ctVoJgdKG4Fci1frhUopkCulZRrdcqVL1e+HCuQI1+OgujlZ4JvTgA2XvgeeLKNJ8lSYLsytqvAjsnYMRknJkXvxolJTjwKu/GsoBsGXDt6t9yYrFhcdixcZ7vRciwhOxaXE0t0B2aLZ7wOZQRLAAAADF3RZEdFH4D7I6rmGmNUOiyutpNd3YE1vS0rLCsTbLPDrJEnqSsKvpn10fn97PVRIFY6fCssjJogkGUCWcYLq6vyZQeB7FRYabWNL8tEwdR4stWWFVKDcFsUZm0TyI5CbvgehlvXCjLh142CclTfzXq58qN3z3Lky5UnV4HlyrNc+ZYr34opsMN3Y7sKos/GccMwnA7CdkxyXMmJR6E4rPxaTky2Y0cV6twh17aV+/nGMSMK+99HkSNYAgAAAMXACofqSpbiMUfx+FBPymfmSfKyqs3GBDK+L/m+TJCKHp/jhe+BLzfwFEt/zgwfjoYTB12yTHu4Lgq9VjJ8D0NwVI3NBOLuz7b8MOpaYT3Xs8Iw61uuvCgeewqXNfOrhf5jK2oESwAAAACDL6vaLNmSOzDRxEQvSfL73NlIxo8Cqhcth4E1FniKR/fCKvAGpG0XMoIlAAAAgIuTZUlWeC+pUaLQrRnSuEMWAAAAAJAXgiUAAAAAIC8ESwAAAABAXgiWAAAAAIC8ECwBAAAAAHkhWAIAAAAA8tKvYLlv3z4tXrxY8+bN0+LFi7V///5e+/i+r3Xr1mn27NmaM2eONm/e3K9tAAAAAIChrV/Bcs2aNVqyZImeeeYZLVmyRKtXr+61z9atW3XgwAHt2LFDTzzxhDZu3KjXX3+9z20AAAAAgKHN7WuH5uZm7dmzR48++qgkqbq6WuvXr1dLS4sqKioy+23btk2LFi2SbduqqKjQ7NmztX37dt11111n3dZfQcdx+e3tf8Yl4nzo9GLyu1KFbgYi9EfxoU+KC/1RfOiT4kOfFBf6A0NNn8GyqalJ48aNk+M4kiTHcTR27Fg1NTXlBMumpiZNmDAh87myslIHDx7sc1t//X/zbjyn/QEAAAAAg4PJewAAAAAAeekzWFZWVurQoUPyfV9SOBHP4cOHVVlZ2Wu/xsbGzOempiaNHz++z20AAAAAgKGtz2A5evRoVVVVqaGhQZLU0NCgqqqqnGGwkjR//nxt3rxZQRCopaVFO3fu1Lx58/rcBgAAAAAY2ixjjOlrp71796q2tlYnTpxQeXm56urqdMUVV2jZsmVasWKFpk6dKt/39cADD+iFF16QJC1btkyLFy+WpLNuAwAAAAAMbf0KlgAAAAAAnAmT9wAAAAAA8kKwBAAAAADkhWAJAAAAAMgLwRIAAAAAkBe30A14+OGHtXHjRm3dulVTpkzRb37zG61evVpdXV267LLL9JWvfEWjR4/udVxHR4c+//nP63e/+50cx9GqVav0wQ9+sABXcOHJ7pNYLKbVq1fryJEjcl1XU6dO1Zo1a1RSUtLruKVLl6qxsVFlZWWSpE984hP6yEc+MtjNvyD1/D256qqrNGXKFNl2+P+GNmzYoKuuuqrXcUePHtXnPvc5vfHGG0okElq/fr2mTZs22M2/4GT3R1tbm9atW5fZ1tzcrDFjxuipp57qdVxtba1++ctf6pJLLpEUPorpU5/61KC1+0J0ww03KB6PK5FISJJWrlypmTNn8l1SQKfrk4kTJ/JdUiBn+h3he6RwTtcnw4cP57ukQLq6uvTlL39Z//mf/6lEIqG3ve1tWr9+vfbt26fa2lq1trZq1KhRqqur0+TJk3sd7/u+HnzwQT333HOyLEvLly/XokWLBv9CioEpoN/+9rfmzjvvNB/84AfN//zP/xjf983s2bPNSy+9ZIwx5pFHHjG1tbWnPXbjxo3mb/7mb4wxxuzbt8+8973vNW1tbYPW9gtVzz557bXXzO9+9ztjjDG+75v77rvPPPzww6c99rbbbjPPPvvsYDb3otCzT4wxZsqUKf367722ttY88sgjxhhjXnrpJTNnzhwTBMF5be+F7nT9ke1Tn/qU+c53vnPaY1etWmX++Z//+Xw38aJyun7gu6SwTtcnfJcUzpn+ruJ7pHDO1CfZ+C4ZPOvXrzcPPfRQ5r/rI0eOGGOMWbp0qamvrzfGGFNfX2+WLl162uOfeuopc8cddxjf901zc7OZOXOmee211wan8UWmYENhk8mkHnjgAa1duzaz7re//a0SiYSmT58uSbr11lu1ffv20x7/4x//OPMszMmTJ+uaa67RL37xi/Pe7gvZ6fpk4sSJuvrqqyVJtm3r2muvVWNjY4FaePE5XZ+ci+3bt+vWW2+VJE2fPl3xeFyvvvrqALbw4tJXfzQ3N+uFF15QTU3N4DYMOfguKT58lwxdfI8MPr5LBs+pU6dUX1+v++67T5ZlSZIuvfRSNTc3a8+ePaqurpYkVVdXa8+ePWppael1jm3btmnRokWybVsVFRWaPXv2Gb9zLnQFC5bf/OY3dfPNN2vixImZdU1NTZowYULmc0VFhYIgUGtra6/jGxsbddlll2U+V1ZW6uDBg+e30Re40/VJts7OTj355JO64YYbzniODRs2aMGCBVq5cqUOHTp0vpp60ThbnyxdulQ1NTX62te+pmQy2Wv7sWPHZIxRRUVFZh2/J/np63ekvr5eM2bM0KWXXnrGczz66KNasGCBPv3pT2vv3r3nq6kXlZUrV2rBggVau3atTpw4wXdJEejZJ9n4Lhl8Z+oPvkcK52y/I3yXDJ7XXntNo0aN0sMPP6yFCxdq6dKl2rVrl5qamjRu3Dg5jiNJchxHY8eOVVNTU69z9PzOuZh/RwoSLF9++WX99re/1ZIlSwrx43EaffWJ53m6//779Z73vEezZs067T4bNmzQj3/8Y9XX1+uKK67QX/3VX53PJl/wztYnP/vZz/TDH/5Qjz/+uP7v//5PjzzySAFaeHHpz99bP/zhD896L9j999+v//iP/9DWrVs1d+5c3XXXXfJ9/3w096Lx+OOP60c/+pGefPJJGWP0wAMPFLpJF72z9QnfJYPvTP3B90jh9PX3Ft8lg8f3fb322mu6+uqr9cMf/lArV67Uvffeq/b29kI3bUgqSLB86aWXtHfvXs2aNUs33HCDDh48qDvvvFN/+tOfcobGtLS0yLZtjRo1qtc5JkyYoDfeeCPzuampSePHjx+U9l+IztQnzz//vHzf18qVKzVy5Eh98YtfPOM5KisrJYX/V+cTn/iEdu/erSAIBusSLjhn65P0n3VZWZkWLVqkX//6172OT9/Unz1sg9+TP9/Z+kOSfvOb3+j48eN6//vff8ZzjBs3LjNRxi233KL29vaL9v9qDpT070I8HteSJUv061//WpWVlXyXFNDp+kQS3yUFcqb+4HukcM7UJxLfJYOtsrJSrutmhrxOmzZNl1xyiUpKSnTo0KFMYPd9X4cPH870Xc9zZH/nXMy/IwUJlsuXL9fzzz+vZ599Vs8++6zGjx+v7373u7rrrrvU2dmpXbt2SZJ+8IMfaP78+ac9x/z58/XEE09Ikvbv369XX31VM2fOHLRruNCcqU/e+973qra2Vo7j6KGHHsqMP+/J8zwdPXo08/npp5/OmW0O5+5MfTJ16lR1dnZKCv/cn3nmGVVVVZ32HPPnz9cPfvADSdKuXbvU2dmpa665ZtCu4UJypv64/vrrJUlPPvmkbr75ZrnumSfbzh7S99xzz8m2bY0bN+68t/1C1d7erpMnT0qSjDHatm2bqqqqdM011/BdUiBn6pMgCPguKYAz9cfx48f5HimQM/VJGt8lg6uiokLvfve79cILL0iS9u3bp+bmZk2ePFlVVVVqaGiQJDU0NKiqqipnWHja/PnztXnzZgVBoJaWFu3cuVPz5s0b1OsoFgV/3Eg227a1YcMGrVmzJmeK+LSamhp9+9vf1rhx43TnnXeqtrZWc+bMkW3beuCBBzJTk2Pg/OIXv9CPfvQjTZkyRQsXLpQkveMd79CaNWt06NAhLV++XFu2bFEymdTy5cuVSqUkSWPHjtXXv/71Qjb9gvXHP/5Rq1evlmVZ8jxPb3/723XfffdJUk6fSNJnPvMZffazn1V9fb0SiYQ2bNjAP9DOg87OTm3btk3/9m//1mtb9t9bq1atUnNzsyzLUllZmf7hH/7hrP94wNk1Nzfr3nvvle/7CoJAV155pdasWcN3SQGdqU/4LimMM/UH3yOFc6Y+kfguKZR169bpC1/4gurq6uS6rjZs2KDy8nKtXbtWtbW12rRpk8rLy1VXV5c5ZtmyZVqxYoWmTp2qmpoa7d69W3PnzpUk3XPPPZo0aVKhLqegLGOMKXQjAAAAAABDF//LCQAAAACQF4IlAAAAACAvBEsAAAAAQF4IlgAAAACAvBAsAQAAAAB5IVgCAAAAAPJCsAQADHlLly7VO9/5TiWTyUI3BQCAixLBEgAwpL3++uvatWuXLMvST37yk0I3BwCAixLBEgAwpNXX12vatGn68Ic/rPr6+sz6Y8eO6e6779Y73vEOfeQjH9E3vvENfexjH8ts37t3r26//Xa9613v0rx587Rt27ZCNB8AgAuCW+gGAACQjy1btuiTn/ykpk2bpsWLF+vo0aO69NJL9cADD6i0tFQvvPCC3njjDd15552aMGGCJKm9vV133HGHVqxYoX/8x3/UH/7wB91+++2aMmWK3vzmNxf4igAAGHqoWAIAhqxdu3apsbFRN954o6655hpNmjRJDQ0N8n1fO3bs0L333qvS0lK9+c1v1i233JI57mc/+5kuu+wyfeQjH5Hrurr66qs1b948bd++vYBXAwDA0EXFEgAwZNXX12vGjBmqqKiQJFVXV+upp57STTfdJM/zVFlZmdk3e/mNN97QK6+8ounTp2fW+b6vm2++efAaDwDABYRgCQAYkjo7O/XjH/9YQRBoxowZkqRkMqkTJ06oublZruvq4MGDetOb3iRJampqyhxbWVmpd77znXr00UcL0nYAAC40DIUFAAxJO3fulOM4evrpp1VfX6/6+npt27ZN06dPV319vebMmaOHH35YHR0d2rt3r7Zs2ZI59gMf+ID279+v+vp6pVIppVIpvfLKK9q7d28BrwgAgKGLYAkAGJKeeuopLVy4UBMmTNCYMWMyr49//OPaunWrVq9erZMnT2rGjBn63Oc+p5tuuknxeFySVFZWpu9+97vatm2bZs6cqeuvv15f/epXeQ4mAAB/JssYYwrdCAAAzrevfOUrOnr0qOrq6grdFAAALjhULAEAF6S9e/fq97//vYwxeuWVV/Tv//7vmjNnTqGbBQDABYnJewAAF6RTp07pM5/5jA4fPqzRo0frjjvu0KxZswrdLAAALkgMhQUAAAAA5IWhsAAAAACAvBAsAQAAAAB5IVgCAAAAAPJCsAQAAAAA5IVgCQAAAADIC8ESAAAAAJCX/x+JTRM0MLh2MAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "bAIfF6Ml42hg" }, "source": [ "এখন দেখি ডাটা কি কথা বলে? Cabin এবং Embarked এখনো কিছু ফাঁকা! উপরের ছবি দেখে আমরা বেশ কিছু ধারণা