Using-ChatGPT-in-Data Science.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4f69ff15",
   "metadata": {},
   "source": [
    "# How to Use ChatGPT in Data Science?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0374efd5",
   "metadata": {},
   "source": [
    "Prompt:\n",
    "\n",
    "`Write code with Seaborn to show the sex distribution of flipper lengths in the penguins dataset.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfcac0d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='flipper_length_mm', ylabel='Count'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# penguins veri setini yükleyin\n",
    "df = sns.load_dataset(\"penguins\")\n",
    "\n",
    "# Flipper uzunluklarının cinsiyete göre dağılımını gösteren histogram oluşturun\n",
    "sns.histplot(data=df, x=\"flipper_length_mm\", hue=\"sex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c5b6c8",
   "metadata": {},
   "source": [
    "Prompt:\n",
    "\n",
    "`Write code with Seaborn to show the bow plot of sex distribution of flipper lengths in the penguins dataset.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9e1e41d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Penguins veri setini yükle\n",
    "df = sns.load_dataset(\"penguins\")\n",
    "\n",
    "# Flipper uzunluğu ve cinsiyet sütunlarını seç\n",
    "flipper_lengths = df[\"flipper_length_mm\"]\n",
    "sex = df[\"sex\"]\n",
    "\n",
    "# Kutu grafiğini oluştur\n",
    "sns.boxplot(x=sex, y=flipper_lengths)\n",
    "\n",
    "# Grafiği göster\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f89f357",
   "metadata": {},
   "source": [
    "Prompt:\n",
    "\n",
    "`Write code to build a random forest model using boston dataset.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdd9bdee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "    \n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test skoru: 0.8922527442109116\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Boston veri setini yükle\n",
    "X, y = load_boston(return_X_y=True)\n",
    "\n",
    "# Verileri eğitim ve test kümelerine ayır\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Random forest regresyon modelini oluştur\n",
    "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "\n",
    "# Modeli eğit\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Modeli test verileri ile değerlendir\n",
    "score = model.score(X_test, y_test)\n",
    "print(\"Test skoru:\", score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc79e868",
   "metadata": {},
   "source": [
    "Prompt:\n",
    "\n",
    "`Write code Scrape with Python and BeautifulSoup to scrape data in \"https://books.toscrape.com/\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f1df9ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title: A Light in the Attic\n",
      "Price: Â£51.77\n",
      "Availability: In stock\n",
      "\n",
      "Title: Tipping the Velvet\n",
      "Price: Â£53.74\n",
      "Availability: In stock\n",
      "\n",
      "Title: Soumission\n",
      "Price: Â£50.10\n",
      "Availability: In stock\n",
      "\n",
      "Title: Sharp Objects\n",
      "Price: Â£47.82\n",
      "Availability: In stock\n",
      "\n",
      "Title: Sapiens: A Brief History of Humankind\n",
      "Price: Â£54.23\n",
      "Availability: In stock\n",
      "\n",
      "Title: The Requiem Red\n",
      "Price: Â£22.65\n",
      "Availability: In stock\n",
      "\n",
      "Title: The Dirty Little Secrets of Getting Your Dream Job\n",
      "Price: Â£33.34\n",
      "Availability: In stock\n",
      "\n",
      "Title: The Coming Woman: A Novel Based on the Life of the Infamous Feminist, Victoria Woodhull\n",
      "Price: Â£17.93\n",
      "Availability: In stock\n",
      "\n",
      "Title: The Boys in the Boat: Nine Americans and Their Epic Quest for Gold at the 1936 Berlin Olympics\n",
      "Price: Â£22.60\n",
      "Availability: In stock\n",
      "\n",
      "Title: The Black Maria\n",
      "Price: Â£52.15\n",
      "Availability: In stock\n",
      "\n",
      "Title: Starving Hearts (Triangular Trade Trilogy, #1)\n",
      "Price: Â£13.99\n",
      "Availability: In stock\n",
      "\n",
      "Title: Shakespeare's Sonnets\n",
      "Price: Â£20.66\n",
      "Availability: In stock\n",
      "\n",
      "Title: Set Me Free\n",
      "Price: Â£17.46\n",
      "Availability: In stock\n",
      "\n",
      "Title: Scott Pilgrim's Precious Little Life (Scott Pilgrim #1)\n",
      "Price: Â£52.29\n",
      "Availability: In stock\n",
      "\n",
      "Title: Rip it Up and Start Again\n",
      "Price: Â£35.02\n",
      "Availability: In stock\n",
      "\n",
      "Title: Our Band Could Be Your Life: Scenes from the American Indie Underground, 1981-1991\n",
      "Price: Â£57.25\n",
      "Availability: In stock\n",
      "\n",
      "Title: Olio\n",
      "Price: Â£23.88\n",
      "Availability: In stock\n",
      "\n",
      "Title: Mesaerion: The Best Science Fiction Stories 1800-1849\n",
      "Price: Â£37.59\n",
      "Availability: In stock\n",
      "\n",
      "Title: Libertarianism for Beginners\n",
      "Price: Â£51.33\n",
      "Availability: In stock\n",
      "\n",
      "Title: It's Only the Himalayas\n",
      "Price: Â£45.17\n",
      "Availability: In stock\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "# İstek gönder ve sayfanın HTML'sini indir\n",
    "url = \"https://books.toscrape.com/\"\n",
    "page = requests.get(url)\n",
    "\n",
    "# HTML'yi parse et\n",
    "soup = BeautifulSoup(page.text, \"html.parser\")\n",
    "\n",
    "# Tüm kitapların listesi\n",
    "books = soup.find_all(\"article\")\n",
    "\n",
    "# Her kitap için döngü oluştur\n",
    "for book in books:\n",
    "    # Kitap bilgilerini topla\n",
    "    title = book.h3.a[\"title\"]\n",
    "    price = book.find(\"p\", class_=\"price_color\").text\n",
    "    availability = book.find(\"p\", class_=\"instock availability\").text.strip()\n",
    "\n",
    "    # Bilgileri ekrana yaz\n",
    "    print(\"Title:\", title)\n",
    "    print(\"Price:\", price)\n",
    "    print(\"Availability:\", availability)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e74ec966",
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   "source": [
    "Thanks for reading 😀\n",
    "\n",
    "Let's connect [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [Instagram](https://www.instagram.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) 😎"
   ]
  }
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