python-scripts/scripts/Linear Regression/Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "05f41765",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "from pandas import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7f82f718",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pandas.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "656efe3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= DataFrame(data, columns = ['col_1'])\n",
    "y= DataFrame(data, columns = ['col_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6db0a71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x,y,alpha=.4)\n",
    "plt.xlim(0,4.5e8)\n",
    "plt.ylim(0,3e9)\n",
    "plt.xlabel(\"Feature\")\n",
    "plt.ylabel(\"Target\")\n",
    "plt.title(\"Feature v/s Target\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ec3fb414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = theta_0 + theta_1*X\n",
    "rgr = LinearRegression()\n",
    "rgr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "40be1d77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.11150918]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#theta_1 or slope\n",
    "th0= rgr.coef_ \n",
    "th0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e4c31635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>production_budget_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5029</th>\n",
       "      <td>225000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5030</th>\n",
       "      <td>215000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5031</th>\n",
       "      <td>306000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5032</th>\n",
       "      <td>200000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5033</th>\n",
       "      <td>425000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5034 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      production_budget_usd\n",
       "0                   1000000\n",
       "1                     10000\n",
       "2                    400000\n",
       "3                    750000\n",
       "4                     10000\n",
       "...                     ...\n",
       "5029              225000000\n",
       "5030              215000000\n",
       "5031              306000000\n",
       "5032              200000000\n",
       "5033              425000000\n",
       "\n",
       "[5034 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#theta_0 or c\n",
    "th1 = rgr.intercept_\n",
    "th1\n",
    "Y=rgr.predict(x)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d71ef51",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mRunning cells with 'Python 3.10.4 64-bit' requires ipykernel package.\n",
      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
      "\u001b[1;31mCommand: '\"c:/Program Files/Python310/python.exe\" -m pip install ipykernel -U --user --force-reinstall'"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x,y,alpha=.3)\n",
    "\n",
    "plt.scatter(x,Y) \n",
    "plt.plot(x['col_1'],Y,color='black')  #to convert dataframe to an array \n",
    "plt.xlim(0,450000000)\n",
    "plt.ylim(0,3000000000)\n",
    "plt.xlabel(\"Feature\")\n",
    "plt.ylabel(\"Target\")\n",
    "plt.title(\"Feature v/s Target\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
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