পেয়েছি - বয়স ০, থেকে ২০, ৩০ না করে দেখা গেল তাদের ভ্যারিয়েশন কিছুটা ভিন্ন। সেজন্য আমরা নিয়ে এসেছি আলাদা আলাদা বাক্স পদ্ধতি। অনেকে এটাকে বলেন 'বিনিং' মানে ভিন্ন ভিন্ন 'বিন' মানে ব্যাগ বা বাক্সে ফেলা। আমাদের ক্লাসিফায়ার এটাকে অনেক পছন্দ করবে। " ] }, { "cell_type": "code", "metadata": { "id": "ky3ZnY1q42hh", "outputId": "651f08ad-5086-4c14-de49-0d2cfc9bb497", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "train.info()" ], "execution_count": 36, "outputs": [ { "output_type": "stream", "text": [ "\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 891 non-null int64 \n", " 1 Survived 891 non-null int64 \n", " 2 Pclass 891 non-null int64 \n", " 3 Sex 891 non-null int64 \n", " 4 Age 891 non-null float64\n", " 5 SibSp 891 non-null int64 \n", " 6 Parch 891 non-null int64 \n", " 7 Ticket 891 non-null object \n", " 8 Fare 891 non-null float64\n", " 9 Cabin 204 non-null object \n", " 10 Embarked 889 non-null object \n", " 11 Title 891 non-null int64 \n", "dtypes: float64(2), int64(7), object(3)\n", "memory usage: 83.7+ KB\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "qBiUfsEe42hh", "outputId": "286bab3c-640d-4eb9-b602-698be9a7bd16", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "test.info()" ], "execution_count": 37, "outputs": [ { "output_type": "stream", "text": [ "\n", "RangeIndex: 418 entries, 0 to 417\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 418 non-null int64 \n", " 1 Pclass 418 non-null int64 \n", " 2 Sex 418 non-null int64 \n", " 3 Age 418 non-null float64\n", " 4 SibSp 418 non-null int64 \n", " 5 Parch 418 non-null int64 \n", " 6 Ticket 418 non-null object \n", " 7 Fare 417 non-null float64\n", " 8 Cabin 91 non-null object \n", " 9 Embarked 418 non-null object \n", " 10 Title 418 non-null int64 \n", "dtypes: float64(2), int64(6), object(3)\n", "memory usage: 36.0+ KB\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "lDWiziju42hh" }, "source": [ "#### ৫.৪.২ বয়সকে ভিন্ন ভিন্ন ব্যাগে ফেলা \n", "বয়সের মতো কন্টিনিউয়াস ভ্যারিয়েবলকে পাল্টে ফেলছি ক্যাটেগরিক্যাল ভ্যারিয়েবলে। বয়সটা দেখুন ভালো করে। ১৬ এবং ১৬ এর নিচে, ২৬ এবং ৩৬ এর নিচে, ৩৬, .... ৬২ এবং তার ওপরে। \n", "\n", "এখানে আমাদের ফিচার ভেক্টর ম্যাপ হতে পারে এধরনের ৫টা ভাগে: \n", "child: 0 \n", "young: 1 \n", "adult: 2 \n", "mid-age: 3 \n", "senior: 4" ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "BLKDyKXX42hi" }, "source": [ "for dataset in train_test_data:\n", "\n", " dataset.loc[dataset['Age'] <= 16, 'Age'] = 0\n", " dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 26), 'Age'] = 1\n", " dataset.loc[(dataset['Age'] > 26) & (dataset['Age'] <= 36), 'Age'] = 2\n", " dataset.loc[(dataset['Age'] > 36) & (dataset['Age'] <= 62), 'Age'] = 3\n", " dataset.loc[ dataset['Age'] > 62, 'Age'] = 4" ], "execution_count": 38, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "4XtXKAxs42hl" }, "source": [ "বয়স কিন্তু চলে এসেছে ০ থেকে ৪ নিউম্যারিক ভ্যালুর মধ্যে। ক্যাটেগরিক্যাল ভাল্যু। " ] }, { "cell_type": "code", "metadata": { "id": "-DvlRCd142hl", "outputId": "3f61d57f-dae2-4652-c449-568dadcd75cf", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "train.head()" ], "execution_count": 39, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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" ], "text/plain": [ " PassengerId Survived Pclass Sex ... Fare Cabin Embarked Title\n", "0 1 0 3 0 ... 7.2500 NaN S 0\n", "1 2 1 1 1 ... 71.2833 C85 C 2\n", "2 3 1 3 1 ... 7.9250 NaN S 1\n", "3 4 1 1 1 ... 53.1000 C123 S 2\n", "4 5 0 3 0 ... 8.0500 NaN S 0\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 39 } ] }, { "cell_type": "code", "metadata": { "id": "-wghNZEd42hl", "outputId": "c4065465-451f-4d04-a182-c3d381caf5c5", "colab": { "base_uri": "https://localhost:8080/", "height": 361 } }, "source": [ "bar_chart('Age')" ], "execution_count": 40, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "H5G3S5CK42hl" }, "source": [ "### ৫.৫ এমবার্কড, কে কোন ঘাট থেকে উঠেছে" ] }, { "cell_type": "markdown", "metadata": { "id": "O1QdURvg42hm" }, "source": [ "#### ৫.৫.১ চলুন ভর্তি করি মিসিং ভ্যালুগুলো \n", "তার আগে দেখে নেই সবচেয়ে বেশি মানুষ উঠেছে কোথা থেকে? ১ম, ২য় এবং ৩য় শ্রেণী ধরে। " ] }, { "cell_type": "code", "metadata": { "id": "g2f5EsO342hm", "outputId": "fe54ef2f-2553-4a52-ad61-2b93f7cb27e6", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "Pclass1 = train[train['Pclass']==1]['Embarked'].value_counts()\n", "Pclass2 = train[train['Pclass']==2]['Embarked'].value_counts()\n", "Pclass3 = train[train['Pclass']==3]['Embarked'].value_counts()\n", "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n", "df.index = ['1st class','2nd class', '3rd class']\n", "df.plot(kind='bar',stacked=True, figsize=(10,5))" ], "execution_count": 41, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 41 }, { "output_type": "display_data", "data": { "image/png": 