Gradient_Descent.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d274e89a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0cc5a931",
   "metadata": {},
   "outputs": [
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   "source": [
    "def gradient_descent(x,y):\n",
    "    m=c=0\n",
    "    \n",
    "    Learning_rate=0.001\n",
    "    iteration=120\n",
    "    n=len(x)\n",
    "    for i in range (iteration):\n",
    "        y_pred=m*x+c\n",
    "        \n",
    "        cost=(1/n)*sum([value**2 for value in (y-y_pred)])\n",
    "        pdm=-(2/n)*sum(x*(y-y_pred))\n",
    "        cdm=-(2/n)*sum(y-y_pred)\n",
    "        \n",
    "        m=m-Learning_rate *pdm\n",
    "        c=c-Learning_rate*cdm\n",
    "        \n",
    "        print(\"m value :\",m)\n",
    "        print(\"n value :\",n)\n",
    "        print(\"cost value :\",cost)\n",
    "        print(\"\\n\")\n",
    "    \n",
    "    \n",
    "X=np.array([1,3])\n",
    "Y=np.array([2,4])\n",
    "\n",
    "gradient_descent(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c707ff78",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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