ChatGPT-for-Data-Science.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "79094326",
   "metadata": {},
   "source": [
    "# ChatGPT-with-Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "927b8d4c",
   "metadata": {},
   "source": [
    "`Draw a box plot showing the distribution of species' flipper length by sex in the Penguin Dataset with Seaborn. Show only python codes and don't write any explanation.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f837b854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='flipper_length_mm'>"
      ]
     },
     "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",
    "# Load the Penguin dataset from Seaborn\n",
    "penguins = sns.load_dataset(\"penguins\")\n",
    "\n",
    "# Draw the boxplot\n",
    "sns.boxplot(x=\"sex\", y=\"flipper_length_mm\", data=penguins)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e48a55",
   "metadata": {},
   "source": [
    "`Web scrape https://books.toscrape.com/ using Python and beautiful soup.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0170325",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A Light in the ... £51.77\n",
      "Tipping the Velvet £53.74\n",
      "Soumission £50.10\n",
      "Sharp Objects £47.82\n",
      "Sapiens: A Brief History ... £54.23\n",
      "The Requiem Red £22.65\n",
      "The Dirty Little Secrets ... £33.34\n",
      "The Coming Woman: A ... £17.93\n",
      "The Boys in the ... £22.60\n",
      "The Black Maria £52.15\n",
      "Starving Hearts (Triangular Trade ... £13.99\n",
      "Shakespeare's Sonnets £20.66\n",
      "Set Me Free £17.46\n",
      "Scott Pilgrim's Precious Little ... £52.29\n",
      "Rip it Up and ... £35.02\n",
      "Our Band Could Be ... £57.25\n",
      "Olio £23.88\n",
      "Mesaerion: The Best Science ... £37.59\n",
      "Libertarianism for Beginners £51.33\n",
      "It's Only the Himalayas £45.17\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "# Send a request to the website and retrieve the HTML\n",
    "url = 'https://books.toscrape.com/'\n",
    "html = requests.get(url).content\n",
    "\n",
    "# Parse the HTML using Beautiful Soup\n",
    "soup = BeautifulSoup(html, 'html.parser')\n",
    "\n",
    "# Find the elements on the page that you want to scrape\n",
    "book_containers = soup.find_all('article', class_='product_pod')\n",
    "\n",
    "# Extract the data from the elements\n",
    "for container in book_containers:\n",
    "    title = container.h3.a.text\n",
    "    price = container.find('p', class_='price_color').text\n",
    "    print(title, price)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963550f4",
   "metadata": {},
   "source": [
    "`Load the iris dataset with scikit-learn and then build a logistic regression model to predict iris flower species with Python.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff50c3fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 1.00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Load the iris dataset\n",
    "iris = load_iris()\n",
    "X = iris['data']\n",
    "y = iris['target']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Build the logistic regression model\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model on the test data\n",
    "accuracy = model.score(X_test, y_test)\n",
    "print(f'Test accuracy: {accuracy:.2f}')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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