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Jknbu3KlJkyZp3LhxmjFjhmpra0Pf19oYAACAHbRZtBwOh2bOnKmysjKVlJQoIyNDK1asUCAQ0EMPPaS8vDyVlZUpKytLK1askKRWxwAAAOyizaKVnJysESNGhF4PHTpU1dXVKi8vV1xcnLKysiRJU6dO1TvvvCNJrY4BAADYRYcuhg8EAnrppZc0ZswY1dTUqG/fvqGxlJQUBQIBNTQ0tDqWnJzc7s9LTe3ZkXiwsWjclgLWYb4Akaur/Xx2qGgtXbpU8fHxuuOOO/THP/7RVKaQ2tomBQJB458TTl1tgkSLI0carY6AKJGWlsB8Qbvw+9wa0fjz6XQ6zntyqN1Fq6ioSP/617+0Zs0aOZ1OeTweVVdXh8br6urkdDqVnJzc6hgAAIBdtGt7hyeeeELl5eVatWqV3O4zu55fffXVam5u1o4dOyRJL7/8ssaPH9/mGAAAgF20eUbrs88+09NPP63LL79cU6dOlST169dPq1at0vLly5Wfn6+Wlhalp6eruLhYkuR0Os87BgAAYBdtFq0rr7xSn3766TnHhg0bppKSkg6PAQAA2AE7wwMAABhC0QIAADCEh0oD6LCE5Dh1i3VbHeOCReNt+82nvWps4OHpQLShaAHosG6xbt2+/h6rY9jKhpyn1CiKFhBtKFoAOszr82pDzlNWx7AVr89rdQQAF4CiBaDD3C63KgqnWB3DVvov2ihxRguIOlwMDwAAYAhFCwAAwBCKFgAAgCEULQAAAEMoWgAAAIZQtAAAAAyhaAEAABjCPloAOixw2vu/+zqhswROs2EpEI0oWgA6zBnr1sS5b1gdw1ZKHp8sNiwFog9LhwAAAIZQtAAAAAyhaAEAABhC0QIAADCEogUAAGAIRQsAAMAQihYAAIAhFC0AAABDKFoAAACGULQAAAAMoWgBAAAYQtECAAAwhKIFAABgCEULAADAEIoWAACAIRQtAAAAQyhaAAAAhrisDtDVBE571X/RRqtj2ErgtNfqCAAAnBNFK8ycsW5NnPuG1TFspeTxyZJarI4BAMDXsHQIAABgCEULAADAEIoWAACAIRQtAAAAQyhaAAAAhlC0AAAADKFoAQAAGELRAgAAMISiBQAAYEibRauoqEhjxozRoEGDtHfv3tDx/fv3KycnR+PGjVNOTo4qKyvbNQYAAGAXbRatsWPH6oUXXlB6evpZx/Pz85Wbm6uysjLl5uYqLy+vXWMAAAB20WbRysrKksfjOetYbW2tdu3apQkTJkiSJkyYoF27dqmurq7VMQAAADu5oIdK19TUqE+fPoqJiZEkxcTE6JJLLlFNTY2CweB5x1JSUsKXHAAAIMJdUNHqLKmpPa2OgCiRlpZgdQTAOOY57KCrzfMLKloej0eHDh2S3+9XTEyM/H6/Dh8+LI/Ho2AweN6xjqqtbVIgELyQiJbpahMkWhw50mh1BFthnluDed65mOfWiMZ57nQ6znty6IK2d0hNTVVmZqZKS0slSaWlpcrMzFRKSkqrYwAAAHbS5hmtgoIC/eEPf9DRo0d15513Kjk5WW+99ZaWLFmiBQsWaPXq1UpMTFRRUVHoe1obAwAAsIs2i9bixYu1ePHirx0fMGCAXnnllXN+T2tjAAAAdsHO8AAAAIZQtAAAAAyhaAEAABhC0QIAADCEogUAAGAIRQsAAMAQihYAAIAhFC0AAABDKFoAAACGULQAAAAMoWgBAAAYQtECAAAwhKIFAABgCEULAADAEIoWAACAIRQtAAAAQyhaAAAAhlC0AAAADKFoAQAAGELRAgAAMISiBQAAYAhFCwAAwBCKFgAAgCEULQAAAEMoWgAAAIZQtAAAAAyhaAEAABhC0QIAADCEogUAAGAIRQsAAMAQihYAAIAhFC0AAABDKFoAAACGULQAAAAMoWgBAAAYQtECAAAwhKIFAABgCEULAADAEIoWAACAIRQtAAAAQyhaAAAAhlC0AAAADKFoAQAAGELRAgAAMMRo0dq/f79ycnI0btw45eTkqLKy0uTHAQAARBSjRSs/P1+5ubkqKytTbm6u8vLyTH4cAABARHGZeuPa2lrt2rVLzz77rCRpwoQJWrp0qerq6pSSktKu93A6HabiGXVJr+5WR7CdaJ0r0Yx53vmY552Ped75onGet5bZEQwGgyY+tLy8XPPnz9dbb70VOnbzzTeruLhYV111lYmPBAAAiChcDA8AAGCIsaLl8Xh06NAh+f1+SZLf79fhw4fl8XhMfSQAAEBEMVa0UlNTlZmZqdLSUklSaWmpMjMz2319FgAAQLQzdo2WJH3++edasGCBjh8/rsTERBUVFal///6mPg4AACCiGC1aAAAAdsbF8AAAAIZQtAAAAAyhaAEAABhC0QIAADCEogUAAGAIRQuAbdTW1mrnzp1WxwBgIxQtG9u/f79aWlokSR9++KF+85vf6NixYxanAsIrNzdXjY2NOn78uLKzs7Vo0SIVFRVZHQsIq02bNqmpqUmStHLlSt11110qLy+3OBUkipat/eQnP5HT6VRVVZXy8/NVVVWl+fPnWx0LCKuTJ08qISFB7733niZOnKiSkhJt2bLF6lhAWD311FPq2bOn/v73v2vLli3Kzs5WQUGB1bEgipatOZ1OxcbG6oMPPtC0adO0dOlS1dTUWB0LCCuv1ytJ2rZtm0aPHi2n06mYmBiLUwHh5XK5JElbt27VbbfdpokTJ4ZWLGAtipaNtbS06OjRo3rvvfc0cuRISRIPCkBXM3z4cN18883629/+puHDh+v48eNyOvnVh67F4XBo06ZN2rRpk0aNGiVJOn36tMWpIEkuqwPAOtOnT9f48eM1atQoDRkyRFVVVUpISLA6FhBW+fn52rNnjzIyMhQbG6umpiaWVNDlLF68WM8884x+8IMfKCMjQ5WVlRoxYoTVsSCedYh/EwgE5PP55Ha7rY4ChM3+/fvVt29fxcXF6cMPP9Tu3buVk5OjpKQkq6MBsAHOn9vYv9+l8otf/EKzZs3SZ599ZnEqILy46QN28Nhjj6mxsVE+n0+5ubkaOnSo3njjDatjQRQtW/v3u1S2bt2q7OxsLV261OpYQFhx0wfs4C9/+YsSEhK0ZcsW9enTR2VlZVq3bp3VsSCKlq1xlwrsgJs+YCfbt2/XjTfeqD59+sjhcFgdB6Jo2Rp3qcAOvrrpIz4+nps+0GWlpqYqPz9fb7/9tkaPHi2fzye/3291LIiL4W3tk08+0W9/+1uNGDFC06dPV2VlpZ5//nk98sgjVkcDjOGmD3RFdXV1evPNNzV06FANHTpUBw4c0EcffaRbb73V6mi2R9EC0OVVVFRoz549oc1LJSk7O9vCRADsgn20bMzn82njxo3avXv3WddmLVu2zMJUQHg999xzWr9+vY4cOaIhQ4Zox44d+va3v03RQpdSU1Oj4uJi7dmz56zf53/6058sTAWJa7RsLS8vTx9//LHef/99XX755SovL1e3bt2sjgWE1YYNG/TKK6/I4/Fo7dq1euWVV9SjRw+rYwFhtXDhQo0aNUrBYFArVqzQtddeq1tuucXqWBBFy9b+8Y9/qKioSAkJCZo9e7ZefPFF7du3z+pYQFi53W7Fx8crEAgoGAxq4MCBqqystDoWEFb19fW67bbb5HK5dM011+ixxx7TBx98YHUsiKVDW4uLi5MkxcTE6NSpU0pISFBtba3FqYDw6t69u06fPq3BgweruLhYHo9HgUDA6lhAWMXGxkqS4uPjVV1drd69e6uurs7iVJAoWraWlJSkY8eO6brrrtOsWbPUq1cv9enTx+pYQFjl5+fr9OnTWrBggZ544gkdOHBAy5cvtzoWEFZZWVlqaGjQtGnTdOutt8rtdmv8+PFWx4K469DW/H6/YmJiFAgE9Oabb6qpqUnZ2dnq2bOn1dEAABeourpaTU1NGjhwoNVRIIoWgC6qrbNWDz/8cCclAcw5depUq+Pdu3fvpCQ4H5YObWjKlCmtPprh1Vdf7cQ0gBnx8fFWRwCMu+aaa+RwOM56rNRXrx0Oh3bv3m1hOkic0bKljz76qNXx4cOHd1ISAAC6NooWgC6toKBAc+bMUXJysqQzt8GvXr1aixYtsjgZED579+5Vv379QmdyT548qS+//FJXXnmlxcnAPlo2Nm3aNB07diz0uqGhQT/84Q8tTASE344dO0IlS5J69eql7du3W5gICL8FCxaEtniQJJfLpfnz51uYCF+haNnYyZMnlZSUFHqdnJysEydOWJgICD+/3/+1Yz6fz4IkgDl+v/+souV2u88599H5KFo2FggEzrpj5cSJE/wFhC5nyJAhKigo0KFDh3Tw4EEVFBRoyJAhVscCwsrlcqmqqir0+osvvlBMTIyFifAV7jq0sQkTJujOO+/UtGnTJEkvvfSSJk2aZHEqILwWLlyowsJCZWdny+Fw6IYbbtDChQutjgWE1Zw5czRt2jRdf/31kqQPPvhABQUFFqeCxMXwtvfaa6/p/ffflyR973vfU3Z2trWBAAAXpLKyUlu3bpUkfec739Fll11mcSJIFC0AAABjuEYLAADAEIoWAACAIRQtAAAAQ7jr0MYeeOABrVy5ss1jQDQaOXJkq8/0/Otf/9qJaQAzXnjhhVbH2YTaehQtG/viiy++dqyiosKCJED4bdy4UdKZh6Q3NDQoJydHwWBQr7766lkb9QLRrLy8XNKZR0t99NFHGjVqlKQz/5AYMWIERSsCcNehDW3YsEHr169XRUWFBgwYEDre2Niob3zjG1qzZo2F6YDwuvXWW/X73//+rGNTpkwJFTGgK7j77rv1yCOPKCMjQ5JUVVWlwsJCfp9HAM5o2dDo0aN12WWXaenSpXr44YdDx3v27KlBgwZZmAwIv6amJtXV1SklJUWSVFdXp6amJotTAeFVXV0dKlmSlJGRoQMHDliYCF+haNlQenq60tPTVVpaGjrm9Xp17NgxHtmALmf69OnKzs7WDTfcIOnMjtmzZ8+2NhQQZr1799aqVat02223STq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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "lHzjc0iq42hm" }, "source": [ "১ম, ২য় এবং ৩য় শ্রেণী ধরে প্রতিটা শ্রেণীতেই সবচেয়ে বেশি মানুষ এসেছে সাউথহ্যাম্পটন থেকে। ৫০% এর বেশি। এখানে কোন শহরের কতো মানুষ ১ম শ্রেণী কিনেছে - সেটা থেকে বোঝা যাবে কোন শহরের মানুষ গরীব। \n", "\n", "কি করবো আমরা? সাউথহ্যাম্পটন মানে 'S' দিয়ে ভর্তি করে দেবো। " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "ZA90D1Rr42hm" }, "source": [ "for dataset in train_test_data:\n", " dataset['Embarked'] = dataset['Embarked'].fillna('S')" ], "execution_count": 42, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "AdXo8L4B42hm", "outputId": "202049d3-6a40-47f8-eb8e-46423b17fba6", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "train.head()" ], "execution_count": 43, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitle
010301.010A/5 211717.2500NaNS0
121113.010PC 1759971.2833C85C2
231311.000STON/O2. 31012827.9250NaNS1
