Day 14 of 75 DataSci.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dddd6a96",
   "metadata": {},
   "source": [
    "# Problem\n",
    "Predict the price of a house based on its size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25f0f30d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c52d85dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Sample data (Size in square feet, Price in dollars)\n",
    "sizes = np.array([1500, 2000, 2500, 3000, 3500]).reshape(-1, 1)\n",
    "prices = np.array([300000, 400000, 500000, 600000, 700000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9a4ef5b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(sizes, prices)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "55ca8eed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[560000.]\n"
     ]
    }
   ],
   "source": [
    "# Predict the price for a house of size 2800 sq ft\n",
    "#n=int(input(\"Enter the size :\"))\n",
    "size_to_predict = np.array([[2800]])\n",
    "predicted_price = model.predict(size_to_predict)\n",
    "print(predicted_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4afb45c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted price for a 2800 sq ft house: $560,000.00\n"
     ]
    }
   ],
   "source": [
    "print(f\"Predicted price for a 2800 sq ft house: ${predicted_price[0]:,.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d80d7c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data and the regression line\n",
    "plt.scatter(sizes, prices, color='black', label='Data Points')\n",
    "plt.plot(sizes, model.predict(sizes), color='blue', label='Regression Line')\n",
    "plt.xlabel('Size (sq ft)')\n",
    "plt.ylabel('Price ($)')\n",
    "plt.title('Linear Regression - House Prices')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a19aa0aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}