Simple_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "54db8a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d2769da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = datasets.load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "47d3945d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "         0.01990842, -0.01764613],\n",
       "       [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
       "        -0.06832974, -0.09220405],\n",
       "       [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
       "         0.00286377, -0.02593034],\n",
       "       ...,\n",
       "       [ 0.04170844,  0.05068012, -0.01590626, ..., -0.01107952,\n",
       "        -0.04687948,  0.01549073],\n",
       "       [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
       "         0.04452837, -0.02593034],\n",
       "       [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "        -0.00421986,  0.00306441]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "26a39cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_X = np.array([[1], [2], [3]])\n",
    "diabetes_X_train  = diabetes_X\n",
    "diabetes_X_test = diabetes_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1e0b803e",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_y_train  = np.array([3, 2, 4])\n",
    "diabetes_y_test = np.array([3, 2, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "de3dd3f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "model.fit(diabetes_X_train, diabetes_y_train)\n",
    "diabetes_y_predicted = model.predict(diabetes_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "86cd8de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = linear_model.LinearRegression()\n",
    "# model.fit(diabetes_X_train, diabetes_Y_train)\n",
    "# diabetes_y_predicted = model.predict(diabetes_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "af77dd6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Mean squared error is:  \", mean_squared_error(diabetes_Y_test, diabetes_Y_predicted))\n",
    "# print(\"Weights:  \",  model.coef_)\n",
    "# print(\"Intercept:  \",  model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "94631d68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(diabetes_X_test, diabetes_y_test)\n",
    "plt.plot(diabetes_X_test, diabetes_y_predicted)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "582b2c97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b5605f2",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}