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" ], "text/plain": [ " PassengerId Survived Pclass Sex ... Fare Cabin Embarked Title\n", "0 1 0 3 0 ... 7.2500 NaN S 0\n", "1 2 1 1 1 ... 71.2833 C85 C 2\n", "2 3 1 3 1 ... 7.9250 NaN S 1\n", "3 4 1 1 1 ... 53.1000 C123 S 2\n", "4 5 0 3 0 ... 8.0500 NaN S 0\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 43 } ] }, { "cell_type": "markdown", "metadata": { "id": "h67edM1142hm" }, "source": [ "প্রতিটা শহরকে একটা সংখ্যা দিয়ে পাল্টে দেই আমাদের হিসেবের সুবিধার কথা চিন্তা করে। \"S\": 0, \"C\": 1, \"Q\": 2" ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "7UNzucbq42hn" }, "source": [ "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n", "for dataset in train_test_data:\n", " dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)" ], "execution_count": 44, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "IcD1vsD042hn" }, "source": [ "### ৫.৬ ভাড়া\n", "\n", "মিসিং অংশগুলো ভর্তি করে দেই প্রতিটা শ্রেনীর গড় ভাড়ার ভ্যালু দিয়ে। " ] }, { "cell_type": "code", "metadata": { "id": "WqcVqfKL42hn", "outputId": "78f82c57-eb6c-458a-afa1-a7cb2be5b784", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "# fill missing Fare with median fare for each Pclass\n", "train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n", "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n", "train.head(5)" ], "execution_count": 45, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitle
010301.010A/5 211717.2500NaN00
121113.010PC 1759971.2833C8512
231311.000STON/O2. 31012827.9250NaN01
341112.01011380353.1000C12302
450302.0003734508.0500NaN00
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" ], "text/plain": [ " PassengerId Survived Pclass Sex ... Fare Cabin Embarked Title\n", "0 1 0 3 0 ... 7.2500 NaN 0 0\n", "1 2 1 1 1 ... 71.2833 C85 1 2\n", "2 3 1 3 1 ... 7.9250 NaN 0 1\n", "3 4 1 1 1 ... 53.1000 C123 0 2\n", "4 5 0 3 0 ... 8.0500 NaN 0 0\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 45 } ] }, { "cell_type": "markdown", "metadata": { "id": "H8qiMtJG42hn" }, "source": [ "চলুন কিছু প্লট দেখে আসি - কোন ধরনের ভাড়ার মানুষ বেশি মারা গিয়েছেন। দেখা যাচ্ছে সস্তা টিকেটধারী মানুষদের ভাগ্য ওরকম সুপ্রসন্ন ছিলো না। এর পাশাপাশি \"আর\" এনভায়রনমেন্ট দেখবেন প্রতিবার। না দেখলে অনেককিছু না বোঝা থেকে যাবে। নিচের প্লট থেকে দেখা যাচ্ছে ০ থেকে ১০০ ঘরের মধ্যে বেশিরভাগ মানুষ মারা গেছেন। মানে যারা টিকেট কেঁটেছিলেন ০ থেকে ১০০ ডলারের মধ্যে - তাদের ভাগ্য খারাপ। " ] }, { "cell_type": "code", "metadata": { "id": "AkE8NpEn42hn", "outputId": "8d998514-9231-44be-b093-bb0643cf1e8d", "colab": { "base_uri": "https://localhost:8080/", "height": 205 } }, "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Fare',shade= True)\n", "facet.set(xlim=(0, train['Fare'].max()))\n", "facet.add_legend()\n", " \n", "plt.show() " ], "execution_count": 46, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "mwlQOXtk42hn", "outputId": "74615bfc-80b7-4194-a685-8e6e7b35267f", "colab": { "base_uri": "https://localhost:8080/", "height": 222 } }, "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Fare',shade= True)\n", "facet.set(xlim=(0, train['Fare'].max()))\n", "facet.add_legend()\n", "plt.xlim(0, 20)" ], "execution_count": 47, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(0.0, 20.0)" ] }, "metadata": { "tags": [] }, "execution_count": 47 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "hZ5eLN-442hn" }, "source": [ "এই ছবিতে (০-২০) ব্যাপারটা আরো পরিষ্কারভাবে ধরা পড়েছে। বোঝা যাচ্ছে ৮ ডলার আর তার আশেপাশের টাকা দিয়ে কেনা টিকেটের মালিকদের ভাগ্য সুপ্রসন্ন ছিলো না। " ] }, { "cell_type": "code", "metadata": { "id": "XzwVHm5J42ho", "outputId": "1567926b-86ee-4387-ab73-38fcf395b161", "colab": { "base_uri": "https://localhost:8080/", "height": 222 } }, "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Fare',shade= True)\n", "facet.set(xlim=(0, train['Fare'].max()))\n", "facet.add_legend()\n", "plt.xlim(0, 30)" ], "execution_count": 48, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(0.0, 30.0)" ] }, "metadata": { "tags": [] }, "execution_count": 48 }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "ZSSNF4l942ho" }, "source": [ "এখানেও ভাগ করি টিকেটের দাম দিয়ে বিভিন্ন 'বিন' মানে ব্যাগে। সেটাকে ম্যাপিং করি সংখ্যায়। আগের মতো। " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "zWYDBFrM42ho" }, "source": [ "for dataset in train_test_data:\n", " dataset.loc[ dataset['Fare'] <= 17, 'Fare'] = 0\n", " dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1\n", " dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2\n", " dataset.loc[ dataset['Fare'] > 100, 'Fare'] = 3" ], "execution_count": 49, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "nB_ZQTpz42ho", "outputId": "0c8b056d-45bb-434b-e91c-d53ec770f9da", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "train.head()" ], "execution_count": 50, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitle
010301.010A/5 211710.0NaN00
121113.010PC 175992.0C8512
231311.000STON/O2. 31012820.0NaN01
341112.0101138032.0C12302
450302.0003734500.0NaN00
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass Sex ... Fare Cabin Embarked Title\n", "0 1 0 3 0 ... 0.0 NaN 0 0\n", "1 2 1 1 1 ... 2.0 C85 1 2\n", "2 3 1 3 1 ... 0.0 NaN 0 1\n", "3 4 1 1 1 ... 2.0 C123 0 2\n", "4 5 0 3 0 ... 0.0 NaN 0 0\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 50 } ] }, { "cell_type": "markdown", "metadata": { "id": "iNTUSal142ho" }, "source": [ "### ৫.৭ কেবিন\n", "\n", "আমার এখানে কেবিন নম্বরটার দরকার নেই। দরকার হবে শুধু প্রথম অক্ষরটা। যেমন C23 এর C, কোন জায়গায় কেবিনটা। " ] }, { "cell_type": "code", "metadata": { "id": "Qd-dO5t242ho", "outputId": "33d04228-8906-4ccd-a7c0-5e472198586f", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "train.Cabin.value_counts()" ], "execution_count": 51, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "C23 C25 C27 4\n", "B96 B98 4\n", "G6 4\n", "E101 3\n", "F33 3\n", " ..\n", "A36 1\n", "B41 1\n", "D30 1\n", "C99 1\n", "D46 1\n", "Name: Cabin, Length: 147, dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 51 } ] }, { "cell_type": "markdown", "metadata": { "id": "aG6DukEI42hp" }, "source": [ "শুধুমাত্র দরকার প্রথম অক্ষর। str[:1], কারণ এখানে পাওয়া যাবে কোন ক্লাসের কেবিন সেটা। তাহলে একটা ধারণা পাওয়া যাবে জাহাজের কোন এলাকায় ছিলেন একজন যাত্রী। " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "8guLOPEH42hp" }, "source": [ "for dataset in train_test_data:\n", " dataset['Cabin'] = dataset['Cabin'].str[:1]" ], "execution_count": 52, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "iKk8qBOy42hp", "outputId": "32c77ebf-610c-4e55-ad55-1e3c369ba63d", "colab": { "base_uri": "https://localhost:8080/", "height": 381 } }, "source": [ "Pclass1 = train[train['Pclass']==1]['Cabin'].value_counts()\n", "Pclass2 = train[train['Pclass']==2]['Cabin'].value_counts()\n", "Pclass3 = train[train['Pclass']==3]['Cabin'].value_counts()\n", "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n", "df.index = ['1st class','2nd class', '3rd class']\n", "df.plot(kind='bar',stacked=True, figsize=(10,5))" ], "execution_count": 53, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 53 }, { "output_type": "display_data", "data": { "image/png": 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9rqCgSL16XW368SWmDgEAACzDFS0AAOBoeXmzw1OHU6dO05AhN5o2NkULAAA4GlOHAAAAUYiiBQAAYBGKFgAAgEW4RwsAANjK7/M3eymGlo57KWvXlph+3G+iaAEAAFtdalHRaMbUIQAAgEUoWgAAABahaAEAAFiEe7RM5vP7tGb8c3bHcBSf32d3BAAAzouiZTLDY2hf4Vi7YzhKr3nrJDXYHQMAgHNQtAAAgK06d24nw4g3fVyfr1HV1fWmj9sSFC0AAGArw4jX0qVLTR93+vTpki5dtPx+v/7t31bq7bdLFRfnUVxcnDIyMvTgg/9PV13VK6IMFC0AAOBoTz75S9XX1+v55/9NSUlJCoVC+vOfP9DBg19GXLQu+dRhUVGRsrOz1adPH3322Wfh7fv379f48eP1gx/8QOPHj9eBAweatQ8AAKCtKCs7qPfff1dz5jyhpKQkSZLL5dL3vneThg37fsTjX7JoDR8+XKtWrVJ6enqT7fn5+crNzVVpaalyc3M1f/78Zu0DAABoKz777FN17/5tderUyZLxL1m0Bg0aJK/X22RbRUWFdu/erZEjR0qSRo4cqd27d+vkyZMX3QcAANCW7d+/Tz/+ca4mTLhTTz9dHPF4l3WPVnl5ubp166a4uDhJUlxcnK644gqVl5crFApdcF+XLl1adJzU1I6XEw8OlJaWZHcERBHOFzhBWz3Pjx1zy+NpvfXSL3WsrKwsHTp0UGfOnFZSUpKuueZq/fu/r9brr6/Wnj17zvm+2+1u0e+2Td8MX1FRq2AwZHeMFmmrJ3asO368xu4IiBJpaUmcL4h5bfk8DwaD8vuDrXa8Sx3ryiu766abhqmwcIHmzHlCHTuevchz+nSdQqHQOd8PBoPn/G7dbtcFLw5dVtHyer06evSoAoGA4uLiFAgEdOzYMXm9XoVCoQvuAwAA+N98vsa/L8Vg/rjNMW/eL/TSS7/T5Mn3yePxKCkpSV27pumee34ccYbLKlqpqanKysrSxo0bNWbMGG3cuFFZWVnhqcGL7QMAAPims4uK2rewaHx8vB56aKoeemiq6WNfsmgVFBRoy5YtOnHihCZNmqTk5GRt2rRJv/jFLzRnzhw9++yz6tSpk4qKisLfudg+AAAAp3CFQqE2exNUtN6jxbsOW1eveeva7L0IaHva8r0rgFna8nn+1Vdf6lvf6mF3jMt2vvwXu0er9W77BwAAkNSGr/Fc1OXkpmgBAIBW4/EYOn36VNSVrVAopNOnT8njMVr0vTa9vAMAAIgtKSlpqqw8rtraKrujtJjHYyglJa1l37EoCwAAwDni4jzq2tU5Sz4xdQgAAGARihYAAIBFKFoAAAAWoWgBAABYhKIFAABgEYoWAACARShaAAAAFqFoAQAAWISiBQAAYBGKFgAAgEUoWgAAABahaAEAAFiEogUAAGARihYAAIBFKFoAAAAWoWgBAABYhKIFAABgEYoWAACARShaAAAAFqFoAQAAWISiBQAAYBGKFgAAgEUoWgAAABahaAEAAFjEE8mXDx06pIcffjj8c01NjWpra7Vjxw5lZ2fLMAwlJCRIkmbMmKGbb745srQAAABRJKKi1b17d23YsCH8c2FhoQKBQPjnZcuWKTMzM5JDAAAARC3Tpg59Pp9KSko0duxYs4YEAACIahFd0fqmd955R926ddO1114b3jZjxgyFQiENHDhQjz/+uDp16tSiMVNTO5oVDzEuLS3J7giIIpwvcALO87bBtKK1bt26JlezVq1aJa/XK5/Pp8LCQi1YsEDFxcUtGrOiolbBYMisiK2CE9sex4/X2B0BUSItLYnzBTGP87x1ud2uC14cMmXq8OjRo9q5c6dGjRoV3ub1eiVJhmEoNzdXH3/8sRmHAgAAiBqmFK3169dr2LBhSklJkSTV1dWppuZskw6FQtq8ebOysrLMOBQAAEDUMGXqcP369Zo3b17454qKCk2bNk2BQEDBYFC9e/dWfn6+GYcCAACIGqYUrdLS0iY/Z2Rk6I033jBjaAAAgKjFyvAAAAAWoWgBAABYhKIFAABgEYoWAACARShaAAAAFqFoAQAAWISiBQAAYBGKFgAAgEUoWgAAABahaAEAAFiEogUAAGARihYAAIBFKFoAAAAWoWgBAABYhKIFAABgEYoWAACARShaAAAAFqFoAQAAWMRjd4BYE2z0qde8dXbHcJRgo8/uCAAAnBdFy2TueEOjpm+wO4ajlCwdI6nB7hgAAJyDqUMAAACLULQAAAAsQtECAACwCEULAADAIhQtAAAAi1C0AAAALELRAgAAsEjE62hlZ2fLMAwlJCRIkmbMmKGbb75Zn3zyiebPn6+Ghgalp6dryZIlSk1NjTgwAABAtDBlwdJly5YpMzMz/HMwGNTMmTO1aNEiDRo0SM8++6yKi4u1aNEiMw4HAAAQFSyZOty1a5cSEhI0aNAgSdKECRP01ltvWXEoAACANsuUK1ozZsxQKBTSwIED9fjjj6u8vFxXXnlleH+XLl0UDAZVVVWl5ORkMw4JAADQ5kVctFatWiWv1yufz6fCwkItWLBAt912mxnZlJra0ZRxEPvS0pLsjoAowvkCJ+A8bxsiLlper1eSZBiGcnNzNXXqVN133306cuRI+DMnT56U2+1u8dWsiopaBYOhSCO2Kk5sexw/XmN3BESJtLQkzhfEPM7z1uV2uy54cSiie7Tq6upUU3P2H2QoFNLmzZuVlZWl6667TvX19froo48kSatXr9aIESMiORQAAEDUieiKVkVFhaZNm6ZAIKBgMKjevXsrPz9fbrdbixcvVn5+fpPlHQAAAJwkoqKVkZGhN95447z7rr/+epWUlEQyPAAAQFRjZXgAAACLULQAAAAsQtECAACwCEULAADAIhQtAAAAi1C0AAAALELRAgAAsAhFCwAAwCIULQAAAItQtAAAACxC0QIAALAIRQsAAMAiFC0AAACLULQAAAAsQtECAACwCEULAADAIhQtAAAAi1C0AAAALELRAgAAsAhFCwAAwCIULQAAAItQtAAAACxC0QIAALAIRQsAAMAiFC0AAACLULQAAAAsQtECAACwCEULAADAIp5IvlxZWalZs2bp4MGDMgxDPXr00IIFC9SlSxf16dNHmZmZcrvPdrnFixerT58+poQGAACIBhEVLZfLpcmTJ2vIkCGSpKKiIhUXF+vJJ5+UJK1evVodOnSIPCUAAEAUimjqMDk5OVyyJKl///46cuRIxKEAAABiQURXtL4pGAzq1VdfVXZ2dnjbvffeq0AgoFtuuUXTpk2TYRhmHQ4AAKDNM61oLVy4UImJibrnnnskSe+99568Xq9qa2s1c+ZMLV++XI899liLxkxN7WhWPMS4tLQkuyMginC+wAk4z9sGU4pWUVGRvvzyS61YsSJ887vX65UkdezYUePGjdOLL77Y4nErKmoVDIbMiNhqOLHtcfx4jd0RECXS0pI4XxDzOM9bl9vtuuDFoYiXd3jqqae0a9cuLV++PDw1WF1drfr6ekmS3+9XaWmpsrKyIj0UAABAVInoitbnn3+u3/zmN+rZs6cmTJggSerevbsmT56s+fPny+Vyye/3a8CAAXr00UdNCQwAABAtIipa11xzjT799NPz7ispKYlkaAAAgKjHyvAAAAAWoWgBAABYhKIFAABgEYoWAACARShaAAAAFqFoAQAAWISiBQAAYBGKFgAAgEVMe6k0AOdI6dxeHiN6//qIxneS+n1+VVafsTsGgBaK3r8pAdjGY3j0efF2u2M4yjUzbrI7AoDLwNQhAACARShaAAAAFqFoAQAAWISiBQAAYBFuhgcA4Dw6d24nw4i3O8Zli8ana32+RlVX19sdw1QULQAAzsMw4rV06VK7YzjK9OnTJcVW0WLqEAAAwCIULQAAAItQtAAAACxC0QIAALAIN8MDaLFgY4BXwrSyYGPA7giO42/0//3mbLQWf6Pf7gimo2gBaDF3fJw+GDPW7hiOMnTDOrsjOI4nnnd6trZY/B84pg4BAAAsQtECAACwCEULAADAIhQtAAAAi3AzPIAWCzT4uDm7lQUafHZHAHAZKFoAWiwuwdCPXptqdwxHWTP+OUkNdsdwFJYxaX2xuIyJpUVr//79mjNnjqqqqpScnKyioiL17NnTykMCAGAKljFpfbF4pdzSe7Ty8/OVm5ur0tJS5ebmav78+VYeDgAAoE2x7IpWRUWFdu/erRdffFGSNHLkSC1cuFAnT55Uly5dmjWG2+2yKp6lrkhpb3cEx4nWcyWapSU2799jmIfzvPUlXJFmdwTHicbz/GKZLSta5eXl6tatm+Li4iRJcXFxuuKKK1ReXt7sopWS0sGqeJZamXe73REcJzW1o90RHGf5qEK7IzgO53nrG/TbFXZHcJxYO89Z3gEAAMAilhUtr9ero0ePKhA4+wRBIBDQsWPH5PV6rTokAABAm2JZ0UpNTVVWVpY2btwoSdq4caOysrKaPW0IAAAQ7VyhUChk1eD//d//rTlz5ujUqVPq1KmTioqK1KtXL6sOBwAA0KZYWrQAAACcjJvhAQAALELRAgAAsAhFCwAAwCIULQAAAItQtAAAACxC0QLgGBUVFfrkk0/sjgHAQShaDrZ//341NDRIkrZt26bnn39e1dXVNqcCzJWbm6uamhqdOnVKOTk5mjdvnoqKiuyOBZhq8+bNqq2tlSQ988wzevDBB7Vr1y6bU0GiaDnaz372M7ndbpWVlSk/P19lZWWaPXu23bEAU9XV1SkpKUnvvvuuRo0apZKSEm3fvt3uWICpnnvuOXXs2FF/+9vftH37duXk5KigoMDuWBBFy9Hcbrfi4+O1detWTZw4UQsXLlR5ebndsQBT+Xw+SdKHH36ooUOHyu12Ky4uzuZUgLk8Ho8k6YMPPtC4ceM0atSo8IwF7EXRcrCGhgadOHFC7777rm644QZJEi8KQKwZPHiw/vEf/1F/+ctfNHjwYJ06dUpuN3/1Iba4XC5t3rxZmzdv1o033ihJamxstDkVJMljdwDY5/7779eIESN04403ql+/fiorK1NSUpLdsQBT5efna+/evcrIyFB8fLxqa2uZUkHMycvL0+9+9zvdddddysjI0IEDBzRkyBC7Y0G86xDfEAwG5ff7ZRiG3VEA0+zfv19XXnmlEhIStG3bNu3Zs0fjx49X586d7Y4GwAG4fu5g33xK5emnn9ZDDz2kzz//3OZUgLl46ANO8Ktf/Uo1NTXy+/3Kzc1V//79tWHDBrtjQRQtR/vmUyoffPCBcnJytHDhQrtjAabioQ84wX/+538qKSlJ27dvV7du3VRaWqoXXnjB7lgQRcvReEoFTsBDH3CSnTt36rbbblO3bt3kcrnsjgNRtByNp1TgBF8/9JGYmMhDH4hZqampys/P1x//+EcNHTpUfr9fgUDA7lgQN8M72l//+lf99re/1ZAhQ3T//ffrwIEDevnll/XEE0/YHQ2wDA99IBadPHlSb775pvr376/+/fvr0KFD2rFjh+688067ozkeRQtAzNu3b5/27t0bXrxUknJycmxMBMApWEfLwfx+v9atW6c9e/Y0uTdr0aJFNqYCzPX73/9er732mo4fP65+/frpo48+0ne/+12KFmJKeXm5lixZor179zb5+/xPf/qTjakgcY+Wo82fP18ff/yx3nvvPfXs2VO7du1Su3bt7I4FmGrNmjV6/fXX5fV6tXLlSr3++uvq0KGD3bEAU82dO1c33nijQqGQiouLNXDgQN1xxx12x4IoWo72X//1XyoqKlJSUpKmTJmiV155RV988YXdsQBTGYahxMREBYNBhUIhZWZm6sCBA3bHAkxVWVmpcePGyePxaMCAAfrVr36lrVu32h0LYurQ0RISEiRJcXFxOnPmjJKSklRRUWFzKsBc7du3V2Njo/r27aslS5bI6/UqGAzaHQswVXx8vCQpMTFRR44cUdeuXXXy5EmbU0GiaDla586dVV1drZtvvlkPPfSQUlJS1K1bN7tjAabKz89XY2Oj5syZo6eeekqHDh3S4sWL7Y4FmGrQoEGqqqrSxIkTdeedd8owDI0YMcLuWBBPHTpaIBBQXFycgsGg3nzzTdXW1ionJ0cdO3a0OxoA4L6rONwAAAatSURBVDIdOXJEtbW1yszMtDsKRNECEKMuddVq1qxZrZQEsM6ZM2cuur99+/atlAQXwtShA40dO/air2ZYu3ZtK6YBrJGYmGh3BMByAwYMkMvlavJaqa9/drlc2rNnj43pIHFFy5F27Nhx0f2DBw9upSQAAMQ2ihaAmFZQUKBHHnlEycnJks4+Bv/ss89q3rx5NicDzPPZZ5+pe/fu4Su5dXV1Onz4sK655hqbk4F1tBxs4sSJqq6uDv9cVVWlu+++28ZEgPk++uijcMmSpJSUFO3cudPGRID55syZE17iQZI8Ho9mz55tYyJ8jaLlYHV1dercuXP45+TkZJ0+fdrGRID5AoHAOdv8fr8NSQDrBAKBJkXLMIzznvtofRQtBwsGg02eWDl9+jT/AULM6devnwoKCnT06FF99dVXKigoUL9+/eyOBZjK4/GorKws/PPBgwcVFxdnYyJ8jacOHWzkyJGaNGmSJk6cKEl69dVXNXr0aJtTAeaaO3euCgsLlZOTI5fLpVtvvVVz5861OxZgqkceeUQTJ07UsGHDJElbt25VQUGBzakgcTO8461fv17vvfeeJOn73/++cnJy7A0EALgsBw4c0AcffCBJuummm9SjRw+bE0GiaAEAAFiGe7QAAAAsQtECAACwCEULAADAIjx16GCPPvqonnnmmUtuA6LRDTfccNF3ev75z39uxTSANVatWnXR/SxCbT+KloMdPHjwnG379u2zIQlgvnXr1kk6+5L0qqoqjR8/XqFQSGvXrm2yUC8QzXbt2iXp7KulduzYoRtvvFHS2f+RGDJkCEWrDeCpQwdas2aNXnvtNe3bt0+9e/cOb6+pqdFVV12lFStW2JgOMNedd96pP/zhD022jR07NlzEgFjwz//8z3riiSeUkZEhSSorK1NhYSF/n7cBXNFyoKFDh6pHjx5auHChZs2aFd7esWNH9enTx8ZkgPlqa2t18uRJdenSRZJ08uRJ1dbW2pwKMNeRI0fCJUuSMjIydOjQIRsT4WsULQdKT09Xenq6Nm7cGN7m8/lUXV3NKxsQc+6//37l5OTo1ltvlXR2xewpU6bYGwowWdeuXbV8+XKNGzdO0tmp865du9qcChJTh4722GOPacGCBYqPj9eYMWNUWVmpKVOm6MEHH7Q7GmCqvXv3aufOnZKkwYMHc+UWMefo0aMqLCzUhx9+KOnswyBz585Vt27dbE4Grmg52P79+5WUlKS33npLQ4YM0c9//nP96Ec/omgh5vTt21d9+/a1OwZgiUAgoNdee03Lli2zOwrOg6LlYH6/X5K0c+dODRs2TO3bt5fbzdJqiC0ff/yxlixZorKyMgUCAYVCIblcLpZ3QMyIi4vT+++/r5/+9Kd2R8F5ULQcrHfv3po8ebL27dun6dOnq76+3u5IgOnmzZunn/zkJ+rfvz//I4GYdeutt2rlypXKyclRYmJieHv79u1tTAWJe7Qcrb6+Xtu3b1efPn2UkZGho0eP6tNPP9Utt9xidzTANHfccYfWr19vdwzAUt+cGne5XOErt3v27LExFSSKFoAY9+tf/1rXX3+9hg0bZncUAA5E0UITo0aNUklJid0xANPccMMNqqqqUocOHWQYBvdoIeZVV1fz9oM2hHu0HOiLL7644L7KyspWTAJYjxXgEcv27t2ruXPnyu12q6ioSEVFRfrwww+VnJysFStWKCsry+6IjscVLQfq27ev0tPTdb5/9MeOHQu/OwsA0Lbdc889mjRpkmpqavTMM8/oscce0+jRo/XOO+/o97//vV566SW7IzoeV7QcKD09Xa+88sp5F7LjPhbEisrKShUXF6u8vFzDhw9v8nLdadOm6V/+5V9sTAeY4/Tp0xo+fLgk6ZlnntHo0aMlSdnZ2ayr1UbwrLMD3X777Tp8+PB59912222tnAawRn5+vjp37qwJEybo7bff1iOPPBJeO66srMzmdIA5vjkzMXTo0Cb7gsFga8fBeVC0HGj27Nm6/vrrz7svLy+vldMA1jhw4IBmzZql22+/XS+88ILS0tI0ZcoUNTQ02B0NME16enr4JekFBQXh7V999RVraLUR3KMFICb98Ic/1B//+Mcm24qKirR7924dO3bsnH1ALKmrq9OZM2eUmppqdxTH44oWgJiUkZERfpH012bPnq3vfOc7OnDggD2hgFaSmJhIyWojuKIFICZVVVXJ5XKddz2hL774QldffbUNqQA4DUULAADAIkwdAgAAWISiBQAAYBGKFgAAgEUoWgAAABahaAEAAFjk/wPmHUDSWo18igAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "SW-60Hcn42hp" }, "source": [ "একটা জিনিস লক্ষ্য করেছেন? ১ম শ্রেণীতে A, B, এবং C আছে। কিন্তু বাকি ক্লাসে A,B, এবং C কিন্তু নেই। তাহলে একটা ম্যাপিং করি সমান স্কেলিং দিয়ে। একই দূরত্বে। 0.4 দিয়ে প্রতিটার দূরত্ব। কেবিন ধরে। সেটার ভাড়াগুলো ভর্তি করি শ্রেণীর গড় ভাড়া দিয়ে। " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "QdWpwnUA42hp" }, "source": [ "cabin_mapping = {\"A\": 0, \"B\": 0.4, \"C\": 0.8, \"D\": 1.2, \"E\": 1.6, \"F\": 2, \"G\": 2.4, \"T\": 2.8}\n", "for dataset in train_test_data:\n", " dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)" ], "execution_count": 54, "outputs": [] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "VM3JzdjF42hp" }, "source": [ "# fill missing Fare with median fare for each Pclass\n", "train[\"Cabin\"].fillna(train.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)\n", "test[\"Cabin\"].fillna(test.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)" ], "execution_count": 55, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "BeneT-sV42hp" }, "source": [ "### ৫.৮ পরিবারের সদস্যসংখ্যা \n", "এটা নিয়ে বিশাল একটা বড় লেখা আছে 'আর' এনভায়রনমেন্টএ। সেটা দেখুন - কনসেপ্ট একই। " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "0y4DEGyV42hq" }, "source": [ "train[\"FamilySize\"] = train[\"SibSp\"] + train[\"Parch\"] + 1\n", "test[\"FamilySize\"] = test[\"SibSp\"] + test[\"Parch\"] + 1" ], "execution_count": 56, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lEGHVNm942hq", "outputId": "912da721-30c5-4a2d-94c7-1797b8a43674", "colab": { "base_uri": "https://localhost:8080/", "height": 220 } }, "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'FamilySize',shade= True)\n", "facet.set(xlim=(0, train['FamilySize'].max()))\n", "facet.add_legend()\n", "plt.xlim(0)" ], "execution_count": 57, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(0.0, 11.0)" ] }, "metadata": { "tags": [] }, "execution_count": 57 }, { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "nBT-y0Cm42hq" }, "source": [ "আবারো পরিবারের ম্যাপিং। দেখুন 'আর' এনভায়রনমেন্ট। *ওপরের ছবি বলছে যারা একা ভ্রমণ করছিলেন তারা মারা গিয়েছেন বেশি। এখানে \"০\" মানে হচ্ছে উনি একা ছিলেন এই টাইটানিক জাহাজে।*" ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "mkerCyG542hq" }, "source": [ "family_mapping = {1: 0, 2: 0.4, 3: 0.8, 4: 1.2, 5: 1.6, 6: 2, 7: 2.4, 8: 2.8, 9: 3.2, 10: 3.6, 11: 4}\n", "for dataset in train_test_data:\n", " dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)" ], "execution_count": 58, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "JYjzLBPB42hq", "outputId": "a4194e3a-5bf5-4d75-962e-0c1f53dcaea0", "colab": { "base_uri": "https://localhost:8080/", "height": 221 } }, "source": [ "train.head()" ], "execution_count": 59, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitleFamilySize
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" ], "text/plain": [ " PassengerId Survived Pclass Sex ... Cabin Embarked Title FamilySize\n", "0 1 0 3 0 ... 2.0 0 0 0.4\n", "1 2 1 1 1 ... 0.8 1 2 0.4\n", "2 3 1 3 1 ... 2.0 0 1 0.0\n", "3 4 1 1 1 ... 0.8 0 2 0.4\n", "4 5 0 3 0 ... 2.0 0 0 0.0\n", "\n", "[5 rows x 13 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 59 } ] }, { "cell_type": "code", "metadata": { "id": "_5ZPg7f-42hq", "outputId": "cfd7971b-234a-497e-a5bf-626c4c4ed7e0", "colab": { "base_uri": "https://localhost:8080/", "height": 221 } }, "source": [ "train.head()" ], "execution_count": 60, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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" ], "text/plain": [ " PassengerId Survived Pclass Sex ... Cabin Embarked Title FamilySize\n", "0 1 0 3 0 ... 2.0 0 0 0.4\n", "1 2 1 1 1 ... 0.8 1 2 0.4\n", "2 3 1 3 1 ... 2.0 0 1 0.0\n", "3 4 1 1 1 ... 0.8 0 2 0.4\n", "4 5 0 3 0 ... 2.0 0 0 0.0\n", "\n", "[5 rows x 13 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 60 } ] }, { "cell_type": "markdown", "metadata": { "id": "WASoOC_142hr" }, "source": [ "অদরকারি ভ্যারিয়েবলগুলো ফেলে দিন। কারণ 'Ticket', 'SibSp', 'Parch' থেকে ফিচার ইঞ্জিনিয়ারিং করে বের করে নিয়েছি নতুন ফিচার। " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "hccviqXg42hr" }, "source": [ "features_drop = ['Ticket', 'SibSp', 'Parch']\n", "train = train.drop(features_drop, axis=1)\n", "test = test.drop(features_drop, axis=1)\n", "train = train.drop(['PassengerId'], axis=1)" ], "execution_count": 61, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "N9GLlaDC42hr", "outputId": "e050c29d-4b05-4edb-c236-52ac65d2cce5", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "train_data = train.drop('Survived', axis=1)\n", "target = train['Survived']\n", "\n", "train_data.shape, target.shape" ], "execution_count": 62, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "((891, 8), (891,))" ] }, "metadata": { "tags": [] }, "execution_count": 62 } ] }, { "cell_type": "markdown", "metadata": { "id": "nByV81Eq42hr" }, "source": [ "### দেখুন সব ফিচার সংখ্যায় \n", "এটা করার কারণ হচ্ছে আমাদের সামনে মডেল তৈরির সময়ে সবগুলো ভ্যারিয়েবলকে নতুন করে বলতে হবে না, যেটা করেছিলাম 'আর' এনভায়রনমেন্টে। " ] }, { "cell_type": "code", "metadata": { "id": "eyXtjLrp42hr", "outputId": "c66e7d94-b51f-4776-93e7-a1cf6184580c", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "train_data.head()" ], "execution_count": 63, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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" ], "text/plain": [ " Pclass Sex Age Fare Cabin Embarked Title FamilySize\n", "0 3 0 1.0 0.0 2.0 0 0 0.4\n", "1 1 1 3.0 2.0 0.8 1 2 0.4\n", "2 3 1 1.0 0.0 2.0 0 1 0.0\n", "3 1 1 2.0 2.0 0.8 0 2 0.4\n", "4 3 0 2.0 0.0 2.0 0 0 0.0" ] }, "metadata": { "tags": [] }, "execution_count": 63 } ] }, { "cell_type": "markdown", "metadata": { "id": "RfwZijbr42hr" }, "source": [ "## ৬. মেশিন লার্নিং মডেলিং " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "zLf383Ee42hs" }, "source": [ "# Importing Classifier Modules\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "import numpy as np" ], "execution_count": 64, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xfMCVAAP42hs" }, "source": [ "সবকিছু ঠিক আছে! কোন ডাটা মিসিং নেই। " ] }, { "cell_type": "code", "metadata": { "id": "dCBCb_AX42hs", "outputId": "574f537b-ccbb-41ff-e468-098f0aad0535", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "train.info()" ], "execution_count": 65, "outputs": [ { "output_type": "stream", "text": [ "\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Survived 891 non-null int64 \n", " 1 Pclass 891 non-null int64 \n", " 2 Sex 891 non-null int64 \n", " 3 Age 891 non-null float64\n", " 4 Fare 891 non-null float64\n", " 5 Cabin 891 non-null float64\n", " 6 Embarked 891 non-null int64 \n", " 7 Title 891 non-null int64 \n", " 8 FamilySize 891 non-null float64\n", "dtypes: float64(4), int64(5)\n", "memory usage: 62.8 KB\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "AQBmrviA42hs" }, "source": [ "### ৬.১ ক্রস ভ্যালিডেশন (কে-ফোল্ড = ১০ ভাগ)\n", "আমরা চলে এসেছি প্রায় শেষের দিকে। শেষ করার আগে একটা জিনিস সবসময় চাইবো - বিশেষ করে নিজের মডেলের 'স্ট্যাবিলিটি' যাতে ভালো থাকে। এখন আমরা কাজ করছি ট্রেনিং ডাটা দিয়ে, কিন্তু যদি অন্য নতুন ডাটা (যেটা মডেল দেখেনি) দিয়ে মডেলটা খারাপ করে? মানে যে ডাটা সে দেখেনি - ট্রেনিং সেশনে। আর সেকারণে আমরা ডাটাকে দশভাগে ভাগ করে একেক সময় একেক ভাগকে দেখাবো না (মানে, লুকিয়ে রাখবো) মডেলকে। নিজের ডাটার মধ্যে চেক করা, এটা একটা মজার জিনিস। নিজের ডাটাকে ঘুরিয়ে ফিরিয়ে মডেলের ভেতরের 'অ্যাক্যুরেসি' দেখার জন্য এটা একটা চমৎকার জিনিস। চলুন, আগে বের করি cross_val_score, এটা টেস্ট 'ফোল্ডে'র স্কোরটা বের করে আনে। cross_val_score কিন্তু ট্রেনিং এবং টেস্ট দুটোতেই প্রতিটা 'ফোল্ড' ব্যবহার করে।\n", "\n", "'n_splits=10' মানে এখানে ডাটাসেটকে ১০ ভাগে ভাগ করা হয়েছে। " ] }, { "cell_type": "markdown", "metadata": { "id": "HlH6NAzp42hs" }, "source": [ "ক্রস ভ্যালিডেশন: ছবি দেখলে কেমন হয়?\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "jWWl-7Na42hs" }, "source": [ "ছবি: ক্রস ভ্যালিডেশন, কিভাবে নিজের ডাটা দিয়ে 'অ্যাক্যুরেসি' জানা যায় " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "0WKjcX9v42hs" }, "source": [ "from sklearn.model_selection import KFold\n", "from sklearn.model_selection import cross_val_score\n", "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)" ], "execution_count": 66, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "b1yookFy42hs" }, "source": [ "### ৬.২ ডিসিশন ট্রি\n", "আগের 'আর' এর এক্সারসাইজ দেখি। সেখানে 'ডিসিশন ট্রি' নিয়ে অনেক কথা হয়েছে। এখানে ক্লাসিফায়ারের 'clf' এর 'অ্যাক্যুরেসি' বের করার চেষ্টা করেছি আমরা। " ] }, { "cell_type": "code", "metadata": { "id": "e2aLqD2c42ht", "outputId": "9c8ecb5b-69e0-4077-e344-ba1ed6004fd2", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "clf = DecisionTreeClassifier()\n", "scoring = 'accuracy'\n", "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n", "print(score)" ], "execution_count": 67, "outputs": [ { "output_type": "stream", "text": [ "[0.76666667 0.80898876 0.7752809 0.76404494 0.88764045 0.76404494\n", " 0.82022472 0.82022472 0.74157303 0.78651685]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "amg5TOXq42ht", "outputId": "aeca9364-dba9-4bb0-860f-af46ba5253e5", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "# decision tree Score\n", "round(np.mean(score)*100, 2)" ], "execution_count": 68, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "79.35" ] }, "metadata": { "tags": [] }, "execution_count": 68 } ] }, { "cell_type": "markdown", "metadata": { "id": "CgqhEFUk42ht" }, "source": [ "### ৬.৩ র‌্যান্ডম ফরেস্ট" ] }, { "cell_type": "code", "metadata": { "id": "2ORb1sLa42ht", "outputId": "c30d8adc-666f-48b2-b098-3d221be751b3", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "clf = RandomForestClassifier(n_estimators=13)\n", "scoring = 'accuracy'\n", "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n", "print(score)" ], "execution_count": 69, "outputs": [ { "output_type": "stream", "text": [ "[0.81111111 0.85393258 0.83146067 0.83146067 0.85393258 0.80898876\n", " 0.83146067 0.80898876 0.74157303 0.79775281]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "X9BNm7Z742ht", "outputId": "32487c68-984f-467e-ef25-a74be1f8a31a", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "# Random Forest Score\n", "round(np.mean(score)*100, 2)" ], "execution_count": 70, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "81.71" ] }, "metadata": { "tags": [] }, "execution_count": 70 } ] }, { "cell_type": "markdown", "metadata": { "id": "KZEJdDef42hu" }, "source": [ "## ৭. ক্যাগলে আপলোড \n", "\n", "প্রচুর ক্যাগলে আপলোড করেছেন আগের 'আর' এনভায়রনমেন্টে। এবার দেখবেন কী? এখানে আমরা একটা 'submission.csv' তৈরি করবো ক্যাগলে আপলোড করার জন্য। " ] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "hBsGnbPU42hu" }, "source": [ "clf = RandomForestClassifier(n_estimators=13)\n", "clf.fit(train_data, target)\n", "\n", "test_data = test.drop(\"PassengerId\", axis=1).copy()\n", "prediction = clf.predict(test_data)" ], "execution_count": 71, "outputs": [] }, { "cell_type": "code", "metadata": { "collapsed": true, "id": "rKrhA3BI42hu" }, "source": [ "submission = pd.DataFrame({\n", " \"PassengerId\": test[\"PassengerId\"],\n", " \"Survived\": prediction\n", " })\n", "\n", "submission.to_csv('submission.csv', index=False)" ], "execution_count": 72, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "TA5Eqdi_42hu" }, "source": [ "### সাবমিশন ফাইল তৈরি করে ভেতরে দেখা " ] }, { "cell_type": "code", "metadata": { "id": "HH6C5VTC42hu", "outputId": "a3ffe6e1-9947-4394-d87d-41cc068cc39a", "colab": { "base_uri": "https://localhost:8080/", "height": 0 } }, "source": [ "submission = pd.read_csv('submission.csv')\n", "submission.head()" ], "execution_count": 73, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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" ], "text/plain": [ " PassengerId Survived\n", "0 892 0\n", "1 893 0\n", "2 894 0\n", "3 895 0\n", "4 896 1" ] }, "metadata": { "tags": [] }, "execution_count": 73 } ] }, { "cell_type": "markdown", "metadata": { "id": "rtyJWDiG42hu" }, "source": [ "## কৃতজ্ঞতা এবং অন্যান্য ব্যবহৃত নোটবুক \n", "\n", "এই নোটবুক তৈরি করা হয়েছে এই নোটবুকগুলোর ইনপুট নিয়ে, মিনসুকের ধারণাটা রেখেছি ইচ্ছে করে:\n", "\n", "- [Mukesh ChapagainTitanic Solution: A Beginner's Guide](https://www.kaggle.com/chapagain/titanic-solution-a-beginner-s-guide?scriptVersionId=1473689)\n", "- [How to score 0.8134 in Titanic Kaggle Challenge](http://ahmedbesbes.com/how-to-score-08134-in-titanic-kaggle-challenge.html)\n", "- [Titanic: factors to survive](https://olegleyz.github.io/titanic_factors.html)\n", "- [Titanic Survivors Dataset and Data Wrangling](http://www.codeastar.com/data-wrangling/)\n", "- [Minsuk-Heo cross validation, submit file generation](https://github.com/minsuk-heo/kaggle-titanic)\n", "- [Demonstrates basic data munging, analysis, and visualization techniques. supervised machine learning techniques](http://agconti.github.io/kaggle-titanic)" ] } ] }